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When a great product hits the funding crunch

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Building a great product is not enough
Today I read a well-done article by The Verge on the shutdown of Everpix, a photo startup that’s gained a small but loyal following. It’s a great read, and I’d encourage you to check it out. There’s a lot of things to comment on, but the Everpix story is a common one these days- a lot of startups have built great initial products, and even shown some strong engagement, but ultimately not enough traction to gain a Series A.

The essay on Everpix drove home a lot of recent trends in startups that have gained momentum for the last year or two. Let’s examine a couple of these trends.

Funding goalposts continue to move
The first thing we’ll talk about is the company metrics. One of the best things about this article was that they did a good job of covering some of Everpix’s stats on engagement, conversion rate to premium, etc.

Everpix stats

  • 55,000 total signups and 6,800 paid users
  • Freemium biz model of $4.99/month or $49/year
  • Free-to-paid conversion rate of 13%
  • 4.5 star rating with 1,000+ reviews
  • MAU/signups of 60%
  • WAU/signups of 50%
  • Raised $1.8M and then a seed extension of $500k
  • Ex-Apple founders with 6 FTEs

You can see that other than the top-line metric of total signups, the other metrics are quite solid. If this company were started just a few years ago, I’m convinced they would have had no problem raising their Series A. These days though, it’s gotten a lot harder.

The reason for that is the “moving goalposts” on what you’re expected to do with your funding.

It’s been widely noted that investing milestones have evolved quickly over time:

  • In 1998, you’d raise $5M Series A with an idea and not much else. The capital would be spent to build the product, and hopefully you’d have some customers at the end of it, but it wasn’t required. You had to do crazy stuff like put machines into a datacenter, at this point. Then you’d raise a Series B to scale the marketing. The qualitative bar for the team, idea, and market was high.
  • In 2004, you’d raise $500k with just an idea. Then you’d build the product and spend $5M to market it. At this point, you could use a free Open Source stack which would accelerate development. You didn’t need to build a datacenter either.
  • In 2013, these days, you are expected to have a product coded up and ready before you raise your first substantial angel round. Maybe the product won’t be launched, but people will want to play with a demo at least. Then you raise $1-2M to get traction on your product. Then if you have millions of signups, then you get to raise your Series A of $5-10M.

In fact, it’s been famously written by Chris Dixon, now a partner at Andreessen Horowitz, that 10 million users is the new 1 million users. I’ve previously written that Mobile Startups are Failing Like It’s 1999, due to the long launch cycles that the Apple Store encourages. I’ve also written about mobile getting harder and not easier over time.

There’s a couple things going on: The sheer proliferation of seed-funded startups, combined with investors who want to invest post-traction, post-product/market fit. Combine this with 1999-style launches for mobile apps, and you have a big mismatch in the supply and demand for funding. Series A venture capitalists are often acting like growth investors now, where they want the entire equation de-risked before they put in much capital, and it’s reasonable to expect this given the technology stack and massive distribution channels.

My question is, in 2016, will the bar be even higher? Maybe angel investors will expect a working product, reasonable traction, and product/market fit all before they put in the first $1M? How much can market-risk be proved out before any professional money is raised?

Monetization won’t save you if it’s not combined with growth
The Everpix story also shows that having a business model isn’t enough- after all, a 12% conversion rate to premium is stellar, which you can compare to Evernote’s 6%, as they mention. The problem is, if you have monetization in place, investors also want to see a lot of growth. Or you need enough growth and scale to be profitable without outside funding.

Work backwards on the latter to see what that looks like:

  • 6 FTEs plus operations costs about $100k/month
  • At $5/month, you need 20k paid subscribers to break even
  • At a 12% free-to-paid rate, you need 160k signups

Turns out, 160k users is a lot, especially if you have a short runway. It’s well outside the boundary of a list of friends and family, or a Techcrunch article, or a big week of promotion from Apple . If you combine this with the rest of your schedule, like 6 months to raise VC, another 6-12 months to build the product, etc., then you don’t have much time to hit your traction milestones.

In contrast to the option to hit profitability, VCs don’t care that much about small scale monetization. They understand that a freemium service can get 1-5% conversion rates, and the question is if you have enough top-of-funnel signups to make the revenue numbers big. In fact, too much focus on monetization too early can lead a red flag, since it’ll mean maybe the entrepreneur is thinking small rather than focusing on winning the market.

A modern startup’s costs are all people costs
The final thing that’s worth pointing out in the article is the cost structure of the company and where the money went:

  • $565k consulting and legal fees
  • $128k office space
  • $360k operating costs
  • $1.4 total personnel costs

In other words, 80% of the costs went towards the employees and contractor/consultants/legal. It’s basically all people costs. You could argue that the office space is really just a function of the people too. Really, only ~15% of the capital went towards actually running the service.

If anything, this trend will only continue. San Francisco housing costs continue the rise, while computing infrastructure only gets cheaper and more flexible.

The nice thing about these costs, of course, is that you can always scale them down by scaling down your team. It’s complicated to do this, of course, since the value in this acquihires incent you to keep a large group of people going up until the end. But if you are convinced to work on the business for the long term, you can always scale things down to a few core folks, though it can be painful.

This is another reason why increasing your cost structure can be tricky if your product isn’t working in the market already. You end up in a case where just a year or two down the road, you have to make the tough decisions to keep going, or to shut the product down. So if you are working on something that you’re really passionate about – or as they say, amazing founder/market fit – then you may want to delay the team buildout so that you don’t end up creating that situation in the first place.

“Milestone awareness” and clear product roadmaps
Ultimately, this flavor of startup shutdown will continue to happen. Products that hit immense traction are the exception, not the norm, for a reason. Given that, what can you do? Ultimately, every founder needs a strong sense of “milestone awareness.” What I mean by that is the ability to understand what you need to accomplish before the next round of funding, and then to work backwards on that until you can put together a reasonable roadmap to get there. You might have to cut costs if the plan doesn’t seem to work. And you’ll have to revisit this plan on a regular basis to understand how it fits together.

The problem with hyper product-oriented entrepreneurs is that they often have one tool in their pocket: Making a great product. That’s both admirable, and dangerous. Once the initial product is working, the team has to quickly transition into marketing and user growth, which requires a different set of skills. It has to be more about metrics rather than product design: running experiments, optimizing signup flows, arbitraging LTVs and CACs, etc. It’s best when this is built on the firm foundation of user engagement that’s already been set up. In contrast, an entrepreneur that’s too product oriented will just continue polishing features or possibly introducing “big new ideas” that ultimately screw the product up. Or keep doing the same thing unaware of the milestone cliff in front of them. Scary.

Any startups that are at the “just add water stage” should email me and I’ll connect you with the resources and people to grow.

It’s funny that people take the lesson away from Apple that you should just focus on product. That’s only half the story, I think, because when you dig into why Apple is so secretive, it’s because the company is really focused on advertising and product launches. The secrecy that’s so deeply embedded in the organization facilitates their distribution strategy- can you imagine building your company culture around your marketing strategy? That’s what Apple’s done, though it’s not often talked about.

Good luck, guys
Finally, I want to wish the Everpix team good luck- they put together something that thousands of people enjoyed. That’s very hard, and more than most people can say. And they took away some very useful lessons that will only make them better entrepreneurs.

It’s never an easy thing to shut down something you’ve worked on for years, but I was insanely happy to see such a high-quality post mortem from The Verge. Thanks for writing this up, guys!

Written by Andrew Chen

November 5th, 2013 at 2:23 pm

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A clever way to buy Facebook ads based on what your users like (Guest post)

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My friend Gagan Biyani wrote up a great piece how to analyze what your Facebook audience is interested in, and using that to buy ads. He’s generously shared it, below. Gagan is CEO and co-founder at Sprig, and before that was at Lyft and started up Udemy. You can follow him on Twitter at @gaganbiyani and he has a new Medium account here. -Andrew

Gagan Biyani, Sprig:
Building Target Groups for Facebook Ads

Facebook advertising is tricky and there are multiple facets to it. By far the most important value of Facebook is being able to target based on demographic information. In this post, I’ll show you exactly how to use the “affinity ratio” to figure out what Facebook likes to target and dramatically increase the performance of your Facebook advertising.

As mentioned above, you have to have Facebook Connect on your app and you must grab the likes of your users. A sample of your users is OK so even if Facebook Connect is merely one option amongst many, that’s fine.

(Credit: This method was created by my co-founder at UdemyEren Bali. We tested dozens of other forms of targeting and nothing came close. I’m sure there are many other companies that have come up with this on their own.)

Note: this only works if you have Facebook like data from your user base

Step 1: Figure out what Facebook pages your users like

Here’s the trick: Download a CSV of all of the unique facebook pages your users like and the COUNT() of the number of users who like each page. Example:


You’ll notice these numbers don’t make a whole lot of sense. That’s because I made them all up!

Notice a few things. First, this list is sorted by greatest # of likes. That list is already a bit useful — and probably something you’ve looked at before. The problem is it doesn’t make this data useful enough.Everyone and their mom’s likes Michelle Obama, so you can’t target your advertising that way. From here, you have to figure out which one of these pages is actually useful to you.

Second, if you have any reasonable-sized user base, you’ll probably have 1000’s of results on the left column. That’s fine but we’ll use a small list for this example. You may have to have some sort of COUNT() limit to make this easier (aka only pages with over 1,000 likes make the cut).

Step 2: Add in the “Global Likes” of those Facebook Pages

Now you need to figure out how many globlal likes each of the pages on your list has. Use the Facebook API or do it manually if you don’t have access to dev resources.

In our B.S. data set above, I went ahead and did it manually. Here’s the data:


In case you’re wondering, I came up with this list by checking Facebook’s page recommendations. They were right on some things (who doesn’t like a little Dr. Oz in the mornings?) and wrong on others (phh, I don’t care about animals).It is probably starting to make sense now. Its not just about the total count of your users who like a given page, its actually about the relativecount.

Step 3: Create a ratio of [Count(users)]/[Global Likes]

From here, your goal is to create an “affinity score” (name created by Dinesh Thiru, who runs marketing at Udemy)


To make these numbers easier to read, I multipled the affinity score by 10,000. Depending on the number of users you have in Column Count(users), you may multiply by a smaller factor of 10.

Now, you have a relative score that allows you to compare different pages. I sorted this list by affinity score. Its interesting to see pages like “Michelle Obama” and “Food Network” to go from the top to the near-bottom of our list! Of course this is make-believe data, but when you have real data you’ll see similar results.

Step 4: Group your high affinity pages

Once you have a list of affinity scores, you need to group them into categories. This is important because otherwise, you wouldn’t have good Facebook targeting groups. Targeting users who like BothSidesoftheTable and the Golden State Warriors will make it hard to write ad copy and create cohesive campaigns.

1-AbAqc1TX5RrSKeZco-5WsgNatural groups will form when you start looking at your data.Two things matter when you are in the final stages of this:

  1. Sample size. The larger the size of your group, the more people you can target with your ads. You don’t want too large a size, though, because then you are paying crazy CPM’s and competing with a larger breadth of advertisers.
  2. Grouping. This is based entirely on your judgement. The natural groups are always ones where you think there’s a lot of overlap amongst those users. Its fairly obvious that people who like TechCrunch and BothSidesoftheTable overlap. In situations like the Monterey Bay Aquarium and In Defense of Animals group, its just a judgement call. Go with your gut.
  3. Expand your targeting using groups. As you create groups, it will be easy to start finding more users to target. So if you have tech blogs like TechCrunch on your list, you can add other ones such as PandoDaily, BusinessInsider and even CNet. Be careful though: there may be a reason your users don’t already like those pages. At Lyft and Udemy, we would use separate ad campaigns for “related” groups and monitor performance accordingly.

That’s a wrap folks. If you have questions, please feel free to ask and I’ll try to get to them.

P.S. This is why we started the Growth Hackers Conference and why I regularly read blogs like Andrew Chen’s or Sean Ellis’s. If you like this, I’ll also try to blog more to help share this kind of information. Tips like this used to be locked up in people’s heads — so poor entrepreneurs like me could never learn them. Now, you can pay $300 (with coupon code “FBadv”) and save months of time and thousands of dollars by going to conferences and reading blog posts about growth hacking. You might also find your next opportunity, meet a great candidate or connect with an industry insider who mentors you.

Written by Andrew Chen

October 28th, 2013 at 10:04 am

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Use this spreadsheet for churn, MRR, and cohort analysis (Guest Post)

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[Andrew: Christoph Janz has written some of the best essays on SaaS metrics and cohort analyses, and he was kind enough share the latest with us below. A bit about the author: Christoph is co-founder and Managing Partner at Point Nine Capital, an early-stage venture capital fund with a strong focus on SaaS investments. Their investments include Zendesk, FreeAgent, Clio, Geckoboard, Contactually and Unbounce. Christoph blogs here and is the creator of a popular SaaS metrics dashboard]

Christoph Janz, Point Nine Capital
Cohort Analysis Spreadsheet

If you’re a long-time reader of my blog (or if you know me personally) you’ll know that cohort analyses are one of my favorite tools for getting a deeper understanding of a product’s usage. Cohort analyses are also essential if you operate a SaaS business and want to know how you’re doing in terms of churn, customer lifetime and customer lifetime value. I’ve blogged about it before and have included “Ignore your cohorts” in my “9 Worst Practices in SaaS Metrics” slides.

My feeling is that over the last 12 months the awareness for the importance of cohort analyses has grown among startup founders. One reason may be that thought leaders like David Skok have been writing about the topic, another reason are web analytic tools like MixPanel and KissMetrics that make it simple to create cohort analyses.

And yet, many founders are still having difficulties with cohort analyses, be it with the collection of the data or the interpretation of the results. With that in mind I wanted to create a simple cohort analysis template for early-stage SaaS startups.

Download the Excel file here.

The idea is that you have to enter only a small amount of data and everything else is calculated automatically. Specifically, what you’ll have to type in (or import from a data source) is the basic cohort data: How many customers did you acquire in each month and how many of them were retained in each subsequent month. If you also want to see your churn on an MRR basis and get a sense for your CLTV, you’ll also have to enter the corresponding revenue numbers.

💌 Are you up to date?
Get updates to this essay, plus more on cohort analysis and SaaS growth.

If you’re not sure how to read a cohort analysis, here’s a quick explanation:


Here are some brief notes on each of the arrays in the sheet:

A1: This is where you enter the raw data. Start with January 2013 and enter the number of new customers that you’ve acquired in that month. Then move to the right and enter how many of those January 2013 customers were still customers in February, March, April and so on. Then move on to the next row. If your data goes further back than January 2013, extend the table accordingly.

A2 and A3: A2 takes the data from A1 and shows it in “left-aligned mode”, making it easier to compare different cohorts. As you can see the columns have changed from specific months to “lifetime months”. A3 shows the number of churned customers as opposed to the number of retained customers. Both A2 and A3 aren’t particularly insightful to look at per se, but the data is necessary for the calculations in B1, B2 and B3.

B1: Shows the percentage of retained customers, making it easy to see how retention develops over time as well as to compare different cohorts with each other. What you’ll want to see is that younger cohorts are getting better than older cohorts.


B2. This is kind of like the  “inverse” of B1, showing the percentage of churned customers as opposed to the percentage of retained customers. In any given row, the sum of the percentages of churned customers plus the percentage of retained customers equals 100%.

B3: B3 is similar to B2, but the difference is that churn isn’t calculated relative to the original number of customers of the cohort but relative to the number of the cohort’s customers in the previous month. Let’s say you have a cohort with 100 customers and after 6 months the cohort has been reduced to 50 customers. If you lose 5 customers in month 7, this represents 5/100=5% churn in B2 but 5/50=10% churn in B3.

So what’s the correct number? There’s no right or wrong here, it depends on the question that you want to ask. If you want to know e.g. “How many customers do I lose within the first six months?”, B2 (in conjunction with B1) gives you the right answer. But if you want to know what percentage of customers you’re losing per month (important when you look at data across multiple cohorts and for lifetime estimates), take a look at B3.

What you’ll want to see in this table is that after a usually relatively high churn rate in the first lifetime months churn starts to stabilize (because the people who never really adopted the product in the first place are now gone).

C1-C3: Same as A1-A3, just for MRR instead of customer numbers.

D1-D3: Same as B1-B3, just for MRR instead of customer numbers. What you’ll want to see is that your MRR churn is lower than your customer churn due to account expansions.


E1 and E2: If you enter the CACs for each cohort, these tables show you when each cohort breaks even.

Also take a look at the second tab in the Excel sheet, which calculates/estimates customer lifetime and customer lifetime value on a cohort basis. Note that the data is highly speculative for younger cohorts for which there isn’t much data yet.

Further notes are included in the Excel sheets.

If you have any questions or comments, please feel free to reach out!

Written by Andrew Chen

October 24th, 2013 at 10:30 am

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Zero to Product/Market Fit (Presentation)

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Starting from a blank canvas is the absolute hardest thing to do. It’s insanely hard. And yet the brave entrepreneurs in the tech industry do it, time and time again, and every year, a few are successful. It’s an awesome thing when it works though.

Anyone who’s working on a new product has question: How do you get to product/market fit? If you’ve been wandering the desert and you can’t retain your users, is there anything you can do about that? Is there a way to add structure to something that seems awfully random? How do you even know you’re there?

Below is a short and sweet deck that I created and presented a few years back to Stanford students. It’s focused on a simple idea: The path from zero to product/market fit can be a straight line, or windy, depending on what kind of idea you choose. I present the tradeoffs involved in creating a product that’s a bit more incremental, versus something that’s more breakthrough.

Although this deck is mostly focused on consumer and consumery enterprise products, it’s also fairly general, drawing ideas from the book Blue Ocean Strategy and others.

Hope you enjoy!

San Francisco, CA

📥 Get this deck as a PDF, plus new updates and essays in the future:

Above: It’s difficult to product/market fit, but today we’ll talk about some of the tradeoffs you can make for it to be easier!

Traction is everything – it’s what investors ask for. It’s what prospective employees and your team is looking for. But getting traction is really a reflection of your product “working” – where users are engaged and retaining, and there’s some organic acquisition. Product/market fit is when people who know they want your product are happy with what you’re offering – more on the topic here at the a16z blog.

Above: Important, once you get to product/market fit, you can shift your focus from the initial product to distribution and win the market. It’s important to do it in this order! In the haste to get to market, there are teams that start spending too much time on partnerships, paid marketing, etc. – all in preparation for a big launch. But the problem is that if you have a leaky bucket, then all those users at the top of the funnel won’t help.

Above: So what does product/market fit look like from a metrics standpoint? Really the best metric to think about is probably retention, which you’d analyze using cohorts. That means it’s sticking, and you need to figure out acquisition. Once it’s all figured out though, then you get the hockey stick. That’s what we’re all looking for, right?

Hitting the inflection point means that you can focus on scaling growth. The activities here are many of the things that I write about – funnel optimization, new user activation, creating notifications and email drip campaigns, etc., etc.

But it’s important to think of these are distinct activities.

If your product isn’t working, you won’t solve that by doing growth activities.

There’s a certain lightning-in-a-bottle aspect to building new consumer companies for that reason.

This is why 20-something year olds often build awesome new companies in Silicon Valley- they make lots of stuff, hit product/market fit, and the capital/talent in the Bay Area comes to help them scale

Entrepreneurs who are young are less encumbered by the notion of “this is how things have always been done” or “this kind of thing has never worked.” They can try, fail, iterate, and make their way towards something pretty new.

Above: Here are some specific ways that I might quickly judge if a consumer product has p/m fit.

You want to see DAU/MAU at >25%. A world-class leading DAU/MAU would be over 50%.

There’s a certain minimum for organic acquisition. You want to see hundreds if not thousands of signups per day, and a D1 of at least 30%. >70% would be world-class there.

And ultimately there’s some scale to show it works – probably 100,000 DAU.

For a SaaS product, you’d like at other metrics that might focus on monetization rather than engagement. Although some products can be evaluated using both sets of metrics.

Above: If you think you have that, send me an email :)

If you compare the metrics above, most startups don’t have product/market fit. It’s usually not working.

So what do you do?


Above: These days, most startups fail because of lack of P/M fit, not technology risk. So if there’s one thing that will kill you, it’ll be the product never quite working, and thus, all the subsequent problems that come with that: Lack of investor interest, employees leaving, cofounder fighting, etc. It’s a stressful time when it’s not working

I find that product/market fit is usually reached either right away, or you have to be incredibly thoughtful about your iterations if you’re almost there but not quite.

So how do you get there?

Above: I want to present to you one framework to think about this, and what kinds of tradeoffs you can make.

Above: Here’s a coffee cup. Product/market fit is actually easy to get, for a coffee cup. It has to hold coffee. That’s about it. There’s not too many variables in designing a cup, and so there’s not a lot to mess up.

Above: Contrast that to a digital product, where there are a million variables. It’s so much more complex than a coffee cup! It’s easy to mess something up.

(The above is a Facebook experimental project, called Paper, that never quite worked out)

It’s easy to mess up because the software medium is so rich. You can build or create anything, and you often end up with something that users can’t quite understand. Worst yet, once it’s not working, adding features won’t help! Sometimes the foundation is just not strong.

Keep these examples in your head, as two extremes on a spectrum. The coffee cup. The messy random digital product.

Above: The more I’ve thought about this problem, the more I’ve come to realize there’s really a spectrum between new versus pre-existing product categories.

The new product category carries a lot more risk, which we’ll discuss.

The pre-existing category carries less risk, but you face more competition, more expectations, etc.

What you want is a point in between those two extremes.

First, over time I’ve come to believe that you actually do want your product to be in a pre-existing product category. Google was not the first search engine, Facebook was not the first social network, and Microsoft was not the first OS.

However, those companies came to dominate those markets because they came in early, when the dynamics were still developing. And the markets grew and grew. But it was also clear what they offered, how they developed something killer.

Above: I want to be more precise than to say that the market must be big – more specifically than that, there must be a large number of users who are pulling on this category in the market. By pulling, I mean folks are actively searching for the product. You can see it in Google Trends. They are clicking on links that mention products in the category. There’s already spend.

Let’s contrast this to the big fake market sizes that entrepreneurs often trick themselves into. Yes there are a lot of college students, and maybe that’s your target market. But how many of them actually want this? Already own a precursor version of your product, or are googling or searching or talking about the category? That’s the real assessment of demand.

Above: Related to the above point, the best markets already have competitors. And maybe they are pretty decent. It’s a good sign when folks can make a decent living building products in the space, but you just have to find a wedge to get in. Or maybe you have a clear thesis on why and how the market will get a lot bigger quickly.

The best case is where the market is large, but fragmented – perhaps because the competitors are not that competent or there’s something structural, like regulatory issues, that keep things small.

Above: If you are in a pre-existing market with competitors and pre-existing demand, then you need a wedge. You need to analyze the market, understand the segments, and figure out how to break in.

My favorite book on this topic is Different: Escaping the Competitive Herd. Worth reading.

Above: There’s a huge advantage to building for yourself because the intuitive advantage it gives you in iterating quickly is fantastic. This isn’t always possible, but it can be a good thing.

Just as we discussed with the coffee cup, one way to get to product/market fit quickly is to just copy something.

There are a ton of problems with this strategy though – if you’re the second player with the same product, you’ll always remain the second player. Your team will hate this strategy. Investors won’t get it.

Sometimes the market is big enough to make this work, but it’s not often.

Above: The better approach is to do a twist. On the spectrum of pre-existing market to new market, pick a point in the middle. Build something that consumers fundamentally understand, but with a clear innovation that you can market around.

A lot of getting to product/market fit is being thoughtful about the category, the axis of differentiation, etc.

I want to run through some common mistakes that people make.

Above: We love these “X for Y” type companies. And it can be a way to generate cool ideas, but it doesn’t say much about the underlying market size and existence of pull in the market.

For instance, “Pinterest for dogs” and “Pinterest for businesses” are both X for Y ideas, but only dogs are a segment of Pinterest. A business-targeted product is more a description of the mechanics of the product, not a smaller set of the broader Pinterest market. And as we mentioned, identifying a wedge in a pre-existing market is key.

📥 Get this deck as a PDF, plus new updates and essays in the future:

As a result, you can think about existing versus new markets using The Substitution Test. If I use this new product you’re building, does it take away engagement or $ from something else. If so, then it’s truly a segment and a heads-on competitor. It’s counter-intuitive but I consider that a good thing, because they you are truly competing in a pre-existing framework- you just need the right functionality to win.

Above: Every existing market has a baseline of product quality, functionality, etc. We’d all love to build the minimum viable product, but sometimes it doesn’t work because the category has evolved sufficiently that you need more than the bare bones.

Sometimes people love a certain kind of tech. Today it might be crypto, tomorrow it might be something else. And they are looking for some kind of product and category to apply to, rather than fundamentally solving peoples’ core needs. It’s an easy mistake to make for technologists.

As you know, I hate new consumer behavior :) Sometimes this can work but it’s rare. At the very least, products have to tap into the same fundamental human motivations that have driven us for 100,000s of years.

Above: This is a San Francisco thing, but entrepreneurs are often building things that sound cool only to each other. No one else cares, but it’s fun and captures some tech trend that’s confined to the SOMA bubble. I call this “art for artists”

Above: This is common. You can execute everything correctly – iterate quickly, great tech, hire well, etc. – but ultimately still take huge risks on your product and market in such a way to not actually be successful.

Above: So let’s say you get something that has P/M fit, and it starts to take off! It’s a good problem to have. So what do you do?

Above: Luckily, you can summon the power of the Bay Area to help you solve this.

There’s a large body of work and experts on scaling companies. You can raise money, hire operators who’ve scaled, and you’re on your way.

I don’t mean to downplay this, except to say that the zero to product/market fit part is so hard that this next stage will be much easier than the first. Ultimately, there’s a few growth channels that work, and it’s much more of an optimization problem.

Above: For the paid ads channel, you’re ultimately looking at it as a LTV versus CAC problem. With some blended/organic traction too.

The ecommerce companies, OTAs, and marketplaces often grow like this.

Or you want to build a viral loop – if your product is social or collaborative in nature – and you can optimize for invites or content sharing or some other loop. This is a deep topic that could be a multi-part series of its own things.

This channel is used by the social networks, video sharing products, but also even productivity/collaboration products in the workplace.

If your growth loop involves a lot of user-generated content, then you can build on Google. For products that are really about reviews on local businesses, companies/products, or even real estate, we see them use this loop.

Above: If you’re at product/market fit, I want to hear from you! Send me a note anytime. Thank you :) (For future reference, you can also download the PDF here).

Written by Andrew Chen

October 14th, 2013 at 10:50 am

Posted in Uncategorized

The Rise of Fat Venture Capital

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Reinventing the VC industry
In 2007, before YCombinator and AngelList had changed the industry, I worked in a nondescript office park in the heart of the venture capital industry off Sand Hill Road. Amid the leafy sprawl of buildings next to 280 and Stanford University, billions of dollars were and are invested out of the fancy offices of VC/PE firms you’ve never heard of. The whole industry has been shrouded in opaqueness since it was created decades ago, built on relationships from business schools, professional networks, and investor referrals. In 2007, I worked at a big firm as an Entrepreneur-in-Residence, and did my best to make sense of this world.

The first thing I noticed was that the venture capital industry seemed so opaque as to be self-defeating. Entrepreneurs didn’t know who to talk to, and most can’t name more than 2 or 3 firms. While venture capitalists would pontificate about the importance of differentiation, all of their websites consisted of a bunch of blue-shirted dudes that all invested in the same stuff. This always confused me.

I remember at one point I had lunch with a General Partner of a Sand Hill firm and we talked about PR:

Me: So why don’t VCs do more PR?

General Partner: Great entrepreneurs know to come to us. We don’t have to go to them. And in fact, having great investment returns and being out there in the public don’t really correlate, so we don’t do it.

It sorta made sense to me then. This approach had been true for the first few decades of the venture capital industry. Getting ahold of a GP through an entrepreneur’s network seemed like a basic test of competence.

Thus, other than raising the money, you didn’t need much overhead to run a VC. You just needed a couple guys in a nice office, 1 admin per partner, and a website. If you wanted to get fancy, you could have a venture partner or associate or two. Maybe 6 professionals plus their admin staff.

But just a few years later, in 2013, we now see VC firms with over 80 professionals. Not just one, but multiple firms are doing this. And even small funds are experimenting with events, content marketing, software infrastructure, etc.

This is the new era of “Fat Venture Capital.”

How did this happen?

Lots of trends driving differentiation of capital
There’s a lot of  trends driving VCs to differentiate. The obvious stuff: It’s cheaper to start a company. There’s more seed money, in the form of both accelerators writing $20k checks and seed funds writing $500k checks. There’s a lot more information out there, for both what firms to pitch and what terms to expect. There’s been a lot of great material covering the trends affecting startups so I won’t elaborate more here.

What’s less frequently discussed has been the recent trends on the venture capital side. A lot of firms decided that the high market risk, low tech risk nature of digital bets meant that they needed to employ a “barbell” strategy. Lots of seed stage investments, plus lots of late-stage investments, and not much in-between. Many early firms that traditionally would lead Series As and then hand off the larger “late stage venture capital firms” now see themselves playing at every stage. But if you wait to do later stage bets, then the traction is more obvious, and there’s more head-to-head competition between firms as they chase obvious deals. More head-to-head competition means that differentiation matters.

Similarly, the lower capital requirements of startups means that there’s been a lot of new firms started recently. It’s a lot easier for some successful startup execs to start a new VC fund by raising $20M or $50M, rather than a traditional VC that raises $200M or more. This has brought a lot of entrepreneurial energy to a sector that often behaves like a sleepy money management industry, rather than the dynamic startups in which they invest.

This entrepreneurial energy is especially important in an industry where the guys who invented the industry are now long retired. When you read books or watch documentaries about the early VC industry, you can see that the early guys who knocked on the doors of insurance companies in the midwest were truly entrepreneurs. Years later, many firms are led by professional investors who are two generations removed from these early innovators. While the successful-entrepreneur-turned-VC is still widely admired, plenty of firms are stocked with MBAs-turned-associates-turned-VCs, who are prone to view VC as a career rather than an competitive and entrepreneurial endeavour in itself.

And of course, many large firms are simply reacting to the competitive pressure from Andreessen Horowitz. They popularized this services-based approach and this WSJ interview is worth reading about their approach. The CAA analogy is particularly insightful.

The unbundling of the General Partner
The impact for entrepreneurs is straightforward. It used to be that an investment from a General Partner of a VC was a bunch of things bundled together:

  • Money
  • Expertise
  • Oversight
  • Professional network

These days, we’re moving to a model where this all gets unbundled. Money comes from the VC, but also from a long list of investors sourced from crowd funding platforms. The professional network for a firm is supported by a large services team. Advice can come from a long list of advisors and operational folks that are easy to track down on LinkedIn. Functions like executive recruiting, technical recruiting, PR, etc. don’t come from the particular GP who wrote the check, but rather, the services team that works for them. Instead of the GP sending you random links they’ve read, instead there’s dashboards and link-sharing services that are private amongst the other portfolio companies of the investor.

Furthermore, we’ll see the internal functions of a VC firm, like marketing, evolve to be pointed towards entrepreneurs rather than investor management. Instead of press releases on PEHub, we’ll see firms act more like SaaS marketers: Content marketing, professional events, even paid advertising. Why shouldn’t a VC firm be buying Facebook ads targeted at the next crop of Stanford CS graduates?

Ultimately great VCs will continue to do well. Many won’t embrace the Fat Venture Capital model, but will do fine, because their judgement, expertise or network is just that good. But for the rest of the industry, moving to differentiate will be the norm.

Entrepreneurs will benefit. More transparency and competition will mean that the “the good ones will come to us” attitude will be a thing of the past. Instead, great VCs will chase the great entrepreneurs, because in a world where the supply of great entrepreneurs is smaller than the supply of plain ol’ money, that’s the way it should be.

Written by Andrew Chen

October 9th, 2013 at 10:30 am

Posted in Uncategorized

How Google and Zynga set & achieve meaningful OKRs (Guest Post)

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[The topic of setting goals, especially quantitative ones, is a really important topic for any team and any startup. A common format has been the OKR, or “Objectives and Key results” which is in use at Google, Zynga, and many other companies. It consists of setting an objective, and also a series of measurable results aligned with that objective. My good friend Kenton Kivestu, (now Flurry but previously Zynga and Google) has experienced this framework first hand and had a lot of insightful stuff to say about it. You can follow Kenton on Twitter and read his blog here. -Andrew]

Kenton Kivestu, Flurry:
How to set & achieve meaningful OKRs

In 2011, when I joined Zynga to work on the mobile poker franchise, we were getting trounced by a competitor named Texas Poker. Their UX was better. They were smoking us on appstore top grossing rankings. And they had 5x the features (not to mention a “premium” pro version of the game they’d just launched). For a company who unequivocally dominated the poker space on FB, our position in the mobile poker market was borderline comical.

The team set an OKR to take the throne: become the #1 top grossing iOS poker game. And then something incredible happened, we did (~6 months later). In my career, I’ve seen many an OKR go haywire (both at Google and Zynga) so this post is my attempt to distill & isolate the common traits I’ve seen in good implementations of an OKR.

First, what is an OKR and why bother?
Google has used OKRs since 1999 at the urging of KPCB partner John Doerr and Rick Klau (former Googler, now Google Ventures partner) has a good post on what is an OKR is (and it’s history). And you can read up on them at Quora too. But the TL;DR is an OKR is a stated goal, known to the whole company and has a pre-defined rubric to measure your success in achieving it.

Whether your at a start-up or big co, the only thing in endless supply is constraints. Time, developer resources, energy and the list goes on. And in a resource constrained world, the best plan of attack is to marshall resources and focus efforts on the best leverage point.

Now the question is, how do you set good OKRs? Like all things, there are many ways to successfully skin a cat but the 3 most common traits I’ve seen in teams that have set and achieved awesome results with OKRs are these:

Contrary to popular belief, this doesn’t mean the the OKR needs to be explicitly quantitative (eg drive X% increase in sign-ups or Y% year over year growth in recurring revenues), although it’s fine if that is the case. But it definitely needs to be measurable in the sense that the team can unequivocally evaluate their progress at the end of the OKR period.

For us, the iOS app store Top Grossing rankings were our measuring stick (for better or worse). There was no fudging the numbers and it was dead simple to measure, we were either ahead or behind.

OKRs are like money. Mo’ money, mo’ problems. The surest way to negate any positive impact from a good OKR is to set 10 good OKRs. It can seem alluring at first – “we’ll accomplish so many things this quarter/year!” – but it will backfire. There is little doubt that 10 good OKRs is worse than nothing. Your team will be torn between competing priorities – should John the engineer work on X or Y this sprint? One will get us closer to OKR A and the other toward OKR B. Inevitably this leads to prioritizing OKRs – this might work if you have 1 or 2 but anymore than that is going to be a recipe for a wasted quarter.

For us, we had 1. If a feature/chunk of work didn’t directly contribute to us climbing the iOS top grossing rankings, it was de-prioritized. As a result, we got a late start on expanding to other platforms (Android tablets, Kindle Fires). That was painful but the focus brought the right end result, since the iOS Top Grossing “pot o’ gold” was orders of magnitude more rewarding than early adoption of Fire or And tablets.

Worth doing
Again, this seems obvious but is worth stating. A good sanity test is ask yourself, “If the company were a person, would it put the successful completion of this OKR on its resume?” If the answer is no, boot it. All too often well-intentioned but “empty” OKRs end up dictating resource allocation. These culprit OKRs typically rise out of a discussion around some product tactics (eg let’s reduce friction in the sign-up flow by X%).

Ok, but OKRs stunt our creativity
An oft argued counterpoint is that OKRs will stunt creativity and the team’s ability to tinker and meander into some great discovery. Defenders of this theory highlight examples of accidental discoveries leading to huge innovations – but this is missing the point. OKRs don’t preclude accidental discoveries, they simply make sure that in absence of a brilliant accident the team is on track to do something else meaningful.

If you’re finding your team sets quarterly OKRs only to trash them each quarter given a brilliant mid-quarter discovery, you’re either:

  • a 2-sigma team that repeatedly launches industry leading core features every quarter despite not initially planning them
  • setting OKRs not “worth doing” as evidenced by repeated willingness to ditch them
  • unfocused and need more operational discipline

In conclusion
OKRs are not a panacea. And they can lead a team astray if they don’t keep them focused, measurable and meaningful. But the flip side is that a well-set OKR can generate a 10x result. OKRs can drive focus and relentless execution, and in turn, those drive incredible results.


Written by Andrew Chen

October 1st, 2013 at 11:00 am

Posted in Uncategorized

Case studies from “Why you can’t find a technical co-founder”

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This is a followup guest post by Elizabeth Yin on her popular essay, Why you can’t find a technical co-founder. Liz is the CEO and a co-founder of LaunchBit, an ad network for email newsletters.  Previously, she worked at startups and Google, and went to MIT for her MBA, and Stanford before that.

Elizabeth Yin:
Why you can’t find a technical co-founder (part II)

I received a lot of emails about my last post on Andrew’s blog: Why you can’t find a technical co-founder, which discussed:

  • Deal-breakers for non-technical entrepreneurs who are looking for technical co-founders (for example: location isn’t a deal breaker but idea validation is)

  • How to get traction on a product idea without a product

  • 3 examples of startups (AngelList, Yipit, and Beat the GMAT) who have become successful today but started without full-fledged products

So, I wanted to dive into this topic more.

Building a minimum viable product with a static website
It can be all too easy to want to build out a full-fledged minimum viable product in code.  But, as an entrepreneur, you only have so much runway.  One of the best ways to cut down on the time required to get to product/market fit is to skip the coding process entirely.  It’s more time-consuming to program in one direction only to realize that you need to build something in a completely different direction.  Not to mention, morale takes a hit when you have to scrap all your work.

So, one of the best ways to speed up time-to-market is to build a minimum viable product with a static website.  Disclaimer: this isn’t possible for every product, but a lot of web business ideas can be built initially with simple static websites.

Here are two case studies of companies that first used simple websites to test their respective business ideas before building out full-fledged dynamic sites.  It was these static websites that helped them find product-market fit very quickly, which enabled them to scale sooner.

Fandeavor is a company that offers game-day experiences.  Founders Tom Ellingson and Dean Curtis previously worked at Zappos, a company that would often sponsor sporting events.  Tom and a handful of Zappos employees would often get red carpet treatment at these sporting events, but these special experiences were limited only to high-end sponsors.  “But what if general consumers could purchase the same awesome sporting experiences too?” they wondered.  So before they left their full-time jobs, they decided to test whether this idea had any legs.

Tom called up a contact at the University of Nevada Las Vegas (UNLV) who ran sports tournaments.  Tom convinced him that the Fandeavor team could help him sell special sporting experiences around the upcoming tournaments.  These gameday packages would include things like signed basketballs, box suites, special sports gear, and even the opportunity to present the game ball at mid court.  In parallel, Dean set up a basic website that would provide information about these gameday experiences.


Fandeavor’s initial website

Through a cross-promotional partnership with UNLV, who promoted these experiences through their Facebook fanpage, these first experiences quickly sold-out.  It was from testing these first experiences that the Fandeavor team realized that doing cross-promotions with other organizations to promote their gameday packages helped them build their audience and also made their sales successful.

The duo continued testing their business idea by offering more experiences on their website.  They even created sections of their site that were empty but helped track demand of what teams and sports people were most interested in.  Just simply by tracking clicks on their relatively simple site, they could figure out what types of experiences to offer next.

Tom and Dean left their day jobs at Zappos to work on Fandeavor full-time.  But soon, they realized that the kind of experiences people were interested in were not necessarily box suites.  A lot of consumers wanted even simpler things — help with travel, hotels, and other logistics in building a great trip experience around tournaments and games.  These were experiences that the team could build with virtually no special business development deals.  So, they started curating these experiences themselves manually.  Eventually, they were able to develop a process around doing this efficiently.

From doing quick tests using a simple website, Fandeavor was able to figure out the mechanics behind how to get their supply (the right curatable gameday experiences) and the demand (promoting through cross-promotional partnerships).


Fandeavor’s website today

Once they established the processes for scaling both sides up, they were able to later build a much more sophisticated backend to curate all their experiences and build their team to repeat these processes.  Today, Fandeavor has raised $525k and is growing 50% month over month.

Moveline is a company that aggregates moving quotes from moving companies.  Founders Kelly Eidson and Fred Cook realized that the moving process was difficult for people for a whole variety of reasons and wanted to help make this process better.

Fred and Kelly collected leads on a simple website to start working with qualified consumers.  And soon, they started going into homes of people who were moving to talk with them about moving issues.


Moveline’s initial website

Kelly and Fred quickly learned that it was a headache for people to document every little thing they had to move and then submit all those items to different moving companies to get quotes.  Kelly and Fred realized that the crux to making the moving process better was to develop a better way of itemizing objects.

At first, the Moveline team was not really sure how to do this better.  They initially went into people’s homes and manually categorized items into spreadsheets.  They repeated this with dozens of people who were moving.  The breakthrough in building a process around this came when someone in a different city contacted them requesting their itemization-help.  Not able to physically go to that person’s home, Fred and Kelly asked to do the itemization over Facetime.  It turned out they were able to accurately and quickly itemize over the video conferencing software, which later became a part of Moveline’s core product.  It turned out that using video conferencing software gave them an edge — they could itemize just about any move from anywhere without requiring local on-the-ground teams.

It was from these initial conversations with dozens of consumers that Moveline was able to tease out a very specific problem to solve in moving.  And, once they were able to manually figure out how to use technology to solve this problem, they were ready for scale.  It was because they used a simple website that fed them leads, but still required the team to “be the product,” they were able to really understand and solve this particular problem in moving.

Moveline’s website today

Only once they had a clear idea of what needed to be built into the product, Fred started hiring a team of engineers and product people to build the first version of Moveline’s software.  Today, Moveline is an 18 person company, has raised $3 million in funding, and since launching nationally in March has added over 200 moving companies to its network.  They now serve customers in over 100 cities in the U.S. and internationally.

Both Moveline and Fandeavor primarily used static websites to collect leads and kickoff interactions with potential customers.  Although static sites don’t seem very sophisticated, through the use of simple input fields and forms, you can collect information to vet potential leads, and you can use them to collect customer insights.  Static websites are a great way to quickly test your product and get to product/market fit.

For more examples of companies who built minimum viable products without coding, see these awesome posts written by Ryan Hoover and Vin Vacanti:

P.S. If you want to learn how to build your first web prototype without coding, attend this workshop I’m hosting on Building a Mobile-friendly Websites Without Code on October 3.  Get 25% off with this discount code: “andrew-hustler”.


Written by Andrew Chen

September 25th, 2013 at 11:01 am

Posted in Uncategorized

Easter Egg Marketing: How Snapchat, Apple, and Google Hook You

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This is a guest post by Ryan Hoover, the Director of Product at PlayHaven and creator of Startup Edition. Follow him Twitter at @rrhoover or visit his blog to read more on product design.

Ryan Hoover on Easter Egg Marketing

Last week I received this Snapchat from a friend:


I was quite surprised to see a black-and-white photo. “When did Snapchat add filters?” I asked in response.

With pride and delight, my friend responded with instructions: enter the text caption, “B&W…”, to activate the black-and-white filter. Grinning, I immediately sent a dozen B&W Snapchats to my friends, anticipating their curious replies because I knew something they did not. I knew a secret.

The black-and-white filter is one of Snapchat’s many Easter eggs. An Easter egg is an inside joke or hidden feature. The term was first coined by a programmer who hid his signature inside a secret room within the Atari 2600 video game, “Adventure”. Decades later, Easter eggs are a recurring meme in gaming, a head-nod to players, and have expanded into non-gaming, consumer products. Look closely and you may find Easter eggs hidden within your favorite applications.

The nature of Easter eggs is an oxymoron. Why would Snapchat or any other product hide functionality. Apps should be intuitive and accessible, right?

Easter eggs go against users’ expectations and in turn serve as a powerful form of marketing, enabling companies to:

  • Get People Talking – People love knowing secrets but they like to share them even more. Consider the last time you heard office gossip or a twist ending to a much-anticipated film. Did you have an urge to tell others? Wasn’t it amusing to see your friend’s reaction as you revealed the secret?
  • Bring People Together – As a child, we were told not to withhold secrets from our parents. But we did so anyway. Mutually shared secrets between our friends and siblings brought us together. The same is true for Easter eggs. Those aware of the Easter egg secret share a common understanding, separating people “in the know” from outsiders. This helps form a bond between holders of the secret and strengthens a community.
  • Build a Brand – Easter eggs are fun and produce a “WOW!” effect. This delight forms a memorable connection between the brand and its users through a shared understanding not privy to everyone.

Here are three companies that use Easter eggs to spike engagement, establish a community, increase word-of-mouth, and build a memorable brand:

Apple (Siri)
When Siri launched, Easter eggs bombarded the internet. People could not get enough of Siri’s unexpected, witty answers to the hidden questions and phrases.


Siri’s humor was inescapable as blogs, TV shows, and social networks exploded in discussion. Websites dedicated to its Easter eggs sprung up. A Google search for “siri funny questions” results in over a million results. The free marketing instigated by these Easter eggs was arguably more impactful than Apple’s multi-million dollar advertising budget.


The secretive and clever nature of Easter eggs encouraged users to explore the product to uncover its secrets. When users cracked an Easter egg mystery, they acquired bragging rights, often sharing their discovery with friends. As a result, Siri became a celebrity as people promoted her wit.

As I was walking down Howard St. in San Francisco, I overhead a group of people talking.

“What are those pink mustaches for?” A woman said, pointing to a blue sedan.


“I don’t know! I see them everywhere.” Her friend replied.

This wasn’t the first time I had heard this question. I observed two other groups of people ask the same question earlier that week.

Lyft, a ride-sharing service, aroused curiosity by decorating its fleet of vehicles with furry, pink mustaches. Instead of plastering its brand name on the side panels of its cars, Lyft used an Easter egg to catch people’s attention and get them talking. After seeing a half a dozen pink mustache-sporting vehicles, one is bound to resolve the mystery.

As users demonstrated an effort to understand the meaning behind the silly spectacle, by asking friends or searching online, Lyft became more memorable. This is especially important in competitive markets, where recall and top-of-mind-awareness determine which transportation service one turns to the moment they need a ride. Lyft creatively stood out from the competition by hiding its brand behind an Easter egg: a pink mustache.

Google is known for its shenanigans and clever Easter eggs. The search goliath often replaces its logo with Google Doodles, turning its name into art in honor of holidays, special events, and anniversaries. The company even turned its logo into an interactive synthesizer and zamboni mini-game. These rotating doodles spur conversation and encourage people to visit the homepage to see what others are talking about.


Less obvious, though arguably more impactful, are Google’s hidden search terms. Visit google.com, type in “do a barrel roll”, and press enter! Be prepared for a delightful surprise as the screen rotates. This hidden gem continues to spread across the web years after its release in 2011, as evident by the hundreds of tweets like the one below, shared just this week.


Despite being a multi-billion dollar corporation, Google demonstrates playfulness, charming its users with fun surprises. Although Easter eggs provide no value to the product’s core offering, it creates a personality to the brand and encourages users to engage in marketing the product by forwarding it to friends.

Easter eggs are more than a fun distraction and can serve as a powerful form of marketing. Consider how you can incorporate Easter eggs in your product or service to increase engagement, establish a community, get people talking, and build a memorable brand.

And now that you know the secret, don’t you want to share it? ;)

For more essays on product design, subscribe to Ryan’s email list.

Written by Ryan Hoover

September 17th, 2013 at 10:15 am

Posted in Uncategorized

How is Yahoo really doing? Here’s the Google Trends data (Guest Post)

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Note from Andrew: I recently wrote an essay on the virtues of using Google Trends to analyze the underlying traction of products instead of focusing on PR. I mentioned offhand that it would be interesting to break Yahoo’s properties apart to see how they are doing independently of each other, and Matt (bio at the end) sent me some great notes on the topic – you can also follow him on Twitter at @mattgierl. It’s worth noting the caveats that Google Trends data is directional, probably doesn’t do  good job on mobile, etc., but nevertheless I found the findings thought-provoking.

Matt Gierl:
How is Yahoo Really Doing?


To say that there has been a bit of buzz around Yahoo as of late would be a drastic understatement.

From Marissa Mayer’s appointment to CEO, to their re-launch of photo-sharing app Flickr, their $1.1B acquisition of Tumblr, and most recently, their bizarre (ingenius?) 30-logos-in-30-days PR campaign: purple-mania has reached a fever pitch. Investors seem to have bought into the hype as well, as Yahoo’s stock price has nearly doubled from its 52-week low to over $28 per share as of the time of this writing.

But I, for one, am not ready to count myself as one of the believers. Let me tell you one of many reasons why.

In Andrew’s post last week, he made the case for why navigational query volume is a better proxy for evaluating  a company’s traction in the market, as opposed to something like unique monthly visits (which can be inflated a variety of ways which Andrew discusses). Fortunately for us all, the Google Trends tool makes this ridiculously easy to do.

In the post, Andrew takes us through the charts for some of the current and former high-flyers in the tech space including AirBnb, Pinterest, Twitter and, last but not least, Yahoo. Here’s what he found:


Surprisingly, Yahoo experienced pretty strong growth until mid-2009, where we see volume stall a bit.  Further still, there has been a noticeable decline from the end of 2012 to today. While these are some interesting trends overall, it leaves a lot unsaid in terms of how the various Yahoo properties (e.g. Yahoo News, Yahoo Sports, etc.) may be affecting their overall navigational query volume, so I did some digging and found that it’s actually a bit of a mixed bag.

The Good

Several Yahoo properties are doing well on navigational queries: News and Sports, in particular.

Let’s start with Yahoo News.

Yahoo News

yahoo news

While there aren’t a ton of positives with respect to navigational query growth for Yahoo’s properties, there have been a couple bright spots. To begin, we see that Yahoo News has shown steady growth over the last 10 years. Whoever said journalism was dead?

Yahoo Sports

Yahoo Sports has enjoyed a positive trend overall as well, interspersed with cyclical spikes corresponding with the starts of the NFL/NCAAFB and MLB seasons. It’s tough to say definitively, but this is likely a result of the explosion of fantasy sports in the U.S. and abroad, which grew 60% from 2007-2011 and has shown no signs of slowing.

yahoo sports


The Bad

On the other hand, several Yahoo properties are flat. As I alluded to earlier, it’s definitely not all roses for Yahoo though.

Yahoo Finance

Here, in one of the more interesting of all the Yahoo property charts, Yahoo Finance, we see a modest growth trend obscured by a stalagmite-like formation which occurred during the economic collapse of 2008-2009. The bump in ad revenues was a small silver lining for them in the midst of their stock plummeting to half of its former value, I’m sure.

yahoo finance 

Yahoo Weather

Yahoo Weather appears to be falling out of favor as well, as volume has declined to roughly half of what it was at its high at the start of 2011.

yahoo weather


The Ugly

Several Yahoo properties are hitting slumps though, even in important categories like Mail, Travel, and Shopping.

Yahoo Mail

Turning now to one of the most critical properties in the Yahoo ecosystem, Yahoo mail, we see an even bleaker story still. After experiencing a strong period of growth from the Since mid-2010, search queries for Yahoo Mail have steadily declined, coinciding directly with the rise in volume that Gmail has enjoyed over the same time period.

yahoo mail



Yahoo Travel

Yahoo Travel hasn’t fared much better. Volume has continued to decline steadily since 2007, likely due to the growth of more sophisticated travel aggregators like Orbitz, Kayak, Hotwire, Hipmunk and even Bing Travel over the past 5 years.

yahoo travel

Yahoo Shopping

And finally, in perhaps the most defining chart of all, Yahoo Shopping continues its decade-long decline in navigational query volume. But let’s be honest, when is the last time you’ve intentionally navigated to Yahoo Shopping for your consumer product needs? Yeah, I can’t remember either. And I bet you can guess one of the major culprits who appears to be syphoning off this volume…

yahoo shopping



Now, I understand that navigational query volume is not everything and that there is much more to the Yahoo story than what I’ve managed to include here, but nonetheless, this doesn’t instill much confidence in their ability to reverse the trend and drive growth moving forward. When it comes down to it, they simply don’t have a single rockstar product to speak of that can serve as the archetype for success in the post-Web 2.0 era.

And while some may be quick to suggest that Tumblr fills this role, I think it’s still too soon to call.  Hopefully for Yahoo’s sake, that hint of stagnation in Tumblr’s chart since 2013 is merely a red herring.



The Recap

So to recap, we have:

  • Two of Yahoo’s properties, News and Sports, showing moderate to strong growth in the last decade
  • Yahoo Finance and Yahoo Weather in steady decline since 2009
  • Three of Yahoo’s key businesses in Yahoo Shopping, Mail, and Travel getting their lunches handed to them by the competition
  • And, finally, a brand new $1.1B question mark

So while I must admit that I rather enjoy a gimmicky PR campaign every now and then, the negatives significantly outweigh the positives here. As a result, until Yahoo can show us some real, tangible traction with a core product, I will continue to remain a doubter.

About the author: Matt Gierl is a an MBA student at UCLA Anderson, digital marketer, and sporadic blogger over at Medium. As a former consultant at the data science firm, dunnhumby, Matt has helped some of the world’s most iconic brands achieve new growth by better understanding the behaviors of their most loyal customers. Matt currently resides in Los Angeles where he is an MBA student at UCLA Anderson. If you happen to be in the area, I hear he is willing to grab coffee and chat about marketing, tech, startups and the like, so get at him at @mattgierl.

Written by Matt Gierl

September 9th, 2013 at 10:31 am

Posted in Uncategorized

Ignore PR and buzz, use Google Trends to assess traction instead

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[Yelp shows a healthy navigational search graph – lots of people are continuing to search for its brand, and you can see some seasonality where it peaks every August, goes down during Q4, then starts coming back up in Q1.]

PR buzz is useless for assessing product traction
Traction is everything, and it’s easy to confuse press buzz with actually having product/market fit. Writers for blogs and newspapers love novel ideas that sound amazing on paper, especially when the new products are being introduced by credible entrepreneurs. However, when you’re in the business of making product and investment decisions, it’s important to understand what’s actually working and what’s not- having buzz isn’t enough.

This article is about one of the ways to answer the question, “Is X product really working?” The easiest, fastest, free way to assess the traction of a competitor or buzzy startup is to use Google Trends. It’s a great tool from Google that gives you a chart of how many search queries are being generated, which is a fantastic way to see if the consumer pull demand is increasing or decreasing.

Navigational queries are the best representation of consumer “pull” from the market
The reason why this works is that Navigational Queries are one of the three major kinds of queries that consumers plug into search engines. People search for a brand like “yelp” “facebook” “zynga” when they want to directly navigate to that domain, and as you might imagine, it’s a strong indicator of customer loyalty. These navigational searches represent the “pull” of the market, and if the graph of this pull is flat or declining, then you might have a problem on your hands.

Consumer demand is the leading indicator, uniques are the lagging indicator
The reason why a graph of navigational queries is so powerful is that it partially removes three major sources of traffic which are often inauthentic, unsustainable, or susceptible to artificial inflation:

  • Unsustainable paid ads, where spending outpaces lifetime value of the users acquired. Or, commonly in mobile, where a bunch of ads are purchased in an attempt to shoot an app up in the charts.
  • Content farming, where a lot of low-quality content pages are created. Although a lot of users may end up arriving at these pages as a result of searching and clicking on something, they don’t know (or care) that they are on these pages- these aren’t really long-term users or customers.
  • Drive-by traffic, which often looks like photo/video hosting, or IFRAME’d content, which people click into from Twitter, Facebook, or some other social channel. Similar to content farming, these are often “one click wonder” visits where people click in to view some content, but don’t actually engage or care about the underlying product.

Of course, doing something simple like graphic navigational queries isn’t strong enough to remove the entire effect of the above- instead, it just mutes it a little bit, and that’s the important thing in the long run. Ideally you want to see a long, smooth set of traffic that’s been built over months and years, not a huge spike driven by unsustainable means.

This consumer demand curve is really the leading indicator of traffic. Generally if I continue to see the graph going up and to the right, I will think that a company is pretty healthy. If it’s not, no matter how much is being written about them in the press, I’ll be skeptical.

Zooming in on traffic seasonality
One of the best parts about Google Trends is how granular it gets. You can ask to see the graphs on a 30-day rolling basis, as well as breaking it down by country. One the common patterns you’ll see is that there are two kinds of websites:

  • Time-saving, where you use them at work Monday-Friday and they help you do your job.
  • Time-wasting, where you use them at home in the evenings, and heavily on the weekends. They spike Saturday/Sunday and flatten out during the weekday.

Here’s an obvious example of a time-saving product, Linkedin, which shows huge gains during the week but then it gets depressed during the weekends- amazingly the weekdays are 100% higher than the weekends, according to this chart!

Screen Shot 2013-09-03 at 2.56.35 PM

Triangulate with AppAnnie, Facebook stats, Twittercounter, Twitter search, and Quantcast
Of course, there’s a ton of caveats to using such a simple tool. Searches will rise even when unsustainable methods are used, just because more consumers are being exposed to the brand. Similarly, the other big issue is that mobile apps don’t often benefit from search engine traffic the same way that websites do, since none of the content in the apps are being indexed.

Thus, remember that this analysis is strongest for web products, and to think of this as directional.

If you want a higher degree of confidence, consider using AppAnnie, Facebook stats, and Quantcast to figure out what’s going on. With AppAnnie, you can see the rankings of mobile apps and how they change over time. This can be useful in conjunction with Google Trends, since you can look up something like Angry Birds, which used to be the top grossing iOS app, which you can see below:

Screen Shot 2013-09-03 at 2.49.03 PM

When you combine the above drop in rankings to their Google Trends chart, which I’ve included below, you can see that Angry Birds is stalling quite a bit:

Screen Shot 2013-09-03 at 2.50.58 PM

You can also use Facebook stats services, for the products that allow for Facebook sign-in, to get a sense for how things are directionally going. Quantcast and Compete are also free services that let you look up uniques/month for web products, though they are often wildly in conflict with each other since they use the same kind of sampling that makes Alexa unreliable too. Back in 2006 I wrote about how Alexa works and all the flaws with their methodology, that I learned first-hand working with Nielsen/ComScore in my adtech days.

Qualitatively, doing a Twitter search on the brand is great too. Ideally you want to see a ton of authentic tweets about people actually talking about the product- again, the focus is on whether or not people really understand they are using a product and what they think of it. You can supplement this with a service like Twittercounter to see if their follower count is growing well over time. If a product claims a ton of uniques in a press release, but very few followers and very little Twitter conversation, it might be inauthentic traffic. (Thanks to Adam Besvinick for reminding me to mention Twitter searches)

How are Airbnb, Foursquare, Pinterest, Twitter, Quora, and Yahoo doing?
And finally, I thought it might be interesting to share the current graphs of a couple current (and previous) high-fliers to see how they’re doing on Google Trends. Some of the are universally considered to be doing well, and some are not. let’s see what Google Trends says, at least on a high-level.

Seems to be growing very nicely. You can see some seasonal peaks in August where people rush to go on vacation for the summer, and then it rapidly drops off from there. The growth seems very strong.


This is a tricky one- you both see that it’s flat, which is in line with what’s being discussed in the press. On the other hand, since it’s mostly mobile, it’s hard to say if this navigational query analysis is that useful. The silver lining on this also is that the traffic has held steady and hasn’t declined for a year now, which means there’s likely a strong core of retention holding everything together. Requires some more analysis to figure out what’s going on here.


Same kind of indicators as Foursquare. There was a huge run up in 2011/2012, due to a lot of Open Graph nonsense, but now that the traffic spigot has died down, there’s still a good base of activity. As I understand it, they’re over 100M uu/month and holding onto that traffic, which should be enough audience to build a solid company.



Alongside Facebook’s graph, both seem like they are plateau’ing and going flat. But could this just be users transitioning from web-centric usage to mobile? I guess we’ll know when their IPO S-1 documents come out- are they accelerating audience growth or is is starting to slow down?


Although Quora hasn’t been in the news much lately, and a lot of digerati seem to have abandoned ship, their core base of traffic looks pretty good. Growing slowly. Building high-quality content via SEO is really hard- but it looks like Quora is gradually succeeding. Also note the epic launch curve, and that years later the current demand graph is still 1/3 of the initial peak.


It’s fascinating to me that Yahoo was actually still growing strongly up until 2010 or so. Now it’s flat and maybe even declining a bit. Curious to see if these curves can actually reverse themselves- a more detailed analysis would probably pull up these curves for every single important property, from Mail to News to everything else, and see if any one property is dropping faster than the others.


Written by Andrew Chen

September 3rd, 2013 at 3:12 pm

Posted in Uncategorized

Books I’m reading (2013)

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A friend asked me what I was reading this year, so I wanted to share the sorta tech/nerd related ones at least, along with a quick blurb about what they are:

  • The Little Kingdom. Michael Moritz, in his previous job as a journalist, covers the early Apple years. Late on he wrote a followup, which I haven’t read yet. Great complement to all the contemporary Steve Jobs adoration, since it’s written from the perspective of the early days.
  • Expert Political Judgement. UPenn professors analyze why people are so bad at predicting all sorts of things in geopolitics, whether it’s elections or which dictators get deposed. Talks about two styles of analysis- hedgehogs which have “a big idea” and start their analysis with that versus foxes that try to analyze lots of data.
  • Predictable Revenue. Ex-Salesforce sales head breaks down how they sold to B2B. Lots of great details on how to organize sales teams, generate leads, incentive compensation, etc.
  • Engineers of Victory. Detailed dives into specific WW2 engineering problems: Defeating the UBoats, resisting the blitzkrieg, etc. Talks about how the engineers played a role in winning the war.
  • The Better Angels of our Nature. Amazing book by Steven Pinker, which I originally found via this glowing review by Bill Gates. He calls it one of the most important books he’s ever read. Pinker tells a compelling story, via graphs, anecdotes, and academic studies, about how violence has fallen over the last several thousand years.
  • The Signal and the Noise. One of my favorite books I read this year, by Nate Silver. Talks through how people go about modeling different things, whether it’s elections, gambling or weather. Lots of important points made about model errors and how people suck at predicting.
  • Sports Gene. After reading Malcolm Gladwell’s Outliers, this book is a great followup that talks more about the “nature” part of the nature/nurture debate. Talks about Jamaican sprinters, Kenyan runners, high jumpers, and the variance in the 10,000 hour “rule.”
  • Antifragile. Loved the first 1/3 and last 1/3 of this book. Taleb talks about the idea of antifragility, where things benefit from disorder. (Not just robustness, which resists disorder). He starts with the idea from a financial concept, but cleverly applies it to his own personal health and weightlifting routine. Could probably be shorter and less boastful though.
  • Your First 1000 Copies. Short and sweet book on how to build a mailing list to launch a book. A friend sent it to me after he started noodling on writing a book. I found some of the mailing list ideas helpful for this blog.

So there you have it! If you have more recommendations for what to read this year or next, shoot me a tweet at @andrewchen.

Written by Andrew Chen

August 26th, 2013 at 11:23 am

Posted in Uncategorized

Constrained media: How disappearing photos, 6 second videos, and 140 characters are conquering the world

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Constrained Media. It’s an innovative category of products that ask invite users to create content on a platform, but with arbitrary constraints- Twitter’s 140 character is perhaps the most famous example.

There’s now been a whole series of these apps, quite successful ones, such as:

  • write in 140 characters or less (Twitter)
  • compose a 6 second video (Vine)
  • upload maximum 400×300 thumbnails (Dribbble)
  • view a photo in 3 seconds before it disappears (Snapchat)

Why would it make a product more successful by forcing constraints on content creation? Isn’t more always better? Wouldn’t each of these products be better off by removing the constraints? And does every constraint work, or is it all arbitrary?

I’ll argue that the constraints are a fundamental part of what makes the products work. The higher engagement in constrained media products is based on their ability to break through the 1% barrier for content creation. This 1% rule is the famous rule of thumb for user generated content services like Wikipedia or YouTube that says 1% of your users will create content, 9% will edit and curate, and 90% will just sit back and view.

Of course, having only 1% of your users actively creating content sucks. So let’s talk about how to fix that.

Frictionless content creation
The obvious thing is that constrained media apps make it easy to create content. Anyone can type in 140 characters, take a photo, or hit a button to compose 6 second of looping video. Constrast this to a big blank textarea like traditional blogging or a sophisticated photo tool like Photoshop, which requires much more creative energy to use.

More interesting is how these constraints impact the simplicity of the product UI. These constraints mean that the product can support a smaller number of use cases, making it more toy-like, and easy to use. Often, you can power the entire interaction with one button, like Snapchat or Vine. Just hit a button to create content, and once you hit the limit, it’s all over- no worries about editing and rearranging the content.

Both the simplicity of the content, as well as the product UI, makes the whole experience much more directed and higher conversion.

Communication rather than publishing
Building on easy content creation, the next step is shift the context to communication, rather than publishing, which encourages a higher level of participation. The 1% rule sounds good on paper, but think about it in the context of communication products. What’s the content creation % for email, IM, Skype, or texting? I’m sure it’s a lot higher than 1%, perhaps even close to 100%. The point of communication is that all parties involved create content that’s directed at other people, and everyone participates.

Twitter has @mentions, Dribbble has rebounds, and Snapchat is all about communication. This invites people to participate, because the media can be directed at other people, and there’s a built-in context to talk to one another. This leads to email notifications based on healthy user-to-user engagement. This drives frequency, virality, and all sorts of other good stuff.

Replying is easier than creating
Creating content from scratch is hard. Similarly, being the first to communicate can be hard- anyone who’s introduced themselves to a stranger knows the feeling. However, replying is easy. If someone takes a picture of themselves making a funny face on Snapchat, then a natural response is to make a funny face back. Even more if you know that the picture was sent specifically to you, then you feel like you owe a response.

If anything, this increases the constraints- you have the constraint of knowing who you should reply to, and also the constraint of the kind of content that was sent to you. And surprisingly, these constraints make it easier to come up with something to send back.

Make casual content OK by reducing the variance in effort
Nobody likes a showoff. And in fact, a platform with too many showoffs lead to funny social norms, where people tend not to participate because they don’t want to compete with those who are more skilled or who have more time.

Instead, constrained content creation reduces the variance in output between the low-skilled and high-skilled users, which makes it so that everyone can have fun. The best analogy for this might be something like kickball versus professional baseball, where the former is more about everyone having participate by “dumbing down” the sport, not winning. Dribbble is a community of designers where posting your in-progress work in 400×300 “shots” is part of the norm- meaning more frequency and engagement. Constrast this to portfolio sites that you update once a year at most.

Discoverability of content is an important factor too. If you make it too easy to find the more effortful or highest skill content, this creates a kind of leaderboard that discourages content creation, although the content consumption experience might be improved. It’s a tradeoff. Snapchat’s private, ephemeral context means that it’s the only place where it’s safe to post crappy selfies of yourself.

What do you do with all that extra engagement?
All of the above translates to more frequent, more inclusive content creation. This powers traction. More frequency of use means there’s more opportunities to take users through viral loops, as well as firing organic user-to-user notifications that power retention. It becomes easy, for instance in Snapchat’s case, to ask the user to include a couple extra recipients of a photo after you’ve replied. Or after you’ve created a 6 second video, it’s easy to ask the user to share it onto a couple different social networks.

So the next time you’re designing a new social product, consider adding a constraint, but not any arbitrary one. Make it one that makes content creation easy, more communication-oriented, and produces the social norms you want. That’s the best way to beat the 1%.

Written by Andrew Chen

August 5th, 2013 at 11:28 am

Posted in Uncategorized

The highest ROI way to increase signups: Make a minimal homepage (Guest Post)

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Mattan Griffel has written some great essays on user growth over at Growhack, and you can  follow him on Twitter at @mattangriffel. In particular I’m fond of his essay The Minimal Homepage, which states something that everyone who’s A/B tested their homepage knows: Keep it simple, and ask for your signup upfront. It’s one of the easiest and highest ROI ways to increase signups, because your visitors won’t find their way onto low conversion pages and bounce. Surprisingly, it’s still counterintuitive to many. I’ve referred people to this blog post before, and Mattan graciously offered for it to be reposted here. Enjoy! -Andrew

Mattan Griffel:
The Minimal Homepage

What do you notice about the homepages of the fastest growing companies in the world?

Dropbox_Homepage-1024x715 Quora_Homepage-1024x715 LinkedIn_Homepage-1024x715 Groupon_Homepage-1024x714 Pinterest_Homepage-1024x715

Here’s what I’ve noticed:

  • No access without signup. Most startups make the mistake of giving people who visit their site free access to content, whether it’s apartment booking or daily deals. This is often a bad idea. Contrary to popular belief, the more things a visitor can interact with on your site before they’re prompted to sign up, the lower your signup rate will be.
  • Navigation and hyperlinks are almost always absent. Over the years internet marketers have developed what they call the “Squeeze Page” with minimal content and a single clear call-to-action because they discovered that additional information could distract a visitor or cause them to click away to a different website. Notice that there’s nothing below the fold on any of these sites.
  • Focus on a single, clear value proposition. In almost every case, the product’s value proposition is boiled down to one clear statement: “Your best source for knowledge” or “Be great at what you do”. People almost never read more than one sentence on your site (and they won’t even read that one unless it’s big enough and strategically placed), so there’s no point in trying to figure out your top 3 “bulletpoints”. This also makes it much, much easier to test as a growth hacker. Just replace one sentence with another until it works.
  • Your product is not about sharing. I see this mistake all the time. Lots of startups start out thinking that people will use their product because it helps them “share” things more easily. Let me be clear here: most people do not share. And even those people who share things aren’t sharing things 90% of the time. Most of the time on the web is spend consuming, not producing. More than 50% of Twitter users almost never tweet. This is why Twitter has shifted their messaging from “the easiest way to share with your friends’ to “Find out what’s happening, right now, with the people and organizations you care about”. If you cater only to proactive people, you’ll be alienating most of your potential users.
  • Big images. Big images increase conversion rates. Just do it.
  • Embedded signup forms. Start your signup process on the homepage so people don’t have to click through to a new page for no reason. Generally speaking, the more clicks you have in your signup process, the more people will drop off along the way. Note that these signup forms are almost always on the right-hand side, above the fold. They also rarely ask for more than a name, email and password.

When I tell people these things they often complain: “But everyone knows Twitter and Facebook, so they don’t have to explain what their product is about. No one has ever heard of [my startup] so I actually need to explain it to people.”

You are wrong.

Maybe you and I already know what Twitter and Facebook are about, but we’re not the people they’re trying to get to sign up on their homepage. 2.4 billion people use the internet and more using it each day. Believe it or not, there are still people on earth who haven’t heard of Twitter or Facebook. Those are the people these homepages are trying to convert – not the luddites who refuse to sign up (trust me, Twitter and Facebook stopped caring about them long ago).

The same is true for your startup. Don’t be stubborn and don’t think that for some reason your startup is an exception. Making that kind of assumption because you’re scared to try something counter-intuitive is a sure way to make sure you never do anything innovative.

[UPDATE: I read a great comment on Facebook and wanted to share it below. -andrew]
Emmett Shear makes a great point in a comment on this essay, included below:

I dislike essays like http://andrewchen.co/2013/07/29/the-highest-roi-way-to-increase-signups-make-a-minimal-homepage-guest-post/ because while part of his point is valid (look at all these companies who have decided to gate things behind “signup first” and have very simple front pages!) there are tons of counter examples. Just look at the Alexa top 10.

#3 YouTube — putting a giant “sign up first” wall in front of YouTube probably would have killed them.
#6 Amazon — Amazon is all about converting people into accounts AFTER they decide to buy, and you better believe they’ve a/b tested it.
#7 Wikipedia — Primarily a read-first experience
#8 QQ – Holy crap that is a lot of text

Now Google/Facebook/Baidu certainly follow the “simple homepage” design. But he’s overgeneralizing terribly and shows no indication he’s aware of it. The point of thinking about design is to be aware of tradeoffs, not to push the latest trend as “the smart way to do it”.

Said another way, increasing signups isn’t necessarily important for every company, and many successful companies don’t focus on it. So I would restate to “here’s how to increase signups” idea with “here’s how to increase signups once you’ve decided that signups are important to increase.” Great point Emmett!

Written by Andrew Chen

July 29th, 2013 at 11:07 am

Posted in Uncategorized

9 ways a billion dollar new mobile company might be created (Guest Post)

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My good friend Bubba Murarka recently started blogging over at bubba.vc. He’s now a Managing Director at DFJ and tweeting at @bubbam. Prior to DFJ, he headed up Facebook’s Android efforts, and is an expert on all things social and mobile. He wrote the blog post below on his blog, which I’ve cross-posted here. -Andrew

Bubba Murarka on Mobile:

We’ve been in “New Mobile” – a world of wireless broadband and mobile OS platforms enabling great end user experiences – for about 5 years. The improvement in the capabilities of devices has been astonishing. But in truth we are still in the first inning of New Mobile reshaping just about everything we do and everywhere we do it.

Since leaving Facebook, I’ve been asked more and more for my perspective on mobile ecosystem. Here are my current observations on why New Mobile is still in the earliest stages:

  1. The move from feature phones – mobile phones without robust browsers or a compelling application ecosystem – to always-connected touchscreen computers in our pockets still has a long way to go. Smartphones are barely the majority of total mobile phone sales in the U.S., let alone globally.
  2. The industry talks about smartphones and tablets as both being “mobile” devices instead of seeing them as two very different beasts. This is starting to change and I’m excited to see the wave of companies that are “tablet first” – but please don’t let that become a mindless mantra!
  3. It’s no longer about iOS vs. Android. Now the hard question is whichAndroid versions (Gingerbread vs. Jelly Bean) and flavors (e.g. Samsung, Amazon, etc.) you are targeting and why. Said another way, Android fragmentation, and dominance, has just begun.
  4. Completing transactions on mobile is still a big hassle (except for M-Pesa). App store and carrier billing fees are too expensive to be an option for anything other than high-margin digital goods. Whoever cracks this in a way that any 3rd party app can use is going to be very rich.
  5. Content creation on mobile devices is horrible. Much of the content we consume on mobile today requires the capabilities of a PC to produce, including the keyboard, mouse and purpose-built apps. Products likePaper and Vine have shown that there is considerable demand for creation via the touchscreen.
  6. True mobile multitasking hasn’t been invented yet. Smartphone screens are smaller and better suited to handle one app at a time with abstracted file access. But we’re used to working with multiple windows and applications our computers with a global file system. When will a new UX model emerge, especially on tablets, to enable multitasking?
  7. There’s no “mobile native” ad unit to allow publishers to monetize their audiences and thus focus on building richer and more engaging experiences. Instead, startups have to spend a ton of time on business model innovation, which is another really hard problem to tackle. My money is on Facebook cracking this nut (full disclosure: I am still heavy on the stock, so my money is literally on them) though I think Yahoo could be a surprise contender.
  8. Only two types of paid subscription services have gained traction on smartphones: Content licensing such as Rdio and Pandora One, and storage such as Evernote. What else are users willing to pay a subscription for on their smartphones?
  9. There have been some billion dollar exits like Instagram and Waze, but we haven’t had a stand-alone, New Mobile company go from garage to an enduring multibillion-dollar independent company in the Americas or Europe yet (it has happened in China though).

There is a lot to be unpacked and everything above is up for debate as we refine our collective thinking through discussion. The only thing I know for sure is that I’m excited to learn about, identify and nurture the best mobile-focused companies out there.


Written by Andrew Chen

July 17th, 2013 at 11:00 am

Posted in Uncategorized

Mobile traction is getting harder, not easier. Here’s why.

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The “classic” growth formula for mobile is broken
Once upon a time, the formula for getting mobile traction was something like this:

  1. Build something insanely great
  2. Get Apple/Google to feature you, alongside a big PR launch!
  3. Watch your app hit the charts
  4. Buy some cheap installs to propel it even further
  5. Voila, hockey stick! (and hopefully not a shark fin)

This worked for a few memorable years, and things were good- especially for new startups and indie developers. But gradually this classic formula stopped working, with nothing equivalent to replace it. Getting initial traction on mobile has gotten a lot harder, even though you’d expect a richer and bigger mobile ecosystem to have emerged to increase the opportunities to achieve mobile growth. (ps. if you’re interested in developing new approaches to mobile growth, just email me.)

It was only a matter of time. As I argue in my essay The Law of Shitty Clickthroughs, all marketing strategies eventually result in shitty results over time. In marketing, first-movers trump (at least initially) – if you do something new, then you’ll see high response rates as people respond to the novel tactic, whether it’s a new kind of creative, a new acquisition channel, etc. Eventually though, as your tactics become industry-wide “best practices,” the response rates fall as your customers get used to the techniques.

History has repeated itself again, within the channels that drive mobile traction. Let’s discuss how the ecosystem has matured, including factors like: Increased app store competition, Higher CPI rates, editorial dynamics, and the overall investment trend.

Product differentiation is harder with a much bigger app store
Let’s cover the most obvious thing first- the number of apps has gotten a whole lot bigger. Whereas before a new app might be competing against non-consumption, in all the major mobile categories there’s been a huge increase in the total number of apps. Those that were successful in 2009-2010 are now facing 4-8X the competition, if you look at just the aggregate numbers.

Whether you’re building an app for photos, shopping, messaging, local, movies, or news, there are now 2-3 very high-quality competitors in each category. A new mobile developer is no longer competing against the first wave of amateur-built apps. These days, it’s much harder.

Below is a recent chart that shows the incredible growth in # of apps:



Cost Per Installs have gone up over time
Initially, buying an app install was relatively cheap. You had a lot of options- everything from mobile ad networks, incentivized install providers, “free app a day” services, and even more adventurous options. More importantly, not a ton of companies were doing it, so prices were low.

This Cost Per Install has skyrocketed though, both due to demand and a lack of supply. After only a short time, the supply of paid installs has contracted as Apple has banned some providers and warned others. Similarly, mobile games figured out the enormous monetization potential on iOS and Android – they’ve bid up the installs significantly, up to a few bucks per install.

Here’s a chart showing the increase in CPIs over the first half of 2012, though anecdotally I hear it’s much higher than this now:


Editorial teams further the platform’s own strategic goals
The editorial teams inside the Apple and Google stores can certainly help some apps, and they do. Yet they are skewed more towards the needs of the consumer, and to the goals of the platform.

For Apple, my impression is that they care more that the first 25 apps that a user installs are amazing experiences from well-known brands, rather than servicing the needs of the overall million apps that in the store. As a consumer, I surely appreciate this, but it doesn’t help new unknown developers break into the market.

For Google, any team that’s met with them in the last few quarters can tell you that they care a lot about tablet devices. While they are winning market share on phones, the numbers for iPad versus Android tablets show a different story. If you want to be featured in Google Play, they strongly encourage you build a tablet app even if the market for it is tiny. They also care a lot about Google+, but that’s another story.

Investment has dried up for experimental new consumer mobile apps
While investors still have an optimistic outlook for the overall mobile market, there doesn’t seem to be a lot of conviction to deploy their capital on risky new consumer mobile startups. My sense is that there’s a feeling the ‘great consumer mobile experiment of 2009-2012’ has been run, where a ton of seed capital went into a wide range of mobile companies, and now the motivations have changed.

Just look at how the composition of YCombinator Demo Day companies has changed- in the late-2011 event I attended, it was >50% consumer mobile. Now it’s SaaS, consumer hardware, marketplaces, etc. Mobile is often an aspect, but no longer the main focus.

The silver lining
Despite the difficulties outlined above, I’m still wildly optimistic about the future of mobile. It’s still the best platform upon which to build a new company, but we must choose to embrace and work around the new difficulties we’re facing in 2013. It’s not enough to simply repeat what worked in the past- otherwise we’ll have a new generation of mobile companies that fail like it’s 1999, as I’ve written about.

While it’s getting harder, the opportunities within mobile are still the largest since the beginning of the computer industry. We’re barely over majority smartphones within the US, as Nielsen reported last month (June 2013). While it’s impressive that some apps have reached 100M+ installs, in an overall market of billions, we’re just getting started. We have a lot to look forward to over the next 10 years.

Written by Andrew Chen

July 9th, 2013 at 3:36 pm

Posted in Uncategorized

Why you can’t find a technical co-founder (Guest Post)

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This is a guest post by a friend of mine on email marketing. Elizabeth Yin is the CEO and a co-founder of LaunchBit, an ad network for email newsletters.  Previously, she worked at startups and Google, and went to MIT for her MBA, and Stanford before that. PS. I’m training growth hackers. Email me.


image credit: SilentMode

Hey, I’ve got this great idea for a startup…do you know any developers who might be interested in working with me?

I get asked this question a lot.  So, my co-founder Jennifer and I were curious and surveyed developers on what would compel them to team up with a non-technical co-founder.

The results were surprising.  This survey was not particularly scientific.  We received 104 submissions from developers, of which 35 were actively working on their own projects full-time and 69 were not.  We asked participants to rate how important a particular criteria was to them in deciding whether to join a non-technical person’s startup.  1 = Not important.  5 = very important.

Location is not a deal-breaker


I would’ve expected location to be a deal-breaker for just about everyone.  I would have expected all would-be technical founders to strongly prefer being in the same city as his/her non-technical counterpart.  But, only about half said location was a deal-breaker.

Idea validation is extremely important


In contrast, idea validation was extremely important to potential technical co-founders.  You, as a non-technical entrepreneur, are not selling a dream or the vision.  You are selling traction.  Some people who took our survey commented about their ideal proof of validation, “If they have $1M in sales and have shown that people are willing to buy this thing without it even existing.”  Another mentioned, “Validated early adopters/customers [are people] who said they’re going to pay for the product when their minimum viable product is out for their use.”

Prior relationship is not a deal-breaker


Also interestingly, I would’ve expected developers to overwhelmingly prefer to work with people they’ve already worked with or know from before.  And while, the data shows that many people would prefer to work with someone from their past, about 40% of technical folks don’t really care.

Pulling this all together, if you’re looking for a technical founder, the number one thing you should be doing is to get traction for your startup idea.  This means validating your idea, getting customers or users, and ideally getting revenue.

Getting traction without a product
So how do you get traction without a developer to build the product?  Very much in the spirit of the Lean Startup Methodology, there are a number of successful tech startups that got started without doing any programming.  Here are 3 companies that took off without writing any code in the beginning.

Fast-growing startup Yipit, a deals-aggregation company, got started in 2010 as a side project without any code.  The founders, Vin Vacanti and Jim Moran, wanted to just get Yipit out the door in a couple of days, so in the beginning they manually aggregated deals from major daily deal sites — Groupon, LivingSocial, et al — by hand.

Screen Shot 2013-06-17 at 2.30.03 PM

They put up a landing page to aggregate email addresses and collect category preferences.  Then, they manually categorized each of the deals they collected and emailed their subscribers based on indicated preferences.  In a the true spirit of hustling, they did not build a web crawler to aggregate the deals — they manually aggregated deals themselves at 3am everyday.  As Yipit started getting traction, it was getting more unwieldy to handle, but instead of hiring developers to build out web scrapers, they hired more people to continue manually aggregating, categorizing and emailing deals for 9 months, because the Yipit team wanted to continue to learn how to tweak the product quickly to make it better.  Since those early days, Yipit has since raised $7M+ in total.

Beat the GMAT
Beat the GMAT, a social networking site for prospective MBA students, started in 2005 as a side project — just as a blog.  The founder, Eric Bahn, used his blog to solve his own GMAT problems to help him practice for the exam.  His blog became so popular, readers started emailing him to ask for help on problems.  Although Eric would email people back, he was soon receiving 50+ emails per day from blog readers.  He realized he needed to scale himself.  So, he replaced his blog with forum software so that readers could help each other.

However, the number of visitors to his site was not large enough to make the forums particularly lively or helpful.  So, he continued personally answering a lot of people’s GMAT questions in the forums.  He took this a step further — he wanted to wow his visitors with quick responses, so he made sure each posted question in his forums received a response within 1 hour.  It turned out that a lot of prospective MBA students, however, lived in Asia, so he hired a contractor to call him whenever a prospective MBA student posted a question in his forums.  This often required Eric to jump out of bed in the middle of the night to answer forum questions.  But, a year into following this exhausting routine, Eric found that he had built up enough traffic in the forums, and other people were now responding to questions before he could even reach his computer.

Screen Shot 2013-06-20 at 7.50.50 PM

The MBA community started clamoring for more, so the Beat the GMAT team decided to transform their forums into a full-fledged social networking site.  Since the team wasn’t technical, they outsourced the development of their site to what it is today.  Beat the GMAT bootstrapped its way to $1M+ in annual revenue with just 4 full-time employees and was acquired by Hobson’s in 2012.

Founders Naval Ravikant and Babak Nivi had already been successful entrepreneurs by the time they started AngelList, a social networking site for angel investments.  They had the resources to build out a huge site for AngelList, and they initially did, but they quickly found they had overbuilt and did not have users.  So, they took a step back and dumped  everything.  They started again using a mailing list and Wufoo forms to hack together a community of entrepreneurs and angel investors.  They asked both sides to fill out forms with information about their companies and investments.  They manually brokered introductions between relevant entrepreneurs and investors.

Screen Shot 2013-06-20 at 7.52.00 PM

Only once they started getting interactions going did they decide to build out the product that we see as AngelList today.  “We always do it manually…until we know how it works and then we automate it,” explained Naval.  Today, over 1000 startups have been funded on AngelList, and the company is rumored to be raising a first round of funding at $150M valuation.

The truth is — traction matters.  And, if you’re a non-technical founder with just an idea, it’s probably tough to find a technical co-founder.  Having traction on that idea will make it a world easier to find technical talent.

P.S. But, I know that figuring out how to get traction isn’t easy.  So, I’m organizing a conference called Hustle Con (July 9 in Mountain View) to teach new entrepreneurs on how to get customers first.  We’ll be talking about topics such as how to go from 0 to $5M in revenue and how to build an audience before you have a product.  We have a great full line up of speakers including Scott Cook (founder of Intuit), Gagan Biyani (co-founder of Udemy), Jess Lee (CEO of Polyvore), and Arjun Arora (CEO of Retargeter) who will share how they acquired customers.  And, I’m giving away one free ticket on this blog.  All you have to do is tweet why you want a @hustlecon ticket before June 27th, and the best tweet will win.  For those who don’t win, get 25% off with this discount code: “andrew-hustler”

Written by Andrew Chen

June 25th, 2013 at 10:45 am

Posted in Uncategorized

How to grow your app revenue with DuPont analysis (Guest post)

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About the author: Kenton wrote this fantastic piece about analyzing in-app revenue, drawing from his work experience at both Zynga, where he runs their mobile poker product, and before that, Google. You can follow him at @kivestu and his blog here. -Andrew


When I worked at Google, Eric Schmidt used to say “Revenue solves all known problems.” He was right.

And if you’re monetizing a mobile app today, there is a good chance that in-app purchases (IAP) are a critical component of your monetization (if not the sole pillar).* Yet we don’t have great tools for understanding the mechanics of revenue models driven by IAP. Financial analysts who wrestle with similar problems can shed some light.

Financial analysts often use a technique called DuPont Analysis – named after the famous chemical company that created it – to understand what components of a business are driving financial returns.

The DuPont Analysis equation looks like this:


This equation states that if you take a company’s profit margin, asset turnover and financial leverage, multiply them together, you’ll get Return on Equity (ROE) – a measure of how much profit a company generates per the amount invested into the company. It’s an insightful way to quickly understand what is the driving force behind returns (and also known to be one of Buffet’s favorite metrics).

The key insight from DuPont analysis is the principle of decomposing a common metric into the components that drive it.

To successfully monetize via IAP you need a deeper understanding of revenue drivers – top line revenue or even revenue / daily user (aka ARPU) is not enough. A more sophisticated understanding starts with the Transactions Payers Revenue (TPR**) equation:


Rev/DAU (aka ARPU) is a quick measure of how much revenue you’ll make for each user you have on a given day – it’s an overall indicator of your ability to monetize your users.

Payers/DAU measures how many of your users on any given day actually pay – meaning on that particular day X people actually transacted within your app.

Rev/txn measures how much each transaction was worth – this is particularly important for developers that have a large range of price points available in their app (as many games do, for example). Note: If you only sell a single item this metric will be a constant equal to the price of that item.

Txns/payer measures the number of transaction you got for each payer you had in the app in any given day (eg how many transactions did the avg. payer complete.)

Let’s run through a mechanical example. Let’s say two different mobile apps have a $0.10 rev/DAU. On the surface, it might seem like these apps are similar:


But if you dig a little deeper and collect the other key metrics we’ll need for TPR analysis, differences will manifest themselves:


And if you run the calculations, your picture now looks like this:


So what?

The chart above is critical to understand if you’re focusing on improving monetization because it tells you where your leverage points are. For example, if you’re the CEO of product B and you tell your VC that the critical way you’re going to grow revenue is by converting more payers, your VC ought to call BS. Why? Because 5% of daily users paying is already pretty damn good. You might be able to get 5.1% or even 5.5%, but that won’t move the bottom line much. A 10% improvement on an already stellar number, would equate to an extra $10 / day (10% improvement on payers means 5 incremental payers, each doing 5 txns / day, giving you $10+ bucks a day).

However, if you instead focused on increasing the revenue per transaction, you might find there is significant upside. After all, people shell out in excess of $0.99 for a single Coke in most places around the world, so it seems like your revenue per transaction has head room to grow. Maybe by highlighting volume discounts (or some other product tweak) you could get revenue per transaction up to $0.60. It’s not quite a Coke yet, but hey, its an improvement. That would be pretty great though – and it’d net you a $50 bottom line improvement, not bad!

What next?

The TPR equation, while helpful, is just the first step. Any thorough understanding of IAP revenue will require peeling back another layer of the data. For example, is payers / DAU being driven by active payers or new payer conversion? Or what about lapsed payers returning to the app? What about “red herrings”? In some cases, an increasing rev/DAU metric might actually point to long term problems acquiring and monetizing new payer (this can happen when new DAU starts declining, new payer conversion dips and rev/DAU looks healthy because committed, elder users of the app are pushing it up). More on these more nuanced layers in a followup post.

*The most recent Distimo data suggests that revenue from IAP no accounts for 70%+ of app store total revenues, up from ~50% in Jan. 2012.
**TPR stands for Transactions Payers Revenue. I didn’t want to be narcissistic and name if after myself and TPR sounds official, akin to those TPS reports (albeit hopefully more valuable).

Written by Andrew Chen

June 17th, 2013 at 11:00 am

Posted in Uncategorized

New college grads: Don’t sell your time for a living

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If there’s one thing I could tell every graduating student, this is what I’d say:

Jobs suck. At least the traditional version of a job, in which you do something you sorta hate, from 9-5p, and are paid for your time to just grit your teeth and do it. Let’s call this the “sell your time” version of a personal business model: You sell your time to an employer, and they pay you for that time.

Turns out this personal business model sucks.

Even The Onion agrees, in their article Find The Thing You’re Most Passionate About, Then Do It On Nights And Weekends For The Rest Of Your Life.

There’s so much conflict stemming from the fact that this is the predominant mode of work in our society. All the hand-wringing about work/life balance, finding what you love, kids versus work, etc. – an important source of these anxieties come from the fact that a “sell your time” model of work means you’ve set your personal time (and goals) in direct conflict with the time you have to sell for work.

Stop selling your time
There’s a better way – though it might not be the easiest way. The key is to find a way to stop selling your time, and to find another business model instead. And the important aspect of this personal business model is that you’ll be able to make money even if you are sleeping.

1) Learn to make something. Anything.
First and foremost, I think it’s important to learn to make something. Anything. It could be an app, blog, table, YouTube channel, video tutorial, or anything else. Then study the people who have become successful enough to support themselves in this craft, and study them, copy them, stalk them, and meet them.

It always shocks me when people don’t really know how to make anything. Or haven’t ever tried. It’s something we’ve all done as kids – drawings, crafts, etc. – but somehow a very large number of professional workers find themselves in a state where they only know how to repackage other peoples’ work rather than doing anything themselves. Weird.

2) Create a feedback loop with your audience/customers
Remember that the end goal isn’t to make art, it’s to get out of selling your time for a living. So even while you’re learning to make stuff, you’ll want to learn how to make stuff that people actually want. This means you need to create a feedback loop between you and your customers, whoever they may be. This means you’ll want to constantly show people your work, no matter how bad it is. You’ll want to try and build an audience, or a customer base. Again, this is a skill in itself and may take years to figure out.

It’ll also be an opportunity to find small wins in what you do- whether that’s improvements in craftsmanship, or from finding an audience for your work. This kind of positive feedback will keep you going.

3) It’ll take years to become competent
It’s been discussed endlessly in books like Malcolm Gladwell’s Outliers, but it takes years of solid practice to be any good at anything. And then 10,000 hours (roughly 10 years) to become a world-class expert.

But even before you sink years into something, you’ll get frustrated much earlier on because you’ll think that you suck at it. There’s a great quote from Ira Glass (of This American Life) about the difficulty of getting good at anything, starting as a beginner:

“What nobody tells people who are beginners — and I really wish someone had told this to me . . . is that all of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, and it’s just not that good. It’s trying to be good, it has potential, but it’s not.

But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. A lot of people never get past this phase. They quit. Most people I know who do interesting, creative work went through years of this. We know our work doesn’t have this special thing that we want it to have. We all go through this. And if you are just starting out or you are still in this phase, you gotta know it’s normal and the most important thing you can do is do a lot of work. Put yourself on a deadline so that every week you will finish one story.

It is only by going through a volume of work that you will close that gap, and your work will be as good as your ambitions. And I took longer to figure out how to do this than anyone I’ve ever met. It’s gonna take awhile. It’s normal to take awhile. You’ve just gotta fight your way through.”

The period where your taste outpaces your ability to produce it is a hard one. You know your goals but don’t quite know how to fulfill them. That’s why it’s easier to be a film critic rather than a film director :)

The point of all of this isn’t to do it alone. In fact, you’ll find that it’s rare you can do something substantial by yourself. Instead, the above feedback loop most usually involves teams of people, at least once the basic groundwork has been done.

Technology startups are a perfect example of this- it exemplifies the process of getting a bunch of smart people together to learn and make something valuable for the world. It’s a remarkable thing to get the experience of creating something from scratch, and seeing it through to its success in the market. But even if startups aren’t right for you, and  you choose to write books for a living, in a success case you’ll work with editors, research assistants, other writers, etc.

On a final note, if this essay reminds you that your job sucks, just watch this clip from Office Space where they beat up a fax machine.

PS. I’m training Growth Hackers. Email me.

Written by Andrew Chen

June 3rd, 2013 at 11:00 am

Posted in Uncategorized

Does your product suck? Stop adding new features and “zoom in” instead

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[Adapted from an answer I wrote on Quora, and thought I’d share it on my blog too.]

Adding a lot more features won’t save your product
Everyone’s worked on a product it’s failing despite a ton of work behind it. It’s not for lack of great ideas, or a lack of bright minds working long and hard on the product. In the startup world, often this comes because after a new product is launched, there’s a Trough of Sorrow where features are often added to try to spark traction. After a few months of this, and a few shifts in direction, it’s easy to get a Frankenstein product that tries to do too much.

At this point, adding new features won’t help– what’s broken is at the core of your product, not out on the edges. Adding more to edges won’t do anything, because most of your users aren’t even getting there.

Eric Ries has a wonderful term for what to do here, which is to consider a “zoom in pivot.” He talks about it in his book Lean Startup, as a kind of pivot you can do if your product isn’t gaining traction.

The idea of the zoom in pivot is:

A single feature in a product becomes the whole product, highlighting the value of “focus” and “minimum viable product,” delivered quickly and efficiently.

The question is, how do you pick the feature you’re going to zoom into? And how do you validate that it can work as a standalone product? And how do you execute the pivot itself and what metrics can you look at?

Picking the new product
The actual process of picking the new product is the same as picking any new product for a startup. Ultimately it still has to go after a huge market, it has to be differentiated against competitors, and have a distribution model. You have to be passionate about it. Etc, etc. All the standard strategy issues apply, and I’ll leave this as an exercise to the reader.

In terms of tactics though, the big thing from a metrics standpoint is to try and figure out what’s actually getting enough usage to actually execute the “zoom in” pivot. After all, if you zoom into a smaller featureset that isn’t being used currently, that’s obviously much risker than noticing that out of 10 features, 1 or 2 are getting all the usage, so then you dump everything else.

Based on developing a product strategy, and looking at current usage metrics, you can develop a hypothesis for what a smaller product might look like. You can also create some goals you want to hit as far as the metrics are concerned- obviously the usage of the zoomed in feature should be much higher, but by how much? And the usage of the secondary features should become zero or minimal- are you OK with that? The next step is to test it.

Iteration and testing
It should be easy to test a “zoom in” pivot- just default the navigation and the description of the product to focus on what you’re zooming into. You can even test a few ideas simultaneously if you want to.

Here are a few high-impact places to test:

  • Changing all the landing page where new/unregistered users arrive to reflect the new positioning
  • Taking users directly into the functionality after they sign in or sign up, so that you are defaulting to that usage
  • Using modal lightboxes or other highly prominent UI to channel users into the zoomed in featureset
  • At the end of the typical workflow of the user, to take them to the feature again

The above suggestions focus on making the zoomed in feature more prominent, but you can also make the other features more secondary. You can do the following:

  • Burying other features into submenus like “Extras” or “Goodies”
  • Removing other features from global navigation UI
  • Rewriting headlines to de-emphasize unneeded features, or removing text about them from landing pages, bulleted lists, etc.

The combination of all of the above – either by making the main feature more prominent, or the burying the secondary features – should help the goal. You can A/B test these, primarily focusing on new users, to see what the effect looks like.

From a metrics standpoint, I think as a baseline you’d want the zoomed in feature to increase significantly in usage, and for the secondary features to go to zero or nearly so. You also want to make sure some of the aggregate stats around frequency of use, time on site, content shared, etc. to be stable depending on what you care about.

Choosing a feature
After this iteration process, picking the zoomed in feature should be easy. You may have to go through an A/B testing process to smooth the transition from the old featureset to the minimalist one, but over some period of time you should be able to make the metrics move in the direction you want.

If it turns out the metrics are stubborn and some important metrics go down, then that’s much more problematic. It might turn out that the zoomed in feature you picked is somehow not right enough. Or maybe the userbase you’ve amassed isn’t right for the pivot. Or maybe you need to develop the featureset a bit more, in the direction you’ve pivoted, to get to the right product.

For all of these, the Plan B might be to either accept the new featureset and deal with the reduced numbers, hoping to fix them later. Or alternatively, the Plan B might be to pick a new featureset or continue iterating on the zoomed in featureset, until it works. That’s all gray area.

Written by Andrew Chen

May 16th, 2013 at 11:32 am

Posted in Uncategorized

Linkedin, Facebook, Google, Twitter, eBay, YouTube, Wikipedia, Amazon, Hotmail, Blogger, Apple: How they used to look

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This is an oldie but a goodie I had to share again- here are some screenshots of early web products, some close to their inception, some a couple years later.

If you have some other screenshots of early products, be sure to tweet me at @andrewchen and I’ll try to put them up.


Written by Andrew Chen

May 13th, 2013 at 10:00 am

Posted in Uncategorized

The critical metrics for each stage of your SaaS business (Guest post by Lars Lofgren of KISSmetrics)

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[My friend Lars is a product marketer at KISSmetrics and loves helping SaaS businesses understand how their business is growing. He writes regularly for the KISSmetrics blog and his personal marketing blog. He wrote the following post about SaaS products and the metrics you use to evaluate their success level. Lots of great information in there. You can follow Lars at @LarsLofgren -Andrew]


How healthy is your SaaS business?

We’re bombarded with KPIs and an endless series of metrics to tell us how we’re doing.

But instead of using data to measure our progress, it’s much more likely that we get lost and start focusing on metrics that are easy to track but don’t mean anything.

For a SaaS business, there are a few core metrics that need your undivided attention. And the priority of these metrics shift as you grow. If you’ve only had paying customers for 2 months, it doesn’t make much sense to track lifetime value. But later on, lifetime value is essential.

In this post, I’m going to break down the essential metrics for each stage of a SaaS business.

What this framework will give you:

  • By focusing on a few key metrics, you’ll also be focusing on the core problems you need to solve to get your business to the next level.

  • Data doesn’t do you any good unless you act on it. Each of these metrics clearly tells you how you’re doing. Right away, you’ll know where you need to spend your time.

  • Each stage has two metrics that balance each other. This keeps you from over-optimizing one metric and unintentionally harming the long-term health of your business.

Let’s jump in.

Before Product/Market Fit
You’ve just made the decision to start your business and you’ve got plans for world domination.

But before you can start building your empire, you need to make sure you have the right product for the right market.

For most new products, there’s usually a disconnect at the beginning and customers don’t quite want what you have. Either you need to go after a different target market or you need to change your product to fit their needs. When you get this match, we call it product/market fit.

You’re probably in this stage if:

  • You’ve just started.

  • You don’t know who your ideal customer is.

  • People are testing your product for the first time.

This is the first major hurdle you’ll need to overcome. But how do we measure our progress when we don’t have any data? You don’t even have any paying customers at this point and if you do, it’s not many. At this point, running a bunch of A/B tests won’t help you test your business model.

Instead, you’ll rely heavily on qualitative feedback and one critical survey question.

Primary Goals:

  • Validate core business assumptions by talking to people in your target market. If people ask you for your product before you even try to sell them, you’re going in the right direction.

  • Survey users and have at least 40% say that they‘d be very disappointed if they had to stop using your product.

Metric #1: Qualitative Feedback
Yes… this isn’t technically a metric. But it’s too early for data anyway so you’ll need to make the most of what you can get: feedback.

Right now, you really only have one goal: build the right product for the right market. And the fastest way to do this is to start talking to your customers.

If you have any users at this point, jump on Skype and get a deep understanding of their main problems. Ask them to show you how they currently solve the problem you’re going after. Then show them what you’ve been working on to see if they get excited about it. Usually, you’ll want to follow this format for the interview:

  1. Basic demographic questions to get a better sense for who you’re talking to.

  2. Deep questions about the current problem.

  3. Present your solution for feedback (don’t sell it, just get feedback).

You’ll want to do 10-20 of these customer interviews.

If you don’t have any users at this point, go and talk to people that you think would want to use your product. This is a great way to start testing different target markets efficiently. It’s a lot easier to schedule 10 more Skype meetings than it is to rebuild or rebrand your product.

When you want to start scaling feedback (especially as you move into the later stages of your business), use Qualaroo surveys, SurveyMonkey, feedback forms, and usability tests like UserTesting.com. But when you’re just starting, talk to people in your target market one-on-one. The insights will always be much better.

At KISSmetrics, we still do customer interviews each and every time we make a major change to our product. Adding a new feature? Go talk to customers. Revamping an old feature? Let’s talk to our customers that use it the most. Starting a new project like our Google Analytics app? Find a group of Google Analytics users to talk to. We do it every single time.

Metric #2: Measuring Product/Market Fit
There’s just one little problem with all this customer feedback though.

It’s super difficult to measure objectively. Are people REALLY interested in our product or are we only focusing on the positive feedback while downplaying the negative feedback?

Luckily, there is a survey question that will help you quantify whether or not you’ve reached product/market fit. Full credit goes to Sean Ellis for this question. Here it is:

How would you feel if you could no longer use [product]?

  1. Very disappointed

  2. Somewhat disappointed

  3. Not disappointed (it isn’t really that useful)

  4. N/A – I no longer use [product]

Send this to people that have used your product at least twice, experienced your core product offering, and used it in the last two weeks. The goal is to get at least 40% of your users to say “very disappointed.”

If you don’t meet the 40% benchmark, you may need to reposition your product or pivot entirely. If you do hit it, time to move on to the next stage.

More Resources
To dive into more detail on what you’ll need to make it through this stage, read through these posts:

Beginning to Scale
So you’ve found product/market fit.

You’ve got revenue coming in and a growing customer base. Now it’s time to build a business.

Up until this point, you didn’t really need to track much. Outside of basic user signups and revenue, there wasn’t anything to track. Now that you got the right product for the right market, there are two metrics that will keep you headed in the right direction.

You’re probably in this stage if:

  • You’ve found at least one way to acquire customers consistently.

  • Many of your customers stay subscribed and want to keep paying you.

  • Your monthly revenue is starting to grow.

Primary Goals:

  • Consistently grow MRR while controlling churn.

  • Get monthly churn to 1-2%. If it’s above 5%, ignore everything else until you lower it.

Metric #1: Monthly Recurring Revenue (MRR)
For a SaaS business, monthly recurring revenue is a much more valuable metric to track than traditional revenue. It’s the total revenue you received during the month that came from recurring subscriptions.

The health of a SaaS business heavily depends on recurring revenue. It can take months to regain the cost of acquiring a customer and the real profits come from increasing that subscription revenue. One-time windfalls just aren’t that valuable to us. By tracking monthly recurring revenue, we can see exactly how our business is doing month-to-month.

Unfortunately, tracking MRR can get tricky. There’s several use-cases that your tracking systems will need to be able to handle:

  • Having annual plans on top of your regular monthly plans complicates things a bit. The annual revenue actually needs to get divided between each month of the subscription, not just the month when the customer is billed.

  • Upgrades and downgrades get tedious to track. If a customer moves from a $10/month plan to a $50/month plan, you’ll need to add an extra $40/month to your MRR.

  • You’ll need to remove revenue when it churns with a cancellation.

Speaking of churn…

Metric #2: Churn
Growing MRR is one side of the coin at this stage. The other side is churn. If you can’t keep customers subscribed, it won’t be long before your MRR won’t budge and your business will stall.

The thing is, churn can be a devious metric. At the beginning, a monthly churn rate of 10% doesn’t seem so bad. If you have 100 customers, 10 of them left. Not that big a deal right? It’s pretty easy to get 10 more customers. But what happens when you have 10,000 customers? Now 1,000 of them left in a single month. Even the best marketing machines have a hard time keeping up with something like that.

Your churn rate starts out innocent and easy to handle. But it can quickly get out of control if you’re not keeping a close eye on it. In order to build a strong foundation that will help your company grow over the long-term, you absolutely, without a doubt, NEED to get control of your churn rate.

So what’s a good churn?

It always varies by industry. But in general, it’s critical that you get your monthly churn under 5% and your goal should be 1-2%. Later on, you can experiment with upsells and cross-sells to get negative churn.

Sooner or later, you’re going to hit a wall.

The main channel you’ve been using to acquire customers will start to slow down and you’ll hit diminishing returns. If you want to keep growing each month, you’ll need to find new sources of growth.

You might start testing affiliate programs, new ad networks, PR, business development, referral programs, new types of content marketing, conferences, event marketing, or whatever type of marketing happens to be hot at the moment. You’ve got LOTS of options to choose from. Some of them will be a great fit for your market, others will fail completely.

You’re probably in this stage if:

  • Growth is beginning to slow for the first time.

  • Continuing to improve your main channel is getting a lot harder.

  • You’ve successfully controlled your churn.

So as you start to experiment with new channels of growth, you need to focus heavily on two metrics. These metrics will keep your experiments in check and make sure you scale profitable channels.

Primary Goals:

  • Keep your cost per acquisition to one third of your lifetime value.

  • Get each customer to profitability within 12 months.

Metric #1: Lifetime Value (LTV)
How much revenue do you earn in total from a customer before they leave your business? For a SaaS business, it’s absolutely critical to track lifetime value. When you factor in acquisition, support, and product costs, it can take a SaaS businesses 6-12 months to turn a profit on a customer.

To make sure customers stay long enough to keep your business healthy, we use lifetime value (some people abbreviate it as CLV or LCV).

By now, you’ll have had customers long enough so that you can actually figure out your LTV. Use the formula here to get started. When you have more resources, you might also want to include second-order revenue in your LTV calculation.

Metric #2: Cost Per Acquisition
As we begin to experiment with new channels to keep growing, cost per acquisition keeps us in check. It’s the total cost it takes to acquire a customer from a particular source.

For the average CPA of your business, you can total up your entire marketing and sales expenses over a month then average that out over the total customers you acquired. But we need to take it a step further and segment CPA by acquisition channel. This tells us whether or not customers from new channels are worth the effort.

When you’re experimenting with new channels, it’ll usually be pretty obvious if the math won’t work out. Bad channels tend to be BAD channels. So keep experimenting until you find the ones that work.

Not only will CPA help you evaluate new channels for growth, it’ll help you figure out how far to push your main channels. How much can you actually spend to acquire a customer on AdWords or Facebook? How many writers can you hire to put together content? By keeping an eye on these metrics, you’ll know how far is too far.

A popular rule of thumb is to keep CPA to one third of your LTV. And a customer should become profitable within 12 months.

More Resources
Once you get past product/market fit, use these posts to help you work through all the details:

A Quick Overview
For each stage of your SaaS business, track these metrics:

  1. Before Product/Market Fit: Customer feedback and the product/market fit question

  2. Beginning to Scale: Monthly recurring revenue and churn

  3. Expansion: Lifetime value and cost per acquisition

Keep in mind that each stage is not completely exclusive. Let’s say that I’ve found product/market fit and I’m starting to scale. If I’m using AdWords to acquire my customers, I’ll definitely want to keep an eye on my cost per acquisition. But at this point, I’m still trying to get a handle on my churn for the first time. I don’t REALLY know how long these customers are going to stick around. So I’ll check my CPA to make sure it’s somewhat reasonable (if the total revenue from a 12-month subscriber doesn’t cover it, you have a problem). Otherwise, I’ll spend most of my time improving MRR and churn.

What about funnels? What about engagement metrics, ARPU, active users, number of visits to signup, and everything else? By all means, track the other metrics you need. But the above metrics are the bare minimum. Move mountains to track them before worrying about the rest. There’s little reason to track a random engagement metric if you don’t know what your MRR or churn is.

What have I missed? I’d love to know how you track your own SaaS business.

Written by Andrew Chen

May 6th, 2013 at 11:30 am

Posted in Uncategorized

The death of RSS in a single graph

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Google Trend graph for “rss” – bad news.

I recently wrote a blog post about moving all my RSS readers to email subscriptions, and I immediately got 30+ negative comments on it. Obviously it struck a cord. I still believe what I said, and here’s some more data and reasoning to back it up:

RSS has been dying for years
First off, the image above is the Google Trends search on “rss” over the last few years. That tells you how many people are searching for RSS on Google. To me, that’s the best indication that as a consumer-facing technology, there’s been waning interest for years. Does any blog want to bet on that as a long-term trend? Combine that with the imminent shutdown of Google Reader, and you can guess that a lot of folks using RSS readers will move to non-consumption rather than switching to an alternative. Yes, there will always be a vocal minority that loves feed readers, ultimately RSS will be more like QR codes or Segways than a mainstream technology.

Ultimately, my bet is that RSS will stick around but more as a way for content services to talk to each other – you’ll see random blogs appear in places like Flipboard or Zite automatically – but the idea that people will see the little orange RSS button and click on it is a lost cause. (Oh, and searches for “google reader” don’t fare well either)

RSS doesn’t have a reply function
Interactivity between a writer and their audience is is one of the most rewarding aspects of maintaining a blog. RSS was meant to be a different way to present content, and doesn’t have identity or interactivity baked in. One of the best aspects of email subscriptions (and Twitter) is that you can actually see who’s taken interest in your work. You can even reach out to them and start a friendly conversation. Some of the most important relationships in my career have been made over email and Twitter.

As I switch over to emphasize email, my hope is that I can increase the level of interactivity with my audience. The way its set up now, if you hit reply to any email post, write a quick note, it’ll go directly into my inbox unfiltered. And better yet, we might even have an intelligent conversation!

Moving off RSS will lead to better content
Feedback loops let you iterate on what kinds of content resonate with your audience. Writers need feedback loops to improve their writing – everytime a new essay is emailed to my readers, I get a ton of feedback. I know exactly who and how many folks have unsubscribed. I can reply to ask them why, by writing an email. I also know how many new people have subscribed, and often look at their email domains to figure out if they’re a corporate, a startup, a VC, etc. This kind of detail helps me write better content and get to know my audience. All good stuff. And obviously RSS is just about content, and doesn’t have this kind of feedback built in.

Consumers are moving to “integrated” readers
Related to the negative trend in RSS interest, consumers have adopting other platforms instead. RSS readers were invented in a different era. Blogger, TypePad, and WordPress were created in an era where we thought of blog networks as a bunch of standalone websites, decentralized, like the internet. But it turns out that’s not as easy to use as it could be. Turns out consumers love it when they can follow, view feeds, and create content, all on the same site. This is the core of the feed-oriented homepages of  Twitter, Instagram, or Tumblr – the integrated reader has won out.

Email subscribers are 2x more active than RSS readers
The other thing I’ve noticed is that email subscribers are just stickier and more active. From my own personal data from my blog, I know that although I theoretically have 5x more RSS subscribers than email, from a traffic standpoint, the mass of RSS subscribers don’t make up for their numbers. On a per-email subscriber basis, I get about 2x the activity rate from people clicking links from RSS as compared to email.

So when it comes to the very practical question: When a blog is designed to prompt users to subscribe for future content, what should you push for? RSS or email? The answer is easy, go with email. In otherwards, in order for the numbers to work out, I’d need an RSS prompt to convert at 2x as email to get the same activity level. Given that the market size and interest in RSS is decreasing over time, and a small vocal minority uses an RSS reader, I think it’s pretty obvious where you want to go there.

Until RSS is redesigned (ha!), I repeat: RSS, I quit you. And if you have a blog, you should be thinking about this too.

Written by Andrew Chen

April 29th, 2013 at 9:45 am

Posted in Uncategorized

Featured essays from 2011-2013: Facebook, Growth Hacking, Mobile, and more.

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I’ve recently tried to recommit myself to blogging :) and as part of that, I pulled together my recent set of essays and redid the Featured Essays section of this blog. If you missed anything, check them out below- they are a collection of what I’ve written over the last 18 months or so. In the coming months, I hope to continue writing more about mobile, especially the nascent field of mobile marketing. Thanks for reading.

Oh, and if you are reading this from an RSS feed, please subscribe to email instead. I explain why here: RSS I quit you.


Product/Market Fit



Industry and Investing

Written by Andrew Chen

April 23rd, 2013 at 10:00 am

Posted in Uncategorized

Why developers are leaving the Facebook platform

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Attitudes towards the Facebook platform have changed
Recently, Bill Gurley of Benchmark wrote a great piece on how platform companies like Facebook, iOS, Android, eBay, and others manage the ecosystem around them. It’s an important essay and I’d recommend you all read it. I found myself nodding my head as Facebook was discussed. In recent conversations with fellow entrepreneurs in Silicon Valley, it’s become a common belief that Facebook has become an undesirable platform for a startup to build their company.

Last month, I even heard one prominent VC even went so far as to say:

If your audience comes primarily from Facebook, that’s just uninvestable.


That’s a big shift from just 3-4 years ago when everyone was building Facebook apps and deeply integrating it into their products. I remember visiting a floor of an incubator where the head guy proudly said, “Everyone on this floor is working on Facebook apps.” And everyone thought that there was going to be a new thing, the “social OS” that was going to be the next layer of the internet.

So what happened? Why have developers soured on the Facebook platform?

Multiple factors in this analysis
The summary of the reasons why developers have increasingly left the Facebook platform for other platforms:

  • Lack of virality
  • Higher ad rates
  • Constant retooling
  • Competition
  • The feed is finite
  • Mobile platforms are the new sexy opportunities

This essay tries to elaborate on each of these reasons. Perhaps this will be educational for future platforms in how they work with developers, and hopefully Facebook will ultimately come to fix these issues. I don’t agree with all of these opinions, but in the spirit of comprehensiveness I’m going to document all the POVs I’ve heard.

Lack of virality
When the Facebook Platform first launched, it was the Wild West. You could do almost anything. I remember hearing that a lot iLike’s growth at the launch of the Facebook platform was because they figured out you could set up an invite screen with all your friends’ names pre-checked, and people would just click OK. It’d invite all of their friends, and the apps grew very fast. Turns out that sucks for UX, and it makes total sense for Facebook to turn that off, even if developers would rather have it there. Same with Zynga, and same with Viddy.

But now that those channels have all been dialed down, mostly for very legitimate reasons, it’s hard for even app that’s a “good actor” in the ecosystem to achieve sustainable viral growth. Many of the channels that existed last year no longer exist today, and they were taken out without replacements. So now that the excitement has faded, we’re back to launching mobile apps on Techcrunch and hoping to ride the iOS charts- that still seems to work for some people, and developers have started focusing there.

Higher ad rates
One way to view acquisition on Facebook (and Google, for that matter) is that there’s a organic marketing channel (via feeds and search results, respectively) and a paid channel, that blends paid content into the organic stuff. Back a few years ago, there was a ton of undervalued ad inventory on Facebook and a lot of companies went nuts on both the organic and paid channels. This was because Facebook took the long view in building up their ad infrastructure, and let people bid it up over time rather than sticking AdSense on all their pages. Facebook does a trillion pageviews a month, so it turns out there was a lot of cheap ad inventory. A lot of developers and advertisers were able to buy a ton of traffic cheaply, and arbitrage it against their virtual goods or ecommerce businesses.

That arbitrage began to fail as ad rates went up. And with decreased virality, the effective cost per customer also went up, because you were getting fewer “free” users as well. So now in 2013, that arbitrage is a lot harder to do profitably. In many ways, you can look at Zynga and Groupon as very successful one-time arbitrages on Facebook’s 1 trillion pageviews/month. They were able to buy 100M+ customers a few years back, but now that new user acquisition is much harder, they have to look elsewhere.

Constant retooling
I’ve heard the joke that the “Developer Love” email is scariest email you can get from Facebook, because it’s the one that tells you that your app needs to be substantially updated for a new set of APIs. Facebook has an amazing engineering culture driven by “Move fast and break things” but that means some of those things are often their developer partners’ apps. And you need to move as fast as Facebook to keep up. Just look at the Developer Changes page to see how often new things are released.

Part of this retooling means that there’s a maintenance tax on whatever app has been created on the platform, since you have to pull your prized engineers off their projects to do constant maintenance and reintegration into the new viral channels. That’s just to keep up. It also means that what works today may not work tomorrow. If you are making important decisions on staffing, business models, financing, then a lot of uncertainty is introduced because your business might get disrupted by platform changes happening in a few months.

It also turns out that at least for some categories of services, Facebook actually thinks about the competitive aspects of their product and it’s not just a completely open platform. If you talk with folks who are working on messaging or photos or even walkie-talkie apps, you’ll hear stories about how apps have been shut down. Turns out, especially because so many folks are working on mobile these days, that a lot of overlap gets created. I’ve even heard that Facebook isn’t letting some messaging apps buy advertising on their platform – not just turning off the APIs, but actually refusing to accept money for ads. Pretty interesting stuff.

The feed is finite
Many of the distribution issues on Facebook have to do with the fact that the feed is finite. A person will only look at the first 10 or 20 stories on any given visit, and anything you put into that grouping takes something out. This leads to all sorts of problems, because as users spend more time with Facebook, all sorts of new activity increases:

  • They “like” more pages
  • They add more friends
  • They “subscribe” to more celebrities
  • They try more apps
  • They sign into more apps with Facebook

All of this means that there’s more potential things their newsfeed algorithm needs to sort out. Not only are there more actions people are taking, but there’s more advertisers buying “likes” and app installs. You end up competing with everyone else for a spot on the feed, and it’s a zero-sum game, as Michael Dearing pointed out to me on Twitter. All of this leads to the marketing channel getting saturated, which I’ve written about in my essay Law of Shitty Clickthroughs, and makes the channel less attractive as time goes on.

Mobile platforms are the new sexy opportunities
And finally, the very obvious thing is that developer attention has shifted over to mobile because that’s where the new successes live now. You might have read, for example, of Supercell’s recent $130M raise valuing the company at $770M. When’s the last time we heard about that for a Facebook app? And how many investors are willing to fund “Facebook apps” now? In my conversations with people, there’s still a lot of perceived opportunity in mobile, and people feel like there’s enough stability.

What’s next for the Facebook Platform?
The Facebook Platform has been an amazing success, in a lot of ways. No other company, with maybe the exception of Google, has given away so much free traffic to developers while asking for very little in return. So let’s not all be whiners here. Years after the platform launch, a lot has evolved, and as a community we’ve all learned a lot. One of those lessons: What makes developers happy and what makes for a great UX are very different things. Same with what makes Facebook a good business, rather than a platform for developers to suck out users.

Can Facebook regain the excitement around the platform that they had years ago? I think the answer is yes, but I think they have to figure out what kinds of apps they want build up on their platform, and really make those partners successful. Show us the existence proof that you can build something big and sustainable on there. Microsoft was an incredible platform because it spawned multiple public companies that built upon them – regardless of the fact they’d chase you down once you proved there was a billion dollar opportunity :) I think if the developer and startup community starts hearing about big successes on Facebook again, people will try it out. But in the meantime, the attention has shifted to where big opportunities are now, and that’s iOS and Android.

Written by Andrew Chen

April 22nd, 2013 at 9:30 am

Posted in Uncategorized

RSS, I quit you. Please subscribe to email updates for this blog instead.

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The short version:
As of today, I’ve removed the links the RSS feeds on this blog, and ultimately will phase them out completely in favor of email. If you want to stay up to date, please switch to an email subscription instead- I usually don’t write more than once a week, sometimes once a month.

You can sign up here.

The long version:
Imagine a world where Google Reader and Feedburner are both shut down – that future is half true already. One clear outcome is that some of my favorite blogs – infrequent, high quality ones – end up getting a lot less traffic. They update infrequently, because they are run by individuals or companies who are really busy :) That’s where RSS subscriptions are really valuable. And their titles aren’t linkbait, because they’re not crazy focused on driving traffic.

Contrast that to blogs that publish a lot like Business Insider or Techcrunch. I think they’ll end up reaping the rewards of a world without RSS. And aggregators like Flipboard, Techmeme, or Hacker News will become even more important. These apps and blogs are now part of your daily habit, in a way where the infrequent/boutique blogs will never be.

Ultimately, there’s a hole in the market that needs to be filled. In the meantime, I can see a lot of blogs switching to email subscriptions and more aggressively submitting their content to aggregators or Twitter. I have 10,000s of subscribers on my RSS feed right now, and I wish I had gotten them all on email instead. Whoops. Rather than waiting for Feedburner to get shut down, I’m going to make the move to email instead. Today’s removal of RSS links is the first step towards that.

Written by Andrew Chen

April 15th, 2013 at 10:39 am

Posted in Uncategorized

How this blog grows: Evergreen content, Social whales, and “Don’t get bored”

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Screen Shot 2013-04-11 at 10.13.05 AM
Above: My twitter followers graph for the last 2 years – it slowly grows, mostly from cross-sell from my blog to Twitter. People find it via SEO, then click the Follow button

Cold start sucks
Everyone who has tried to start a blog knows that the cold start problem is no fun. I spent about a year writing to an audience of about 10 people, including my sister and a few coworkers and friends. I inherently enjoy writing, so that was fine by me, but this phase often discourages people to write at all.

I’ve been writing this blog since 2007 and over time, have tried lots of little experiments on trying to grow the audience. I’ve built a modest sized audience with 50k+ followers/subscribers across RSS, email subscribers, and Twitter. Over the last year, I’ve stuck with one basic formula which has helped a lot, and I want to share it with you- here’s the components:

  1. Evergreen content
  2. Social whales
  3. Don’t get bored

Let’s talk about each one.

Evergreen content follows a Power Law curve
First off, it starts with the content. Just like anything else, there’s a Power Law curve, and a small number of my posts end up generating a very long tail of traffic over months and years. These are my “evergreen” pieces of content which creates a solid base of traffic for the blog even when I’m not particularly active with my blog. They often have a spreadsheet or presentation or some other kind of “asset” that makes it a useful post. Or it’ll define a commonly used piece of jargon that gets Googled, often as “how do I calculate X” for instance. Another strategy is to try a small tweet, and if if people seem to like it, I’ll turn it into a blog post (full discussion on that strategy here). And it may surprise you to know that the title of the blog post matters as much as the actual content of the post. That’s why the tweet-the-title-then-write-it strategy works so well.

The above strategy works because if you can only write every once in a while, you’re probably not going to be breaking news like the pro journalists. So instead you’ll have to differentiate on expertise and insight, rather than trying to tag along on whatever cool topic we are talking about these days. Drones. Bitcoin. Snapchat. Google Glass.

Viral spread of content on social platforms also follows a Power Law curve
The second thing, kind of obvious, is to share your content out to the various platforms after you write it. The less obvious thing is that you are better off “betting the farm” on one platform – say Twitter or Facebook or Linkedin – rather than trying to include links for all 3 and more. I focus on Twitter, and put a big follow button on the bottom of every one of my posts. Focus really helps because first off, the Power Law will show up again and you’ll find all your traffic comes from 1-2 sources anyway. And if you build up an audience and a consistent set of tools and techniques to spread your content on that platform, you’re better off.

Furthermore, even from an individual source of traffic, the distribution of followers on these social platforms also follows Power Law. Thus, it’s really important to have the “social whales” publish your content to their audience- that matters a lot. For me, the difference between a successful post (hitting 10,000s of people) or an unsuccessful one is often a few retweets from folks like Eric Ries, Hiten Shah, Dan Martell, and others. And often these kind of digital relationships are really built on real-life relationships, which is kind of ironic. As much as the world has become global, it’s still important to build real, authentic relationships with people in your field, and that can help with how many Twitter RTs you get.

And finally, don’t get bored
The hardest thing about maintaining a blog is that it’s hard to have something interesting to say every day. It takes years to build up a base of content, get inbound links for SEO, and create real-life relationships with folks in your industry. So rather than optimizing for posts that get traffic, ultimately I think you have to pick topics that you want to write about on a weekly basis and keep going.

How it all fits together
OK, so here’s the summary, in even more colloquial terms:

  • Write evergreen content that people want to read now, but possibly a year from now (breaking “news” sucks, leave that for the pros)
  • Push all of your content onto social platforms, and get people to retweet it
  • This generates SEO, which brings in more people, which brings in more followers
  • Rinse and repeat, and don’t get bored

Ultimately, there’s a loop in there that drives the accumulation of traffic, but the cornerstone to all of this is content that people want to read.

Written by Andrew Chen

April 11th, 2013 at 10:15 am

Posted in Uncategorized

Why are we so bad at predicting startup success?

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Startups and bad predictions
One of my favorite reads this year was Nate Silver’s The Signal and the Noise which has the subtitle “Why so many predictions fail, but some don’t.” It covers a ton of different topics, from weather to politics to gambling, and I couldn’t help but read it with a startup/tech point of view.

After all, the industry of technology startups is all about prediction- we try to predict what will be a good market, what will be a good product, as we “iterate” and “pivot” on our predictions. And of course the business of venture capital is even more directly about knowing how to pick winners- especially the seed and Series A investments.

And yet, we’re all so bad at predicting what will work and what won’t. I’ve written about my embarrassing skepticism about Facebook, but hey, I’m just a random tech guy. For the folks whose job it is to professionally pick winners, the venture capitalists, they aren’t doing very well either. It’s been widely noted that the venture capital asset class, after fees, has lagged the public markets- you’d be better off buying some index funds.

Startup exceptionalism = sparse data sets = shitty prediction models
One of the most challenging aspects of predicting the next breakout startup is that there’s so few of them. It’s been widely discussed that 10-15 startups a year generate 97% of the returns in tech, and each one seems like a crazy exception. And as an industry we get myopically focused on each one of them.

Watch Ben Horowitz elaborate on the sobering stats, starting at the 38:00 minute mark:

With these kinds of odds, our brains go crazy with pattern-matching. When a once-in-a-generation startup like Google comes around, for the next few years after that, we all ask, “OK, but do you have any PhDs on the team? What’s the ‘PageRank’ of your product?” And now that we have AirBnb, we’ve gone from being skeptical of designer-led companies to being huge fans of them. With so few datapoints, the prediction models we generate as a community aren’t great- they’re simplistic and are amplified with the swirl of attention-grabbing headlines and soundbites.

These simplistic models result in generic startup advice. As I wrote about earlier, there’s a whole ecosystem of vendors, press, consultants, and advisors who go on advice autopilot and give the same advice regardless of situation. Invest in great UX, charge users right away, iterate quickly, measure everything, launch earlier, work long hours, raise more money, raise less money – all of these ideas are helpful to complete newbies but dangerous when applied recklessly to every situation.

We all know how to parrot this common wisdom, but how do we know when we’re hearing good versus bad advice? If you think about the idea that there’s 10-15 companies every year who are breakouts, how many people really have first-hand experience making the right decisions to start and build breakout companies?

Hedgehogs and pundits
I was reminded for my dislike of generic startup advice when in his book, Nate Silver writes about hedgehogs versus foxes and their approaches towards generating predictions – here’s the Wikipedia definition on the concept:

[There are] two categories: hedgehogs, who view the world through the lens of a single defining idea and foxes who draw on a wide variety of experiences and for whom the world cannot be boiled down to a single idea.

Silver clearly identifies as a fox, and contrasted his approach to the talking head pundits that dominate political talk shows on TV and radio. For the pundits, the more aggressive, contrarian, and certain they seem, the more attention-grabbing they are. Rather similar to what we see in the blogosphere, where people are rewarded for writing headlines like “10 reasons why [hot company] will be killed by [new product].” Or “Every startup should care about [metric X]” or whatever.

This hedgehog-like behavior is amplified by the fact that there’s always pressure to articulate a thesis on what’s going on in the market. People in the press are always trying to spot trends or boil down complex ideas, and investors are constantly asked, “What kinds of startups are you investing in? Why?” And entrepreneurs are always forced to fit their businesses into the broader trends of the market, to find sexy competitors, all in the change to find a simple narrative that describes what’s going on.

The solution to all of this isn’t easy- to be a fox means to draw from a much broader set of data, to look at the problem from multiple perspectives, and to reach a conclusion that combines all of those datapoints. There’s been some great work on the science of forecasting by Philip Tetlock of UPenn, who’s set up an open contest to study good forecasting here. There’s an interview of him Edge.org here and a video describing some of his academic research below:

Worth watching.

My personal experience  
Over my 5 years in Silicon Valley, the biggest lesson I’ve learned from trying to predict startups is calibration. They talk about it in the video above, but the short way to describe it is to be careful with what you think you know versus what you don’t. I’ve found that my area of expertise where I can make good decisions is actually pretty narrow- I’ve done a bunch of work in online ads, analytics, consumer communication/publishing, and I think my judgement is pretty good there, but it’s much shakier outside of that area.

When I do an analysis, I try to match my delivery with how much I think I know- and these days, it means that they sound a lot more tentative than the younger, brasher version of myself when I first came to SF. I’ve also tried to be diligent in my employment of “advice autopilot” – if I meet with entrepreneurs and find myself saying the same thing multiple times, then I try to refine the idea to take into account the specifics and nuances of that product. It’s easier, lazier, but less helpful to just say the same thing over and over again.

Be the fox, not the hedgehog.

Written by Andrew Chen

April 8th, 2013 at 10:45 am

Posted in Uncategorized

My Quora answer to: How do you find insights like Facebook’s “7 friends in 10 days” to grow your product faster?

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I recently answered a question on Quora and am sharing it on my blog:

How do you find insights like Facebook’s “7 friends in 10 days” to grow your product faster?

Here’s my thoughts below:

Why make a rule like this?
It’s important to remember the goal of making a pithy goal like “7 friends in 10 days” – it’s to help your team drive towards a clear objective. I’m sure “10 friends in 12 days” works well too, as does “5 friends in 1 day” but you just pick something that makes sense and easily memorable.

Anyway, here’s some thoughts about how to make something useful:

Defining the success metric
First, you need a way to evaluate how “successful” a user is, based on their behaviors. You might define this based on something like:

  • days they were active in the last 28 days
  • revenue from purchases in the last 28 days
  • content uploaded in the last 28 days
  • … or whatever else you want to define.

How do you figure out the right evaluation function? You just have to pick one, based on what makes sense for your business. There’s no one-size-fits-all answer here- you need to tailor this based on what makes your product work. In Facebook and Twitter’s cases, since they are ad-based models, they care a lot about frequency and engagement.

Exploring the data
Once you have a way to evaluate the success of a user, then you want to grab a cohort of users (let’s say everyone who’s joined in the last X days) and start creating rows of data for that user. Include the success metric, but also include a bunch of other stats you are tracking- maybe how many friends they have, how much content they’ve created, whether they’ve downloaded the mobile app, maybe how many comments they’ve given, or received, or anything else.

Eventually you get a row like:
success metric, biz metric 1, biz metric 2, biz metric 3, etc…

Once you have a bunch of rows, you can run a couple correlations and just see which things tend to correlate with the success metric. And obviously the whole point of this is to formulate a hypothesis in your head about what drives the success metric. The famous idea here is that, fire engines correlate with house fires, but that doesn’t mean that fire engines CAUSE house fires.

Running the regression
In some cases, it might be obvious that a particular metric correlates more strongly with your success metric than anything else. That helps you along. But if you want to get more formal, then you can do the kind of regression that David Cook describes.

The usual problem I’ve seen for startups is that there’s often not enough data, and too many variables, to be able to generate a really strong statistically significant model. And you can’t really tell your growth team “OK guys, active days is driven by friends, posts, likes, and 20 other factors. Let’s increase them.” Not very inspiring. So instead you’re just looking for something simple that explains enough of variation in success to rally your team behind it.

Verifying your model
After you’ve found the model what works for you, then the next step is to try and A/B test it. Do something that prioritizes the input variable and increases it, possibly at the expense of something else. See if those users are more successful as a result. If you see a big difference in your success metric, then you’re on to something. If not, then maybe it’s not a very good model.

“Branding” your model
Finally, once you’ve explored the data, run some regressions, and verified that your model works- then you have to be able to explain it to other people. So make it dead simple to talk about, repeat it over and over, and generally simplify it to the point where a lot of your growth product roadmap is focused on moving the metric up.

Written by Andrew Chen

April 8th, 2013 at 9:20 am

Posted in Uncategorized

I got a startup pitch via Snapchat, here’s the story

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I’ve recently been asking my Twitter followers to add me on Snapchat, so I can build up a bigger addressbook there and have a more engaging experience. Even though my audience is skewed, it’s a way to attempt to break through into becoming an activated user. If you aren’t an activated users, social products can lack meaning, as I wrote about previously here.

To my surprise, after a few days, I got sent a URL to http://andrewmeetus.com, which turned out to be a new Polish team working on a local + social mobile app. Huge props for the cold snapchat pitch! I met them a few weeks later in Palo Alto, heard about their new product Nearbox, and congratulated them on their creative way to get my attention.

Last thing- feel free to add me on Snapchat, my username is andrewchen. Send me whatever!

Written by Andrew Chen

April 6th, 2013 at 4:52 pm

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Social products win with utility, not invites (Guest Post)

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[Note from Andrew: I’ve recently traded a series of interesting emails on the evolution of social products and how the things that worked years ago- importing addressbooks and blasting out invites, no longer work today. A friend of mine, Sangeet, wrote up a longer analysis on the topic and I wanted to share that with you today. Enjoy!]

About the author: Sangeet Paul Choudary analyzes business models for networked businesses at his blog Platform Thinking. He is based in Singapore, previously at Skillshare, Intuit, and Yahoo. Follow Sangeet on Twitter at @sanguit.

The proverbial chicken and egg problem of building a new social product is well understood among tech startups, and it’s been commonplace to follow two contrasting mechanisms for getting traction.

Traditionally, startups have solved this problem by racing to connect users with each other, essentially providing them the pipes to interact with each other. Twitter, Facebook and LinkedIn have grown big with this connection-first model.

However, a new breed of networks is gaining ground with the content-first model. They provide users with tools to create a corpus of content, and then enable conversations around that content. Behance, Pinterest, Instagram, Dribble, Scoop.It have all gained traction by building a corpus of content before building a social network.

The two contrasting approaches are summarized below:

The rules of building a social product are changing. It’s important to understand this shift to build social products that can effectively gain traction on the internet today.The connection-first model is no longer as effective as it used to be. As the social web grows, and a larger number of social products compete for our attention, we are seeing a dramatic shift towards the content-first model. If you’re still getting users to send out Facebook invites, you’re adding to the noise, instead of standing out and getting noticed.

The Connections-first Social Product

Traditionally, the playbook for building network effects has been the following: Get users on board, connect them to each other and have them create content and conversations.

Social networks like Bebo, Facebook and Twitter used this playbook to create their respective networks leveraging address-book integrations and other hacks to rapidly build a large number of network connections.

The importance of building connections, in this model, cannot be emphasized enough. In fact, the growth teams at Facebook, Twitter and LinkedIn specifically aim for ‘X connections for a user within Y days of sign-up’ to activate the user.

Since a critical mass of connections is required before users experience value, the key to building a successful network is minimizing the friction in creating connections. Contact-list integration helped social networks like Facebook and LinkedIn gain initial traction through the removal of sign-up friction.

In spite of growth hacks like contact-list integration, there is always a lead time in getting users on board and reaching critical mass. This is the ‘gap’ where it becomes very difficult to demonstrate value in using the product.

Frictionless sign-up + Virality = Network Effects? Or not!
Startups often believe that removing friction in sign-up and creating some form of viral acquisition are the two key elements to reaching critical mass. In fact, with the rise of Facebook Connect and the social graph, a large number of social products have sprung up on the promise of frictionless sign-up and viral growth. However, users on the internet have limited time and attention. As more startups leverage the social graph and flood users with invitations to join their networks, users have started to develop invite fatigue.

Clearly, frictionless sign-up and virality are not the one-stop solutions we were hoping they would be.

The secret to network value
Startups often fail to appreciate the gap between technology and value proposition. For products like Evernote, technology serves the entire value proposition. However, for social products, the value proposition is a combination of technology and the content that users create on top of it. YouTube’s value lies in its hosting and streaming capability, but more importantly in its vast repository of videos.

The secret to creating a social product that demonstrates immediate value is to enable content before creating the network.

Content created on the network is the new source of competitive advantage. The videos on YouTube, the pictures on Instagram, the answers on Quora are the primary source of value for users and the key driver of competitive advantage for these platforms.

The Content-first Social Product

Today’s social startups don’t start off as networks. They start off as standalone apps. These products enable users to create a corpus of content first. They then connect the users with each other as a consequence of sharing that content.

Instagram started out as a photo-taking tool and built itself out into a social network subsequently. The initial focus was entirely on the creation of content and the connections were formed over time leveraging other social networks. It is unlikely that Facebook would have considered Instagram a direct competitor in its early days, largely owing to its model of deferring network creation.

How to create a network in stealth mode
Instagram started off as a standalone tool. In doing so, the product provides ‘single-user’ utility to the user even when other users aren’t around on the network. There are two aspects to building single-user utility:

1. The single-user utility should allow creation of content that will ultimately form the core of the network. The core of Instagram is pictures. Discussions are centered around pictures. Hence, the single-user tool needs to allow creation of pictures. This is an extension of the OpenTable model, where a restaurant first manages its real-time seating inventory on a single-user tool, before that very inventory is exposed to consumers on a network, to allow them to reserve tables. Curation-as-creation products like ScoopIt and Storify also use this model to curate content which will serve as the core for network interactions.

2. The product should deliver greater value when users share their content with their friends. The product builds out the network at the backend as more content is shared. Hence, the social network gets created, effectively solving the chicken and egg problem. A new breed of curation-as-creation startups (Scoop.It, Paper.Li etc.) is gaining traction on a similar model.

The new playbook for creating social products is essentially the following:

  1. Have a vision for creating the network but do not start executing on network creation
  2. Enable a single-user tool that creates content that is core to social interactions
  3. Share this content on external networks (social networks, email, blogosphere)
  4. Capture interactions around the content to build network linkages at the backend
  5. Open out the network once a critical mass of linkages have been built

The rise of the content portfolio
Instagram demonstrates how a network is created around a portfolio of user-generated content. Behance and Dribbble have followed similar strategies by providing a portfolio for hosting designs, before adding value through the creation of a peer-review community. Initially, Pinterest appealed to the designer community as a tool to ‘bookmark’ their favorite designs, before it built out the network. Early adopters found enough value in the ability to store designs and pictures, to use the product before the network became active.

The new success factors
Frictionless sign-up and virality are important but they are no longer the key to building social products. The following are key to building content-first social products:

  1. Removal of barriers to the creation of content: Startups like Instagram, which succeeded in simplifying the creation process and in enabling users to spread the word, succeeded in eventually building the connections between users.
  2. Growing the creator base, not just the user base: Since value for the overall networks is scaled by scaling content creation, the platform needs to focus on incentivizing and increasing the percentage of users who create content.
  3. Strong curation models: Content-first social products scale well only when there is a strong curation model in place to separate the signal from the noise. Without strong curation, greater content can actually lead to a poorer user experience leading to reverse network effects.
  4. Incentives: The platform needs to encourage users to build out the connections. This works best when the platform encourages an innate motivation (self-expression or self-promotion) in the user to spread the word about her content. In doing so, the users build the necessary connections that set up the network.

The new growth hacks
In the connections-first model, the one hack that minimized friction in building connections was the contact list integration. In the content-first model, the hack that minimizes friction in creating content is the creation widget. Creation widgets have grown in popularity in recent times, spreading across the internet in the form of browser add-ons and one-click buttons. Several curation-as-creation startups like Pinterest and Scoop.it have used widgets to enable users to create content easily.

The future
This new model of building networks allows a social product to gain traction while value is being created by users. Once enough content is created, the users are connected and the network builds out. Social products that win will focus on enabling users to create content first and generate conversations around it. The creation of the actual social network will be a final step, as a consequence.

Written by Andrew Chen

March 25th, 2013 at 12:22 pm

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Minimize your Time to Product/Market Fit

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Startups need to get to “Product/Market Fit” or die trying. Most die trying. Steve Blank talks about the idea that tech companies die because of lack of customers, not inability to build technology. Marc Andreessen says it’s the only thing that matters. Basically you want to get to the point where your product is working, and if you can’t get there within the first 1-2 years of your company’s existence, you generally run out of money or your team falls apart.

So let’s define a new term: TTPMF – the “Time to Product/Market Fit.” You want to get TTPMF down to the point where you can achieve it, scale up the business enough on traction to either reach profitability or to raise your next round. If your plan for TTPMF exceeds your funding runway, you’re already dead.

Luckily, it turns out that getting a low TTPMF is very easy: Just completely copy something that’s already at P/M fit. (Sometimes this is easier said than done, especially if the incumbent has network effects) But with so many startups that fail because of lack of P/M fit, you’d think it’d be obvious- it’s easy right? Now, if you find that yucky or undesirable, I’m with you. It turns out that although there’s an advantage in reducing TTPMF, cloning products has a lot of business (and ethical, and personal) weaknesses.

Let’s discuss those weaknesses.

Long-term Strategic Value
If you make it an explicit goal of reducing TTPMF, you might think that cloning is great- but be aware that a 100% clone has many weaknesses:

  • It’s uninspired
  • You’ll never get to #1 since you can’t switch existing customers over
  • You’ll never grow the market in a new direction, giving you a different base of users- compared to an incumbent competitor
  • You let a competitor define the market, and you play catch up- you can never play offense
  • If it’s a networks-effects business, you can’t just clone a product, you have to clone a community. That’s hard.
  • .. among many other issues.

Thus, I don’t think you ever want to do a full clone.

Instead, you want to keep the fundamentals the same (80%) while substantially reinventing 20% of the product. That addresses the issues above. There’s a lot of stock methods of reinventing the 20%- you can do this in the cheaper/better/faster variety, or to go to a niche, or to go with some other segmentation. (Again, refer to Steve Blank’s blog for more details on this).

Each of these approaches allows you to create product differentiation which lets you either suck in a different set of users than the incumbent competitor. It lets you head in a different direction so that you can provide a better product for some %, and define that part of the market on your terms. Long-term this provides a more sustainable foundation for the company so you compete more effectively against others in the market.

Thus, don’t just clone, though I think most people make the opposite mistake by trying to invent too much.

How do you balance the two?
So between the two, you can guess how I land on balancing the opposing forces of TTPMF versus Long-term Strategic Value: More than anything, I believe in reducing TTPMF.

In most circumstances, I don’t even think entrepreneurs really have a choice. TTPMF has to be less than 1-2 years or else your startup will implode. Ask anyone who’s been working on a product for more than 2 years and doesn’t have traction to show: It really, really sucks. The first 6 months can be fun because it feels like you’re painting on a blank canvas, but soon enough, there’s just fatigue and the window of opportunity shifts. Platforms change, investors get disengaged, your employees start getting excited about other companies. So if you miss your window, then you’ll run out of money or energy or both.

And perhaps this is unfairly treated as a either/or decision, because in reality it’s not. You can get both a low TTPMF as well as a ton of strategic differentiation in the market, and I wouldn’t settle on anything but an idea that has both baked in.

Isn’t 20% too incremental?
The other important objection is, doesn’t just lead to more incremental companies? Ideally, no. The goal is- Pick the right 20% :)

Ideally the differentiation is baked deeply into the core of the product, not out on the edges. Something the end user can see and feel within the first 30 seconds of using the product. So even if you see that all social networking products are public and anonymous, then you go with something private with real names. But you still have profiles, friend connections, and the other things that people would recognize as a social network product. With Twitter, you might argue that a lot of features were already well understood within a blogging product: the stream of posts, being able to subscribe to others, customizable profiles, etc. But the 20% that could be different was the 140 characters.

Where I agree with you is that if the product is basically completely the same, but the 20% is out on secondary/tertiary features that aren’t used much, that’s probably a recipe for a commodity product.

The reality in the 2013 fundraising market
Given the Series A crunch on everyone’s mind, let’s put a quantitative range on what TTPMF has to be to successfully raise an A.

If you’re a consumer product company with the following characteristics:

  • $40k/month in burn from a team of 4 FTEs
  • $1M raise, so ~2yrs of runway
  • 6 months to raise the Series A, so really 18 months of operating time
  • Target 1 million installs before raising the A
  • 3 months to build version 1.0 and release it

If you believe the numbers above, then how much time do you really have for TTPMF?

First, the optimistic case:
TTPMF = instant. This means that you have 3 months of development to release the v1, and you instantly have great engagement. Then you have 15 months to work on growth, getting it eventually average 2,000 signups/day, to hit 1 million installs to get ready for your Series A. Not bad, and sound doable if you have a low TTPMF.

But what if you have to pivot once or twice? And then you’re 12 months in? Well, turns out you’re not left much time to work on marketing.

TTPMF = 12 months This means you’ll have 3 months to release the v1, then 12 months of iterations. At this point, you’ll have 9 months left before your Series A raise, and you’ll need to scramble on marketing to get to 11,000 signups/day to reach 1 million.

That’s scary stuff, and doesn’t leave you much time to focus on your Facebook integrations, optimizing your signup flows, etc. Believe me, getting user engagement is hard enough, but when you couple it with a high bar on user growth, it’s 10x harder. So leave enough time to work on your marketing optimizations to get your product going.

And then by the end of it, you’re in a death spiral like what I previously described in Mobile App Startups are Failing Like It’s 1999.

Get that extra time by leading with the problem of TTPMF, but don’t forget to keep the big picture in mind also.

Written by Andrew Chen

March 11th, 2013 at 9:45 am

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I’m a Google Glass skeptic and think it’ll be the next Apple Newton

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Turns out nerds are crazy about Google Glass
Recently I tweeted the following:

i’m a google glass skeptic. Who’s with me?

Turns out most people think Google Glass is going to be awesome. Frankly, I was surprised- I figured it would be more balanced. But it turns out that people are more excited about the idea of Glass than any particular use case. And I’m excited about the product category too, but think the v1 might suck.

Google Glass is the new Apple Newton
One day wearable computing glasses may turn out awesome, but I’m convinced that the Google Glass will be like the Apple Newton- a visionary product well ahead of its time, and maybe 10 years after its release, someone will figure out how to make it mainstream using a different design. Regardless of whether the v1 is good, all the investment in wearable computing is certainly exciting. What nerd doesn’t want to fulfill their dream of being a cyborg? And within a few iterations, it may be that the industry will come out with a v5 that is awesome, just the same way that the iPhone/iPad eventually fulfilled the dream of the Newton. Let’s hope that becomes the case, but in the meantime, I’m not optimistic about the v1 of Google Glass.

Is it better than smartphones?
My skepticism is rooted in one idea: For $1,500 (or $1k or even $500), the Google Glass will have to do certain tasks significantly better than the smartphone justify the price. And in the next 2 years, it may have to compete with many other devices like wearable watches that fulfill some of the same tasks too. And I’m skeptical that there’s enough tasks where it’ll be worth it, and I’m skeptical that using voice as the primary input will be good enough to drive the whole interface.

Beyond the idea that it’s cool, you have to ask:

In what tasks does Google Glass actually perform better than a smartphone?

And I don’t think there’s enough use cases to make this work.

Looking at the use cases
One datapoint on this is to watch the recent Glass marketing video to find out all the use cases they demonstrate. But let’s try to ignore all the awesome acrobatics and beautiful scenery, and just focus on what people are actually doing with the UI:

List of use cases
Here’s my list of what people are doing on Google Glass:

  1. show the time
  2. record video
  3. send message via voice
  4. start video conference
  5. search Google images
  6. get the weather
  7. take a picture
  8. get directions on a map
  9. get flight details
  10. translate “delicious” to Thai
  11. look up something on wikipedia
  12. share a photo

(Of course, it should be noted that part of why they are creating this new developer preview is so that more apps can get written- but in that case, it’s fancy technology looking for a use case)

Glass versus phone (or other cheaper wearable devices)
The biggest issue with the above use cases just aren’t significantly better with a computer attached to your face rather than the computer you carry in your pocket. Most of these are basically simple things you can already do on your phone- checking the weather, the time, etc. There’s a small collection of things I’m convinced will be a lot worse, like searching for stuff or sending texts to people, because voice input is still weak. And then there’s a small set of things, like taking POV photos or looking up maps, where Glass can really offer a better experience. Are those enough?

Voice sucks as the primary input
In particular I’m skeptical of voice as the primary input. I think it’ll doom the product in the same way that horrible handwriting recognition doomed the Apple Newton. The state of the art on voice input, frankly, really sucks on both Android and iOS. Have you tried to compose a message that wasn’t “ok” or “coming home” via voice? Especially in a noisy cafe or on the bus? Plus people are going to seem like crazy folks, talking to themselves over and over again, trying to coax their devices to do what they want.

(You can easily do an experiment on this by trying to do everything on your phone without touch for a while- you won’t last long, it’s super frustrating)

It may be that they have some new magic voice capabilities they’ll release as part of Glass, yet at the same time, wouldn’t they bring it to the 500 million Android devices first? And if the magical voice capabilities on smartphones get better, won’t it erode the differentiation of using the devices versus Glass?

I hope it works
Ultimately, my final point on this is that I hope it all works. I haven’t used Google Glass yet, and will be really excited to try it out. I hope it works. But rather than being wowed by just the idea of wearable glasses, I think it’s important to start talking about developing the actual use cases. How will people interact with this thing that will make it an amazing experience? Especially in the context of all the other wearable computing devices we’re sure to carry with us- phone, watch, Fitbit, Nike bands, etc. And those are the kinds of questions we’ll need to answer to really push the next generation of devices forward, rather than just make really awesome gadget porn.

Written by Andrew Chen

March 6th, 2013 at 11:33 am

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Rational Growth (PDF): An intro to growing user signups via data and analytical thinking

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When I talk to startups about user growth, one topic that comes up is that there’s an overwhelming amount of noise out there on the topic. And on top of that, there’s a huge emphasis on the tactics – little tricks like turning buttons orange, or what cool new Facebook integration to try. These tactics are helpful, but without a broader framework to tie it all together, it can get easy to make 10-20% improvements but lack the approach to really substantially grow a product to the millions of users.

Below is my attempt to do a better job, and describe the mindset and experimentation needed to get to growth.

Download the PDF here

About a year ago, I participated in a series of interviews that would try and provide an intro text on the subject. The idea is just to provide a basic intro to thinking about user growth from an analytical standpoint. These interviews got turned into a PDF, sponsored by AppSumo, but then sat in my email inbox for months until I had the time to read through and approve it.

I’m happy to make it available here on my blog.

Outline of the PDF:

  • Introduction
  • Visual to spreadsheet based models
  • Your signup flow
  • Example of DailyDiary website
  • Brainstorming about growth
  • Shortening the signup flow of the DailyDiary site
  • SaaS example with “TeamShare”
  • Convert Now versus Free Trial
  • Other random things to try
  • Conclusion

Written by Andrew Chen

February 26th, 2013 at 3:44 pm

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3 common email marketing failures (Guest Post)

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This is a guest post by a friend of mine on email marketing. Elizabeth Yin is the CEO and a co-founder of LaunchBit, an ad network for email newsletters.  Previously, she worked at startups and Google, and went to MIT for her MBA, and Stanford before that. In her spare time, she enjoys writing posts about how to improve your email newsletter (fun hobby, I know :) ). Some other great posts include Can we improve your open rate and click-through-rate?Why you should love Hotmail and Yahoo subscribers?, and Which links get the most clicks in an email newsletter?

3 common email marketing failures
by Elizabeth Yin, CEO of LaunchBit

Email marketing is one of those tricky marketing channels, where it’s tough to know what or how to improve.  So, my friends and I recently launched the Email Newsletter Report Card, which can analyze your email marketing campaigns of last year and give you an assessment of how well you’re doing.  This free tool integrates into your email marketing product and gives you grades for:

  • your open rate
  • click-through-rate
  • list growth
  • unsubscribe rate
  • spam complaint rate

… all in the form of an infographic.  You can also see how your newsletter compares to other newsletters.

I run a few email newsletters on online marketing, anime, and startups.  This is what I learned from running my own newsletters through the Email Newsletter Report Card, which may apply to you too- here’s 3 common issues:

  1. Email subscribers from bad customer acquisition channels
  2. Too many links in each email
  3. Sending emails at the wrong time

It turns out these issues are pretty easy to detect, and thus fix, which we’ll discuss below.

1) Email subscribers from bad customer acquisition channels (like Facebook!)
It turns out out that where your email subscribers come from plays a huge role in their engagement and quality. Even if your email subscribers double opt-in to the lists, you may still find that certain sources will hit “Spam” more often than others, thus endangering the spam scores of your emails.

Let me compare and contrast two newsletters- as I mentioned before, I have an email list on marketing, and another one on anime too.

My marketing newsletter has a 0% spam complaint rate across all of my campaigns of last year- here’s what my infographic looks like there:

I was surprised, because this is extremely rare.  The flip side is that I have a high unsubscribe rate.  So, it’s not that my audience loves my content — they just know not to hit the spam button.

In contrast, my newsletter called Anime Goodies shows a spam complaint rate nearly 5x the average and a much lower unsubscribe rate:

Most subscribers on the Anime Goodies newsletter came from Facebook.  And, even though these people double-opted into our lists, going forward, I’m much more cautious now of doing user acquisition for this list on Facebook.

Once your newsletters start going to spam, email clients, especially Gmail, start sending more of your emails to spam.  Furthermore, since this newsletter is on a shared IP address, once my audience starts hitting the spam button too much, my email service provider will move me to a “dirtier” IP to share with other spammers.  If I’m sharing an IP address where subscribers on other lists are hitting the spam button, my emails can be affected and go to spam even if my own audience stops hitting the spam button.  So, going to spam is a very bad cycle that I need to fix on the Anime list.

2) Too many links
If you are trying to optimize for users to click on a single call to action in your email newsletter, the best way to do this is to have fewer than five links and place the call-to-action in link #1 or #2.  Having more than 5 links actually distributes your clicks.

This is what the analysis of the # of links looks like, on the Email Newsletter Report Card:

Right now, the average number of links is 23.3 per newsletter (the tool updates the average as more newsletters use it).  As you can see, another newsletter I own, called Startup Frontier, has on 9.4 links on average, which is a lot more links than I thought it would count.  Apparently, it is really easy to unknowingly add links, so that is something I’ll be more cognizant of going forward, since I want to improve my click-through-rate on specific calls to action.

3) Sending newsletters at the wrong time
Lastly, I typically send the Anime Goodies newsletter at 7pm GMT, but I just learned that most people open the newsletter at 6pm GMT — basically a day later:

Although the best time to send your email campaigns will vary across newsletters, I now know that for this particular newsletter I can change the send-time to get my message in front subscribers sooner.

Get your own email newsletter scorecard
If you’re interested in getting your own email newsletter graded, just go here. And below is an example of what the infographic output looks like:

Written by Andrew Chen

February 12th, 2013 at 10:00 am

Posted in Uncategorized

Why it’s hard to evaluate new social products

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Like any tech early adopter, I try and evaluate every new buzzy product that comes to market. These days, many of these come with built-in social and messaging functions.

“What’s the point of this?”
Most of the time I find my most common reaction is “hmmm, well this is neat, but what’s the point?” These new products often look trivial, even unfinished. And yet, I find my reaction is often wrong because of how hard it is to evaluate the “network” component of a new social product, when the network is often 95% of the value prop.

Network blindness
I find myself blinded by the following:

  1. It’s hard to demo the network, when only a few people are on it
  2. It’s even harder to connect w it if it’s not YOUR kind of network – Quora’s the exception to the rule :)
  3. The typical # of connections you need with active users to really appreciate a product is something like 10-20, and it’s hard to break through that threshold if you aren’t acquired organically via invites from a dense network already
  4. The value of the network rises exponentially, which is hard to comprehend. That means a network can rapidly go from useless to useful very quickly
  5. Finally, the non-network components seem technically trivial – the 140chars, the snap, the ding dong. This leads to a “WTF” moment for many people

Imagine using Twitter for the first time, but no one’s on the network. The whole thing seems pointless, and while it’s easy to grok the mechanics of how it works, it’s hard to guess that “oh, one day 200 million people will be using Twitter and then it’s really useful to find out news, celeb gossip, articles, and chat w people.” It’s a communication network at its core, and without the people, it’s not very useful.

eBay, browsers, and other platforms and networks
Many platforms and networks are like this- think of eBay with only beanie babies, and the foresight it would take to think the network would go beyond collectibles. Or imagine using the web browser if the web was only a few thousand pages. The same things that make these really powerful networks later on are the same things that impede comprehension of their value in the early days.

Single user products
Contrast that with great single-user products like Evernote, an amazing mobile game, or a new 3D TV. The value is a lot more obvious and it’s easier to demo because there’s no network component. You don’t have to extrapolate.

In fact, extrapolation of network effects is so hard that it probably makes sense to just try to invest in stuff that already has an engaged network, even at a small level. And to look at stuff like network density, the activity of cross-network interactions, etc. from a metrics standpoint. But even at the end of that, you still need to make a big leap on if they can get to the next level :)

Written by Andrew Chen

February 5th, 2013 at 12:24 pm

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Confessions of a Startup Seagull

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Inspired by the tech community’s recent usage and dissing of every new product that comes to market- The rapid cycle of tech news and new products from startups has created what can only be described as a Startup Seagull:

  • First, they hear about a new product
  • They dive in to try it out
  • Then, they shit all over the place. “This product is horrible!” Preferably in public, on a blog or Twitter
  • Finally, they fly away, never to use the product again

I’ll admit, I do this, all the time. By definition, most new products aren’t great and won’t have amazing retention early on. On the other hand, it’ll be a constant that we’ll all try new products whenever we hear about it. So maybe this is an unavoidable fact.

Ultimately though, I ended making a rule for myself when I blog or tweet, when it comes to startups. I only diss products that are already successful ;) For all the up-and-comers, if I don’t have anything good to say, I just avoid saying anything at all. Then at least I’ll be a Startup Seagull, but without leaving a mess for someone else to clean up.

Written by Andrew Chen

January 14th, 2013 at 1:43 pm

Posted in Uncategorized