Andrew Chen Archives

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.

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.

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.

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.

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!

[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.

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.

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

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.

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:

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 :)

In every startup’s pursuit of growth, it’s important to remember that first and foremost we’re looking to create something that’s sustainable. Building something big and impactful takes years, and your distribution strategy will need to weather the passage of time. If you slash and burn your customers, your platform, or your product design, it’s a matter of time before your active users curve jumps the shark.

This means that your growth strategy has to be “polite” and be considerate of all the parties involved:

Customer-friendly
If people love your product, it’ll growth more quickly and be more viral. Ultimately if you put the same viral mechanics on a photo-sharing product versus a tax returns-sharing product, the former will always do better because no one wants to share their tax returns, not even presidential candidates. Tapping into an emotional desire to share and communicate is a prerequisite for building a long-term product.

Don’t try to force people to do what they don’t want to do, all in the first session. You’ll burn out your audience, fail to retain an active userbase, and while that might look good in the first few months, over time your churn will beat your growth rate. That leads to a rapid decline, which you don’t want.

Platform-friendly
This decade has been amazing for platforms. 20 years ago, it was just Windows. Today, you can build for iOS, Android, Facebook, Twitter, and many other emerging platforms are coming out. Each platform wants something different from you, and you have to learn to play by the rules to have a lasting relationship with them.

Obviously this means you can’t burn their users – that’s the worst thing to do. Dumb, too. Some platforms want more engagement and user-generated content, and others want ad revenues. Learn what it is that they want, and make sure your product helps them as much as it helps you.

Product-friendly
And finally, it’s important that your growth mechanics don’t compromise the design of your product. When you first started writing your product, I’m sure there were big aspirations about what it could do and what good it would ultimately accomplish for the world. Halfway along the way, when it’s time to work on marketing and growth, it can be easy to overreact and compromise your core design. Products meant for classy audiences suddenly turn into quiz apps. Ultimately, to stay excited about your product over the course of years, it’s gotta stay in a sweet spot – you can’t let growth destroy that.

There’s a lot more I want to write about on getting sustainable growth- everything from how to use A/B testing not to make a number go up, but how to make a number stay unchanged while you iterate on the feature qualitatively. I’d also like to write about the quantitative effect of overusing notifications because spam tests well short-term, but destroys your response rates long-term. Those, and many other topics, coming up soon.

Also, if you’re interested, I’ve written before about the importance of balancing growth and other factors in previous posts: You don’t need a growth hacker, How do I balance user satisfaction versus virality?, When does high growth not imply product/market fit?, and Know the difference between data-informed and data-driven.

One of the frequent points I try to make on this blog is that metrics are a reflection of your strategy you’ve chosen, not the other way around.

This is particularly important in the context of comparable numbers, like +1 day or +1 week retention, DAU/MAU, or the plethora of other metrics that are used to assess a business. It’s not a good idea to just blindly try to hit a certain set of metrics – different kinds of products have different sets of healthy numbers. The best example is something like tax software, which has a DAU/MAU of essentially zero but you can still build Intuit out of it. On the other hand, if you compare favorably or unfavorably in your category, all the better. I previously wrote about this in the context of DAU/MAU and “nature versus nuture for products”

On this note, Flurry recently updated their retention versus frequency chart for different mobile app companies and it’s worth checking out.

The outliers are super interesting:

• Communication is both super retentive and high frequency, but man, what a busy space :)
• Streaming Music, Games, and Dating have a lot of frequency while you’re using it, but you soon abandon the app and go somewhere else. Probably a good argument for products in this category to try to make money right away, since you won’t keep them for long
• News, Sports scores, Reference, Weather have high retention, but not necessarily high frequency. Probably some great businesses to be built here, especially when you can tie it to some kind of transaction – sports and reference, in particular. Weather, not so much?
• Retail is probably apps put out by brands that aren’t super useful. The other stuff in that corner all sounds junky
• Photo and Video surprisingly has terrible stats for something so important. Maybe outside of Instagram, it’s sort of an overrated category?

Anyway, worth reading the article and looking at the diagram more closely. Thanks again to Peter Farago at Flurry for putting this together.

UPDATE: Nabeel Hyatt of Spark Capital (previously GM Zynga and serial entrepreneur) wrote some excellent commentary on this chart as well. Worth reading.

Some blog posts work, some don’t. Why?
I’ve been blogging over 4 years, and after writing nearly hundreds of posts, I’ve developed a high-value niche audience of over 15,000 blog subscribers and 28,000 Twitter followers. My focus has been completely on writing about startups and high-tech companies. Building up my blog in this niche audience has been a lot of fun and professionally rewarding too.

I’ve had the time to collect some observations on what works and doesn’t work, and wanted to share an interesting stat: It may not surprise you to know that the most popular 10% of my blog posts drive over 500x the traffic of my average blog post. It’s a classic Power Law distribution.

Blog titles matter
One of the most important things to any blog post is its title. It’s the first impression your writing will make on anybody on Twitter, Facebook, or any news site where your link might be shared. If you don’t impress instantly, people won’t click, and they won’t get to read your amazing content.

So what kinds of blog titles attract the most attention?

Here are the patterns that I’ve found to work really well:

1) “The tweet-sized argument”
It’s highly effective when your title argues for or against something, in a tweet-sized package. Especially when the argument uses lots of superlatives, like “best” “worst” “obsolete” or otherwise.

Take a stand! Make an argument! In real life, people usually don’t believe in the extremes- instead, they are always comfortably in the middle, in the shades of gray between two options. When you argue something, and argue it strongly, they’ll want to read it- if only to refine their own thinking.

Examples:

• If you hate your job, quit it. Today.
• The iPhone 5 is the best phone ever made
• Don’t start a startup, you’ll end up a pauper
• Mobile apps are going to make websites obsolete

2) “The sneak preview”
The other important pattern is when you can use start blog posts with titles like:

• How to do X…
• Why I think X…
• When does X happen…
• 10 ways that X…

Assuming that the topic X you’re picking is really interesting, people will check it out and find it insightful. They’ll share it if they think it’s interesting to learn how to do what it is you’re talking about. The important idea here is that the title is a promise for what you are going to elaborate upon in your post.

What not to do
What happens when you don’t use the patterns like above? Well, the most common case is that people write blog posts that are descriptive, but abstract. Something like “Google and their mobile products” or “Our product features” just sounds weak, compared to “Google makes amazing mobile products” and “5 amazing features in our new product.”

And at the same time, if every post you write is “5 ways to X” you’ll sound cheesy. So there’s a fine line there. Basically the trick is, don’t use your title to describe your content, use the title to trigger an emotional desire to read your content. Do it well, and every post will spread far and wide in your target community.

My little town is having a film festival over the next couple days, and I noticed this movie on design called Design & Thinking. I included the description below, but it features folks design luminaries from IDEO, Smart Design, AIGA, the Stanford d.School, Jump, and many others. It also includes Bill Moggridge, a legend and designer of the first laptop computer, who recently passed. More info:

“Design & Thinking” is a documentary exploring the idea of “design thinking”!
How do we fully engage organizations to think about the changing landscape of business, culture and society? Inspired by design thinking, this documentary grabs businessman, designers, social change-makers and individuals to portrait what they have in common when facing this ambiguous 21st century. What is design thinking? How is it applied in business models? How are people changing the worldwith their own creative minds? It is a call to the conventional minds to change and collaborate.

Anyway, I thought I’d highlight it- if you’re not in Palo Alto, hopefully it’ll make its way to Hulu or Netflix shortly too. Tickets here. Other films at the festival here.

Startups don’t need growth hackers – at first. They need products that are really working in the market. This means users love it, that there’s lots of retention and engagement, even at small numbers.

The reason for this is that ultimately working on scalable growth is an optimization problem. And it’s a combined product management and technical function, to boost an already positive growth curve into something even bigger. The analysis needed to drive user growth require a baseline of usage, whether they are A/B tests, cohort analyses, or lifetime value calculations, and the changes that make those numbers go up are product changes. The more data you have, the faster you can iterate and generate more growth.

In fact, it’s the lucky startups in Silicon Valley that end up spending a significant amount of their time on growth. Most of the startups I run into in Silicon Valley are failing because their products aren’t working yet for their customers- the reflects itself in low growth, but also low engagement numbers too. You won’t fix that just by getting more people to sign up, though it’s critical to iterate on your product with feedback and data from real users, of course.

Pre-product/market fit
When you are pre-product/market fit, and you only have dozens of friends and family using the site, you don’t have enough usage to create a baseline. What you need here is a lot of lead bullets, not one silver bullet. This is where PR, community management, partnerships, and other forms of hard-to-scale growth techniques are great. This is where you need to iterate on the product based on your own expert intuition of what it needs to be. And once you have enough usage and your product is working, then you can use some of the more quantitatively driven growth techniques.

Similarly if your product isn’t retaining users, it won’t help much to pour water into a leaky bucket. Growth without retention may increase your vanity metrics like total signups, growing your active userbase to substantial levels requires you to get beyond just signing up more users. Once you hit some saturation, things will fall apart as your user curve jumps the shark.

So again, I repeat- startups need product/market fit, not growth. Growth comes as a result of having achieved fit, and a growth team is built to optimize the curve. The real question is, how do you get to product/market fit, given that most startups fail to get there?

Early product work is incremental and intuitive
If you’re a startup with minimal users and weak usage, keep iterating on product and doing the hard work of building an initial community. If you think adding some Twitter sharing will help your value prop, then implement it- you don’t need to tune or optimize the functionality until you have some scale. If you think that your landing page doesn’t communicate the value prop very clearly, then just change it. You can get more scientific about it later.

At some point you’ll have enough usage to think about optimizing easy things, like signup or sharing flows. The goal is to move fast and ship a lot of product iterations to get to that usage level. But until then, it’s a waste of time to build a huge analytics system for A/B testing when you don’t have to.

It’s working? Great, now build your growth team
Eventually, if you beat the Trough of Sorrow, you’ll start to find evidence that your product is working. Qualitatively, you’ll see the same users over and over, and they’ll tell you how much they love your product. Your own personal opinion of the product will change – you may not be 100% satisfied with what you’ve built (we never are) but you’ll find some utility for it in your life. Quantitatively, you’ll have to look at other products in your space to compare, to see if you’re really there. For a social consumer product, you might look at metrics like DAU/MAU (is it 10 or 20% or higher?) or next day retention (20% or 30% or higher?) or you’ll start to see some slow natural growth that you can ramp up.

The first steps of working on growth are often super easy – figure out the critical flows in your site, like signing up and sharing, and what factors turn users into successful and active ones. Now start optimizing for that, starting with a few people working on a small number of A/B tests at a time. Based on how that goes, you can ramp it up over time.

If you can be one of the few startups that gets to product/market fit, and you need help with growth, then build up that team as needed. That’s what Twitter, Facebook, LinkedIn, and many others did- they added the growth team after signing up millions of users, and it didn’t hurt them in the long run. Try to start optimizing growth too early, and you may not have the product in place to become a long-term success.

The life of a startup
A few years back at a YCombinator dinner, Paul Graham and the other partners drew a great diagram depicting the life of a new product. The main discussion is here: http://news.ycombinator.com/item?id=173261. It captures a viscerally truthful thing about the life of a new company- first you’re excited, then you’re not, and if you stick with it, you just might make it work. It could take years. But you may fail too, you never know until you do it.

The Question
The big thing is, while you’re in the Trough of Sorrow is, what do you do? How do you beat it?

Traditional business literature won’t help you solve it- most of that stuff is focused on life after product/market fit, after the Trough of Sorrow. A lot of startup stuff is focused on the initial phases, when you don’t have a team, idea, or investors.

What happens when you have a team, an idea, and investors, but it’s not quite working yet? What do you do there?

How to beat the Trough of Sorrow
I have some notes from my personal experience, and from others who have beat the Trough of Sorrow, and wanted to share them. First off, there’s both an emotional component as well as an analytical one.

Dealing with the emotions
Let’s start with the emotional first. First, a couple important things to remember:

• Getting to product/market fit is hard, and even though you feel like you’re uniquely failing, you’re actually not. Turns out every startup has to go through this, but not every startup survives it. Entrepreneurs will blame themselves for failing, but it’s OK, this is hard and we all start the journey by failing a lot.
• A corollary to the above is, expect to face the Trough of Sorrow. It’s hard to avoid. Quitting, starting over, executing a “too big” pivot, and other avoidance strategies won’t keep you from hitting a difficult point again, it’ll just delay the inevitable. Instead, just figure out how to work through it.
• Expect to fight with your cofounders. When things are going great, cofounders tend to go along since the focus will be on keeping the momentum up. When things are mixed or going badly, there will be meaningful disagreements about what to do next
• Quitting is your decision. There’s a huge spectrum of tools you can use to fix up a broken thing. You can change the product, switch customer segments. You can recapitalize the company, reset the team, and fire your cofounders. You can (usually) find a way to keep going if you want to. Whether or not you want to quit, that’s up to you, but don’t think that quitting and starting a new thing will let you start something up without passing through this difficult phase
• Churning customers, employees, and cofounders isn’t failing. While you’re going from one iteration to the next, people will fall off the wagon. It just happens. That’s OK! That’s part of what happens, and even though it’ll feel like it’s a failure, don’t let it discourage you. The question is, does the new strategy make more sense than the old one? You only fail when you fail.

An additional thought on quitting: It’s ultimately the entrepreneur’s personal decision to quit, because there’s always some alternative scenario, as unpleasant as it might be. You can always dilute yourself more, raise more capital, or reduce the burn rate. It can add more time to the clock, which might be unpleasant, yet it might save the company. Is it always logical to do that? Maybe, and maybe not! But it’s worth considering that there’s always another move, and an entrepreneur shouldn’t ever feel like they’re somehow “forced” to quit.

A lot of entrepreneurs quit when they hit the Trough of Sorrow, struggle for 12-24 months, and face up to the reality that they’ll have to raise another dilutive round. Is this a good time to quit? Maybe. But given that the majority of startups go through this kind of stage, I’d actually argue that it’s just part of struggle to being successful. Sometimes it just takes 3 years to get through the Trough of Sorrow, but on the other side is something that might really be worth the pain. Maybe :)

I find that when I spend time with startups as an investor/advisor, a lot of my time ends up being about the above issues. Probably 80%, actually. If you can minimize the emotionality of feeling like you’re failing, you can try to keep the team together and get to the problem solving part.

Dealing with the problems
If you can hold everything together, and keep the team productive enough and the runway long enough to try to make a run at the problem, then here’s a few wild unfounded generalities on how to proceed. It’s super hard to generalize here but here’s an attempt.

• Identify the root problem. Is the product working? Does the onboarding suck? Or is execution on growth lacking? You can figure out the main bottleneck by trying to understand where it’s working and where it’s not. If the problem is high retention and high engagement, but not a lot of people are showing up, just focus on marketing. If the product is low retention and low engagement, you probably have to work on the product. More marketing and optimizing your notifications won’t help there
• I find that much of the time, startups take too much product risk, and that’s why they aren’t working. Most of the new products I run into aren’t at the phase of “we’re product/market fit, just add more users!” Instead, most of the time, the products are just fundamentally broken. They are asking users to do new things, they exist in new markets with no competitors, and as a result, it’s unclear if the customer behavior is there to support their product. Instead, try to take a known working category and try to invent 20% of it, rather than 90%. Apple didn’t invent the smartphone, the MP3 player, or the computer, and yet they are super innovative and successful. You don’t have to invent a new product category either, and it’s easier to get to product/market fit when you have a baseline competitor to compete against.
• Resist the urge to start over. There’s always a feeling that if you just rebooted, you’ll somehow avoid the Trough of Sorrow. Not true. Trust your initial instincts in your market and in your product, and figure out how to guide it into a similar place. If smart people invested in you and in the market, there’s probably something there, but you have to find it.
• Get your product to be stripped down, focused, and so easy to understand that it’s boring. Look, you’re not in this to impress your designery friends, you’re in this to communicate your product’s value prop in simple and focused terms. The closer you are to that, the more boring your product will sound- that’s a good thing!
• Money buys time, and time buys product iterations. This is why there’s a school of thought that says, raise as much money as you can at every point- before product/market fit, raise the max amount so that you have as many iterations as possible to ensure you get to P/M fit. After P/M fit, raise as much money to maximize the upside. Something a few steps back from that extreme is probably the right one :)
• Pick up small tactical wins. Even if you do something in the product that doesn’t scale at first, it can be worth it- like prepopulating content, inviting all your friends, doing PR, etc. These small wins build momentum, raise team morale, gets you incremental amounts of capital, and makes it so that you can keep going. Over time, to scale, you can figure out how to systematize these processes or they can end up bootstrapping bigger and more scalable ideas.
• Small teams are great. They move faster, way faster. If you plan to do lots of product iterations, you don’t need to communicate all the changes and get buy-in from everyone. Conversely big teams have lots of chaos every time there’s a bit pivot. Build out the team afterwards to create the complete featureset, but until then, consumer product teams can just be a few engineers/designers and the product leader. That’s <6 people.

I could write lots more here, but I’ll save some thoughts for next time :)

Finally, I wanted to quickly reference a step-by-step roadmap I wrote a year back with some more thoughts on getting to product/market fit, which you can see here: http://andrewchen.co/2011/05/22/2011-blogging-roadmap-zero-to-productmarket-fit/

I recently wrote a blog post about how Mobile Startups are Failing Like it’s 1999. The idea is that they are taking too long to ship their initial versions and then spending too much time between updates. As a result, they fail in a way that’s reminiscent of 1999 “waterfall”-style product development practices. We can do better.

The post was meant to be a challenge to the whole tech community, and I got a bunch of great responses back on how we might improve the iteration cycle. The ideas and suggestions tended to fall into a couple different categories:

• Picking the right (minimum) product
• Testing the market before launch
• Coding and shipping quickly

I got some great thoughts, particularly from YCombinator alumi, and I wanted to highlight some of the comments. They were very, very good.

Picking the right (minimum) product
The first thing is that it’s important to pick the right minimum product to build. Startups working in the Apple App Store have to satisfy three contradictory things:

1. Release a high-quality app
2. Release it quickly, to iterate with lots of funding in the bank
3. Get enough downloads with <$1M in funding to get the next round

The classic way to say this is, you can have it good, cheap, or quick – pick 2. Most of the time, what startups have under their control is quality and time to release, so let’s just focus on that. The best way to have good+quick is to create a polished app with limited featureset. That way you’re not skipping out on the polish, but you’re also not taking too much time.

The other big thing is to pick an existing market. If you have competitors but have an obvious way to differentiate, the amount of wandering you need to do before hitting product/market fit will (hopefully) be less. Given that iterations are expensive on mobile, this becomes a big advantage. If you are trying to do something new and the consumer behavior isn’t there to support it, then things might get scary since you’ll need to explore the market which takes time, money, and iterations. Expensive.

Kieran discusses the idea of polished but limited featureset in a comment below:

Kieran O’Neill, Founder of Thread
Technical solutions aside, I think the product development answer is to build nicher/one function/quicker apps initially, then expand to more ambitious, tangential goals once you’ve reached some initial success. You want to do this on the web, also; the problem is just more acute on mobile as you point out.

As does Tony, here:

Tony Wright, Founder of Tomo Guides
This is an awesome post. I used to believe that you need a big launch to succeed in the app store. I thought there was so much gravity in the app store that you needed a PR bomb to get you into the top apps— and that organic downloads from Apple’s “Most Popular” lists would keep you above the crowds. But I’ve seen too many big boom apps fall to the basement once their PR wore off.

I think the solution on our side is to launch earlier and re-embrace the MVP. Don’t gun for PR. Find a beta audience and serve them, even if you (and they) have to wade through the awfulness of TestFlight (“easy over the air betas— HA!”). Focus on scalable/repeatable customer acquisition and don’t Launch (with a capital “L”) until you’ve solved many/most of your product/distribution challenges. That way, you’re launch is throwing gasoline on a fire and not a wet pile o’ wood.

The point is that we’ve been trained to iterate fast, deploy multiple times per day on the web, and that’s now a best practice. Facebook deploys twice per day with nearly 600 developers, for example. However, on mobile that culture hasn’t been ingrained. Because of the app store process, really high quality product management becomes important because otherwise, it’s easy to let things take days, then weeks, and then months, between app releases. That’s not moving fast.

Testing the market before launch
Knowing that your v1 will be solid before releasing it also becomes super important, because of two main factors:

• App store leaderboards, where a sustained spike of traffic drives more traffic
• App store reviews, where you want as many 5 star reviews as possible

This means you want to squash all your bugs and deal with the major design issues before you try to get your big launch spike. Otherwise, you might get a spike plagued with bugs and 2 star reviews – not good.

I got a couple great comments about how to do this, by stealthily releasing and rebranding. Matt Brezina’s genius comment below:

Matt Brezina, Founder of Sincerely
This is a great observation Andrew. One thing we did was launch 2-3 versions of our product under a different apple account, without our personal names on the app, before we launched Postagram. When we did a PR launch the product was basically just a branded version of our work from the past 4 months; we knew it would function well and we knew users would love it. Since then we’ve never spent more than 4 weeks developing a release. And we particularly use Android for quick experiments – the apple 1 week app approval delay can really slow down the iteration cycles – that, and the difficulty in doing a/b tests are my least favorite things about the current mobile development environment.

Kenton, who works at Zynga on Mobile Poker, also mentions the great idea to use Android to prototype since the updates are easier:

Kenton Kivestu Senior PM, Mobile Poker at Zynga
Part of the solution is to develop / test features on Android where you don’t face the rigor or delay of the Apple approval process. Also, I think Kieran’s point is valid as well. Apple may have a high quality standard but there is no inherent reason that you need to spend 6 months developing something to get Apple approval. The 6 month development time is probably more indicative of feature creep, broad scope, testing too many things, over-polishing, etc.

Another interesting idea is to test your app initially in another geography so that you can get things right. That might be Canada or New Zealand, where you have high smartphone penetration and an English-speaking audience that’s similar to the US.

Coding and shipping quickly
When it comes to the actual product development execution, you have to ask yourself, what’s the real bottleneck? Is it submitting to Apple? Well, let’s say that you can do that every 7-10 days. Then let’s work backwards and say that you set yourself a simple goal.

Whenever you have an opportunity to submit something to Apple, you have something to submit.

What would it mean to try to satisfy this goal? I think what it means is that you end up building your product out in 1-week chunks. You end up scoping down a lot of the featureset so that you can deliver it incrementally in 1-week timelines, with some testing on day 5. For some longer features, you try to get it as close to 1-week as possible, and spare the minimum wait in between.

Similarly, even as you submit an app every week, you can still have a daily build – just use Testflight. This means you can do an internal release of your app every day, and your friends and family can try things out.

How feasible is this? Well, again, on the web we’ve gotten used to the idea of deploying multiple times a day- why not in mobile apps as well? It’s doable.

Conclusion
So those are the ingredients for iterating quickly: Simple but polished v1 app. Systematic market testing before launch. Strong, iterative product management. Weekly app submissions and daily testflights. Combine that with ample mobile startup funding, and the strong teams we have in tech, and hopefully we’re getting somewhere!

More ideas and suggestions for how to go better, faster, and stronger are welcome. Please comment below.

2012 has been good to startups.
It’s never been easier to raise seed funding, and there’s warnings that we’re in the midst of a “seed stage bubble.” Whether you think it’s a bubble or a boom things are good- you have to ask, how long will the party go on?

My theory is, we’re currently in a golden age for early stage startups, and the early stage market will stay hot for at least the next 3-5 years.

Here’s why the good times will continue
My reasoning looks something like this:

1. Right now, startups with strong teams can easily raise seed funding ($200-$1.5M or so)
2. They can easily raise seed money because there’s a lot of willing investors in the ecosystem. VCs are seeding deals without any price sensitivity, and a lot of angels seeing exits even when the teams fail
3. Angels are willing to invest because they have downside protection due to acquihires. They can invest $50-200k per deal and in the event of a startup failure, they get their money back (and sometimes even get marked up to a profit!). If the startups succeed, they have tremendous upside.
4. The downside protection is driven by acquihires from companies like Twitter, Facebook, Groupon, and others which are paying $1M-$3M per engineer. This makes sense to them because there are multiple billion-dollar markets at play.
5. And ironically, because this whole system exists, the engineers at great startups feel like they can splinter off and start their own thing, which feeds into the whole thing

Given that team acquisitions provide downside protection while the hits drive the real returns, it’s hard for investors behind top teams to lose money. So the question is, when will the downside protection, in the form of acquihires, disappear?

Mobile as the driver
IMHO, the answer to that key question is, I think we’re another 3-5 years because of one key thing that’s driving all of it: iPhone. (And Android, and the rest of the smartphone industry).

It’s going to take 3-5 years for the mobile market to sort itself out. As long as smartphones are still progressing from their current 100s of millions to the final 3B active users number, every company will be investing in this new platform, and they’ll keep buying as to not get left behind. Otherwise, they’ll be left on a previous platform as a new competitor emerges that’s mobile centric, and smokes them.

There’s a whole host of companies in the Bay Area, in Asia, and around the world that are investing heavily on mobile. They’ll buy any team they can get their hands on.

So they’ll keep acquihiring talent, supporting the whole thing, until the mobile market is set.

Whether you think this is a good thing or a bad thing, IMHO the mobile wave is so huge that it has the ability to power the early stage investing marketplace for years. Agree? Disagree? Tell me in the comments.

Stop the madness
The long cycle times for developing mobile apps have led to startup failures that look more like 1999 – it’s like we’ve forgotten all the agile and rapid iteration stuff that we learned over the last 10 years. Stop the madness!

Today, seed stage startups can now get funded, release 1 or 2 versions of their app spread over 9 months, and then fail without making a peep. We learned the benefits of how to iterate fast on the web, and we can do better on mobile too.

How things worked in 1999
How’d we get here? Back in 1999, we did a similar thing:

• Raise millions in funding with an idea and impressive founders
• Spend 9 months building up a product
• Launch with much PR fanfare
• Fail to hit product/market fit
• Relaunch with version 2.0, 6 months later
• Repeat until you run out of money

This was Pets.com, Kozmo, and so on. Maybe you’d fire your VP Marketing in the process too, out of frustration.

Between 2002-2009, we learned a lot of great ways to work quickly, deploy code a few times a week, and get very iterative about proving out your product.

How things work today
Then, with the arrival of the big smartphone platforms, we’ve reverted. It looks like 1999 but instead of launching, we submit into the iOS App Store.

It looks like this instead:

• Raise funding with an idea and impressive founders
• Spend 6 months building up a product
• Submit to the app store and launch with much PR fanfare
• Fail to hit product/market fit
• Relaunch with version 2.0, 6 months later
• Add Facebook Open Graph
• Try buying installs with Tapjoy, FreeAppADay, etc.
• Repeat until you run out of money

Not much different, unfortunately.

The platform reflects its master
We’ve gotten here because the App Store reflects Apple’s DNA of great products plus big launches. They are a 1980s hardware company that’s mastered that strategy, and when developers build on their platform, they have no choice but to emulate the approach as well.

Worse yet, it lets people indulge in a little fantasy that they too are Steve Jobs, and once they launch a polished product after months of work, they’ll be a huge success too. The emphasis on highly polished design for mobile products reverts us back to a waterfall development mentality.

Don’t burn 1/2 of your funding to get to a v1
Startups today have a super high bar for initial quality in their version 1. They also want to make a big press release about it, to drive traffic, since there’s really no other approach to succeed in mobile. And so we see startups burn 1/3 to 1/2 of their seed round before they release anything, it becomes really dangerous when the initial launch inevitably fails to catch fire. Then the rest of the funding isn’t enough to do a substantive update.

What can we do?
How can we stop the madness? What can do we do to combine the agility we learned in the past decade with the requirements of the App Store?

If we can answer this question, we’ll be much better off as an industry.

One of the most compelling organizational things I’ve read about lately is Square’s practice of referring to their product team as Product Editors and the product editorial team, rather than the traditional “Product Management” title. Wanted to share some quick thoughts below about it.

Product managers: One of the toughest and worst defined jobs in tech
The role of “product manager” “program manager” “project manager” is one of the toughest, and worst defined jobs in tech. And it often doesn’t lead to good products. The various PM roles often have no direct reports, but you have the responsibility of getting products out the door. It often becomes a detail-oriented role that are as much about hitting milestones and schedules as much as delivering a great product experience.

Thus PMs sometimes end up in the world of Gantt charts, 100-page spec documents, and spreadsheets rather than thinking about products. Now, all the scheduling and management tasks matter, but it’s too easy for PMs to lead with them rather than leading with products first.

Bad ideas are often good ideas that don’t fit
In the context of literature, books, and newspapers, it’s the job of the editor to pick the good stuff and weave it into a coherent story. You remove the bad stuff, but “bad” can mean it’s a good idea but just doesn’t fit into the story. It’s a compelling and important distinction for consumer internet.

Cohesion and consistency is difficult. When you have an organization with lots of very smart people all with their own good ideas, it’s difficult to decide which path to take. So often, products are compromised as the product “manager” doesn’t feel the responsibility to build up that cohesion as an ends in itself, and instead just tries to do as much as possible with the product given some set timeframe. Focus, people!

Jack Dorsey in his own words
In a recent talk at Stanford, Jack Dorsey describes his idea of editors:

“I’ve often spoken to the editorial nature of what I think my job is, I think I’m just an editor, and I think every CEO is an editor. I think every leader in any company is an editor. Taking all of these ideas and editing them down to one cohesive story, and in my case my job is to edit the team, so we have a great team that can produce the great work and that means bringing people on and in some cases having to let people go. That means editing the support for the company, which means having money in the bank, or making money, and that means editing what the vision and the communication of the company is, so that’s internal and external, what we’re saying internally and what we’re saying to the world – that’s my job. And that’s what every person in this company is also doing. We have all these inputs, we have all these places that we could go – all these things that we could do – but we need to present one cohesive story to the world.”

A video of Jack Dorsey talking about the concept can be seen here:

Lead with product
What’s compelling to me about this is that it really orients the role of product to be about cohesive experiences first and foremost. OK, yes, there’s still schedules first, but it doesn’t drive the thing- great products drive the process.

Similarly, you don’t just jam lots of characters and plot points in a story just because. Even if they are good characters, it can bloat the story. Same with features- sometimes you have many, many good ideas for your product, but if you come to do all of them, you ultimately make it a confusing mess. Instead, you have to “edit” down the feature list until you have a clean, tight experience.

Anyway, I hope to see this trend continue in the tech industry – it sets the right tone for where we should all be focused.

Design is in.

Consumer startups no longer need to argue about product quality – it’s a prerequisite to even an initial launch. This is a good thing, but this post isn’t about that.

For social apps, what you design directly is only half the user experience. The people are just as important! So if you build a really great linksharing site that’s extremely polished and full-featured, but the community consists of Nazis, it won’t work for people.

I’m often reminded of this fact when trying XBox Live, which consists of prepubescents killing you repeated on Halo while calling you gay. The Halo content is amazing, of course, but the community around it is… um… different than me.

Dribbble as an example
Similarly, you could build a product that was an exact replica of uber-design site Dribbble, yet still fail if you didn’t have their users. Half the work is the functionality, but the other half is “designing” the right users. If you haven’t seen the rules, a lot of things have to happen before you’re allowed to actually post content there:

• Why are players drafted? http://dribbble.com/site/faq#faq-why-drafted
• “Undrafted prospects” http://dribbble.com/designers/prospects

Basically, they have a long line of “prospects” which have to be nominated by the community in order to be able to post content. They limit membership like this so that all the content on the site will only be the very best.

Eventually, opening up is key
Perhaps naturally you eventually open up and evolve beyond this, but I think at the beginning you still need a lot of authenticity.

I think the reason why this whole concept feels unfamiliar to me is that for most consumer products, the problem is getting more people, not rejecting them :) Yet at the same time, I’ve learned through a lot of first-hand experience that if you don’t curate the initial community and scale your traffic as a function of this group, you can easily fall into the trap of “designed product, but undesigned community.” That’s no good either.

Surprisingly, it can be hard to figure out if you’re at Product/Market Fit or not, and one of the big reasons is that comparable numbers are difficult or impossible to come by. You have to look at comps for products in a similar or equal product category, and sometimes they just aren’t available.

Nature versus nurture
One way to think about this is that products have a nature/nurture element to their metrics. Some product categories, like chat or email, are naturally high-frequency. You use them a lot. Other products, like tax software, might give you value but you only use it once per year. A lot of ecommerce products are in-between, where you might buy gadgets every couple months but not every day. Just because people only use your product once a year doesn’t mean you don’t have product/market fit, as long as you’re building a tax product and not chat.

The two extremes are interesting:

• Medical apps: They may have high retention since if you have a chronic ailment, you may constantly be using an app relevant to your condition, but maybe not every day
• Books/Games: You read them nonstop for a few days or a week or two, and then once you’ve consumed the content, you never go back

The point I’ll make on this is that due to the nature of certain product categories, there’s a natural range of DAU/MAUs, +1 day and +1 week retention metrics. That’s the “nature” part of the product category. No matter how good your tax software is, you won’t get people to use it every day.

Based on your product execution though, you can maximize the the metrics within the natural range. A really good news product like Flipboard is able to drive 50%+ DAU/MAUs, which are fantastic.

Some product categories cannot get high DAU/MAUs
One key conclusion of this is that it doesn’t make sense to try to compare against Twitter or Facebook’s 50% DAU/MAU unless you are in the same category as them. A lot of social games target 30% DAU/MAU, but we can also see from the Flurry chart that social games are also amongst the highest DAU/MAU categories.

That said, if you are in the same category, then these rival products really tell you how good your metrics could really be, if you executed them in the right way.

Either way, don’t fight your nature :)

Update: New chart from Flurry
A while after I wrote this, Flurry released a new version of their chart, which you can see below. Full article here. It’s interesting to see which categories have shifted a bit, I imagine because the number of new apps in each category has changed a lot.

My friend Mike Greenfield recently started blogging, and I couldn’t recommend his blog Numerate Choir more. I also had him on my list of growth hackers from a month ago. Mike is (as of today) 500 Startups’s first “Growth Hacker in Residence” and before that, co-founded Circle of Moms (acquired by Sugar), and was a data geek at Linkedin and Paypal.

Some of his excellent recent blog posts:

• Why “A/B Testing vs. Holistic UX Design” Is a False Dichotomy
• Cutting the Facebook cord
• The Visionary and the Pivoter

And with his permission, I’ve cross-posted one of his recent essays below.

Enjoy!

No, you don’t need a real-time data dashboard*
By Mike Greenfield
Originally posted on Numerate Choir

When Circle of Friends started to grow really quickly in 2007, it was really tough for Ephraim and me to stay focused.

Many times over the course of a the day, Ephraim would turn and ask me how many signups we’d had in the last ten minutes. That might have been annoying, but for the fact that I was just as curious: I’d just run the query and had an immediate answer.

Rapid viral growth can be unbelievably addictive for the people who are working to propagate it. You tweaked a key part of your flow and you want to see what kind of impact it’s having — right now. You’ve added more users in the last hour than you’ve ever added in an hour before, and you wonder if the next hour will be even better.

That addictiveness can be a great asset to growth hackers; I’d argue that anyone who doesn’t have that sort of jittery restlessness probably wouldn’t be the right fit in a growth hacking role. Restlessness is a huge motivator: I want to grow the user base, so I’m going to implement this feature and push it out as quickly as possible just so I can see what impact it will have. And if this feature doesn’t work, I’m going to try and get something else out before I leave the office so I can see if I’ve uncovered something else before it’s time to go to bed.

One day, I came up with a feature idea as I was walking to the train station in the morning. I coded it up and pushed it out while on the 35 minute train ride. There was a ten minute walk from the train station to my office; by the time I got to the office I saw that my feature was increasing invitations by around 20%.

I loved telling that story to potential engineering hires.

Here’s the thing, though: if everyone in your company behaves like that, you may acquire a huge user base, but you’ll likely never build anything of long-term value. You’ll wind up optimizing purely for short-term performance, never moving toward a strong vision

Back during that Circle of Friends growth period, I decided to automate an hourly stats email to Ephraim and myself. It satisfied our curiosity about how things were growing right now, but it stopped me from running SQL queries every five minutes. At least in theory, that meant we were focused on real work for 58 minutes every hour. In retrospect, it seems ridiculous that we needed stats updates every sixty minutes, but that actually was an improvement.

My distracted experience is why I worry about the effect of analytics companies that now promote a real-time dashboard as an awesome new feature.

It’s technically impressive that they’ve implemented real-time functionality. And at first glance, it’s very cool that I as a user can log in mid-day and see how stats are trending.

But the key distinction — and about 60% of analytics questions I’ve seen people ask over the years are on the wrong side — is if you’re looking at stats now because you’re curious and impatient, or because those stats will actually drive business decisions.

I’m afraid that in most cases, real-time stats are being used by people who aren’t iterating as quickly as growth hackers. The “need” for stats is driven more by curiosity and impatience than by decision-making.

Execs who are making big picture decisions are probably better served by looking at data less frequently. Growth hackers and IT ops types can and should attack problems restlessly — a big part of their job is optimizing everything for the immediate future. But executives are best-served waiting (perhaps until the end of the week), so they can take a long, deep look at the data and think more strategically.

This last weekend, I watched Steve Jobs: The Lost Interview (It’s available on iTunes for $3.99 rental). It’s great for many, many reasons, and I wanted to write an important point I seized upon during the talk. Here’s the link, if you want to watch it yourself.

Let’s start with an important quote:

“Insanely great”

That phrase is one of the most confusing things about the Apple philosophy, and I think it is commonly misinterpreted. Product designers often use it as an excuse to endlessly work on their product, with no release date or eye on costs. It becomes the reason why people want to focus on building completely new products and avoid copying competitors.

Apple has done a lot of stealing and reinventing
Yet in the interview, Steve Jobs has lots of interesting anecdotes:

• Apple copying the graphical user interface from Xerox PARC
• The famous quote, “Great artists steal.”
• How NeXT was building web products, same as everyone else

He says all of this, while at the same time criticizing others for lack of taste and insulting their product quality.

Great products, regardless of source
To me, the way to reconcile this is that Steve Jobs cares first and foremost about great products. Sometimes the way to get there was to steal. Sometimes you reinvent and reimagine. And sometimes, you have to invent.

The point is, building a great product is about curating from the entire space of possible features you could build. Shamelessly steal ideas when they are the best ones. Ignore bad ideas even if they’re commonplace. Don’t think you have to build something totally different to make a great product.

I think this has matched with Apple’s strategy towards their most recent generation of products – though they didn’t invent the GUI, the mouse, the MP3 player, downloadable music, the laptop, or the smartphone, they’ve build some of the best products out there. (I’ll give them a lot of great for the iPad though, which is truly a new invention)

The craving for novelty in Silicon Valley
So for all the product managers and designers out there – if you are finding yourself wanting to do it differently just because, or trying to find novel solutions just because, then maybe your priorities are not in order. The goal of building great products is for you to deliver something great to the customer, not to impress your designer friends on what new layout or interaction you’ve just developed.

Make it insanely great, even while you copy, steal, reinvent, or invent whatever you need to make that happen.

Anyway, it’s a great interview and I think everyone involved in tech products should watch it.

Originally asked on Quora. If you find yourself mostly thinking about balancing satisfaction versus virality, you’re probably doing it wrong. The Quora question is a false dilemma, because it asks you to choose between satisfaction and virality, and then quantifying the tradeoff. Most of the time, if you’re working on naturally viral products, you spend most of your time elsewhere. The world of product decisions is more like:

That is, you have features in your product that either drive growth or don’t, and you have features in your product that either really help the value proposition, or don’t. These are actually pretty independent factors and you can build product features that hit each different quadrant. For example, if you are building a product like Skype, finding your friends and sending invites is clearly a high value prop, high virality action. After all, you can’t use Skype by yourself. But if you take the exact same feature, and try to bolt it onto a non-viral product like, say, a travel search engine, then you’re just creating spam. There’s really no great reason to “find friends” in a travel product, though it might be useful to share your itinerary. A feature that’s high-value in one product is spam in the other. And if you think about each quadrant, you get something like this: Let’s talk about each bucket:

• Awesome features grow your product and also people love them. The Skype “find friends” feature is a great one, but so is Quora’s “share to Twitter” feature. After I write this post, I want people to comment and upvote, so something that lets me publish to my audience, which is both viral and part of the value prop is awesome.
• Do it anyway features are just the core of your UX. Writing on walls on Facebook may not be inherently viral in themselves, but it’s important to the product experience, keeps people coming back, and indirectly helps drive the virality of the product. The more people you have coming back, the more changes you have for them to create content or invite people
• Spam features are high virality actions that your users don’t really want to do, and don’t add to the product value prop. I think this is the bucket that the tradeoff lives of a question like, “should I be viral, or offer a great product?” If you are spending a lot of time in this quadrant, then you are shaky ground.
• WTF needs no explanation

Ideally, you want to pick a proven product category that’s naturally viral and high-retention, for instance communication, publishing, payments, photos, etc. – and then spend as much time building awesome features that both drive growth and also make your users happy. Stay away from spam features as much as you can, or use them sparingly lest your product becomes spam.

[UPDATE: I have taken a much longer and more comprehensive whack at this problem in this deck:

How to build a growth team (50 slides)

Here, I answer a couple important questions:

• Why create a growth team?
• What’s the difference between a “growth hacker” and a growth team?
• What’s the difference between growth and marketing/product/whatever?
• Where should growth teams focus?
• I’m starting or joining a growth team! What should I expect?

Hope you enjoy it!

And for the previous answer, which I typed up on Quora some time ago, you can read below.]

So what does a growth team work on day to day? I would break down what a growth team does into two major buckets:

1) Planning/modeling
2) Growth tests.

Let’s dive in, but starting with the usual caveat – you need a killer product before you should start working on growth.

But first, you need a great product
Let me note that if people aren’t using your product, then you’re wasting your time spending too much time optimizing growth. You need a base of users who are happy and then your job is to scale it.

With that caveat in mind, let’s start with the planning activities:

Planning and model building
The planning/modeling side of things is really about understanding, “Why does growth happen?” Every product is different.

• You might find that people find you via SEO and then turn into users that are retained via emails
• You might find that people come to your site via web and then cross-pollinate to mobile, and that’s the key to your growth.
• You might find you need to get them to follow a certain # of people.
• You might realize they need to clip a certain # of links to get started.

These are all things that are product-specific, so I can’t give specific advice in this answer, but this is the foundation for understanding why your product grows. You can come up with a model by looking at your flows for how users come into the site, by talking to users, and by understanding similar products. You can look at successful users and unsuccessful ones.

Once you have a good model, you can create more specific criteria in evaluating the outcome of a good or bad growth project. Your mental model doesn’t have to be perfect at first- the goal is just to get started. As you execute your project successfully, if your growth goes up, then your confidence will grow. (Or you’ll have to revisit things if you keep improving that one metric significantly but overall signups doesn’t go up)

At a more tactical level, eventually this model gets more fine-grained and you can start thinking about individual things that you can change to increase overall growth. Ideally you can model a lot of this in a spreadsheet so you can do scenario-planning around what works and what doesn’t.

The goal is to create some kind of feedback loop that results in sustained growth. Maybe you buy ads, make money, and then reinvest even more in ads. Maybe you get people to create content, driving SEO, which brings in more people that create content. Or maybe you have something invitation based. The important part is to model this process and its component parts.

Project execution
Once you have a model for how to drive your growth, the next part is to actually come up with a bunch of project ideas that can make those numbers go up and to the right. Ideally you can do lots of A/B tests for pretty short ideas that prove out the concept. If it works out, then keep investing.

For something like this, you’ll need a bit of A/B testing infrastructure, a lot of creativity, and some dedicated engineers to get the tests out there.

Because the majority of A/B tests don’t do what you want (maybe the number is <30%) as a result, you’ll want to have many, many A/B tests going at the same time so that you get a couple winners every week. Sometimes people do 1-2 A/B tests per week and then complain that it doesn’t work for them – they probably need to 5-10X their A/B test output in order to get a win or two per week.

To execute each growth project, you may also need to develop some instrumentation around tracking where users come from, and what they do. This can be a bunch of SQL databases and reporting at first, but might move to something fancier later on.

Eventually, the results of these tactical projects feed back into the uber model – you have to constantly reevaluate your priorities and understand which places in the product are the most leveraged in driving growth. So there’s a feedback loop of jumping from the strategic to the tactical, and back.

Summary
To summarize the above:

1. Have a solid product where your users are happy
2. Coming up with a model for how your site grows
3. Trying out ideas and deploying them as A/B tests
4. If the site grows, then try out more ideas. If it doesn’t, rethink the model in step 1 because it might be broken

Hope that helps.

Answered originally on Quora here.

Question: For online/mobile consumer services, in what scenarios does high organic user growth not imply product-market fit?

There’s been a bunch of recent examples of products that grow quickly but have little to no retention/engagement.

The reason is that in this context, you can think of products as having 2 main components:

• Distribution tactics: This is the viral loop – the flow within the site that generates invites, embeds, links, or otherwise exposes new users to the product. Example, for Skype, you can through to a process of inviting and build your addressbook – this generates invites.
• Product experience: The actual usage patterns of the product. For Skype, that’s chatting or talking over VOIP.

In the case of Skype, the viral loop easily flows into the product experience – as a result, you have a nice product that’s both viral and engaging. This is the good case.

Let’s talk about the dysfunctional cases though:

Viral design patterns that don’t make sense for a product
Sometimes though, you end up with a viral loop that’s pretty different/weird compared to the core product experience. For example, there’s a few design patterns that have been viral in the past:

• Filling out a quiz and comparing yourself to others
• Sending a gift or a poke to a bunch of people and then asking the recipient to poke/gift back
• Finding friends and sending invites
• Getting a notification saying that someone has a crush on you and making you fill out email addresses to guess the crush – these emails then generate the next batch of notifications
• … and newer patterns like the Social Reader design pattern on Facebook, or something like spammy low-quality SEO content, which isn’t viral but is the same kind of idea.

(Note these are less effective these days since they’ve been played out – I write about the idea of people becoming desensitized to marketing here)

Because finding a really effective, working viral loop can be rare, sometimes people build a viral loop and then bolt a product onto it. This can be done in a haphazard way that shows a lot of top-line growth but fails on retention/engagement.

Disjointed viral + product experiences
The problem that sometimes, after completing the viral actions, the experience of then using the product is too disjointed, and users bounce right away. For example, you couldn’t put a “find your friends” invitation system in front of a search engine. It doesn’t make any sense. Search engines aren’t social.

The way you could validate this was happening is just to look at the underlying stats past the top-line growth:

• After signing up, how many users are active the next day? Or the next week?
• How many users bounce after the initial viral flow?
• How aggressive is the viral loop, and do you allow the user to understand and experience the underlying product?
• How well does the viral loop communicate that it’s part of a larger, deeper product?
• Does the viral loop makes sense within the context of the product? Does completing the viral loop make the resulting product experience better?

I would look at any new product and ask the above questions to understand what’s going on. In the success case, you have a lot of retention and engagement, and the more viral the product, the stickier it gets. And ideally the design of the viral loop is very “honest” as to how it fits into the rest of the products.

Business models are important, but today they’re commoditized
Let me first state: Business models are important. Of course businesses have to make money, that’s a given. But that’s not my point – my point is:

Business models are a commodity now, so “how will they make money?” isn’t an interesting question. The answers are all obvious.

So when you see the next consumer mobile/internet product with millions of engaged users, let’s stop asking about their business model expecting a clever answer – they’ll have dozens of off-the-shelf solutions to choose from – and instead, let’s start asking about the parts of their business that aren’t commoditized yet. (More on this later)

Outsource your monetization
Between the original dotcom bubble versus now, a lot has changed for consumer internet companies. Thankfully, monetization is now a boring problem to solve because there’s a ton of different options to collect revenue that didn’t exist before:

• There’s 200+ ad networks to plug into
• Payment providers like Paypal, Amazon, Stripe
• “Offer walls” like Trialpay
• Mobile payment solutions like Boku
• … and new services coming out all the time (Kickstarter)

Not only that, consumers know and expect to pay for services, something that was novel back in the late 90s. If you offer some sort of marketplace like Airbnb, they’ll expect a listing fee. If you are making a social game on Facebook, they’ll expect to be able to buy more virtual stuff. They’ll expect to pay $0.99 for an iPhone app.

Contrast this with the dotcom bubble, in which you were creating brand new user behavior as well as building these monetization services in-house. In eBay’s case, people just mailed each other (and eBay) money for their listings. Small websites had to build up ad sales teams in order to get advertising revenue, instead of plugging into ad networks. Building apps for phones involved months of negotiation with carriers to get “on deck.” At my last startup, an ad targeting technology company, we encountered companies like ESPN which had written their own ad servers because they didn’t have off-the-shelf solutions when they first started their website back in the late 1990s.

Let me repeat that: They wrote their own ad server as part of building their news site. And that means they had engineers writing lots of code to support their business model rather than making their product better.

Product experience renaissance
Let’s be thankful that we don’t all have to build an ad server every time our Ruby on Rails app is successful. This lets consumer product companies focus on what they’re best at. Also, building a new website doesn’t require $5M anymore. The number of risks in getting your company off the ground are vastly reduced when you combine cheap server hosting, an open source software stack, and multiple bolt-on revenue streams.

This frees us up to be able to work on what’s really important: Building and marketing great products.

These days, the primary cost for any pre-traction company is the apartment rent of the developers who are coding up the product. The profitability of any post-traction company is just based on how fast the team wants to ramp up headcount. If a team can hit product/market fit, a lot of other problems are taken care of.

The lesson behind Facebook’s $3.7B in revenue
Once upon a time, I was skeptical about Facebook’s business model because they received a mere 0.2 cents in advertising revenue per pageview they generated. In 2006, I calculated that maybe they could generate $15M in revenue per year maximum – a nice business, but not a world-changing one. I wrote about this topic here: Why I doubted Facebook could build a billion dollar business, and what I learned from being horribly wrong.

As I wrote in my post, it turns out I was wrong, and Facebook in fact generated $3.7B in 2011 and will generate more than $5B this year. I was wrong in an interesting way though – it turns out that they didn’t dramatically increase their revenue per pageview, but rather they just grew and grew and grew, to ~1 trillion pageviews/month. My mental model was all wrong.

In fact, we have a lot more experience with advertising and transaction based models. It’s pretty clear that an engaging social website will have 0.1% to 0.5% CTRs on their ads, and net an average $0.50 CPM. If you sell something, or have a freemium site, then you can expect 0.5% to 1% of your active users to convert. There’s lots of benchmarks out there, which I discuss in this older blog post. The point is, if you have the audience, you can find the revenue – it’s getting the big audience that’s the main problem.

The last dotcom bubble conditioned many of us to think about a different world than the one we face today. In 1997, there were a mere ~100M users on the internet, mostly on dialup modems. Let me repeat that: The entire dotcom bubble, with all of its bubbly goodness, was based off of 100M dialup users. Compare that to today, where we have 20X that number, over 2 billion users on broadband and mobile. The graph, courtesy World Bank via Google, is incredible.

The point is, the consumer market has grown by so much that the upside opportunity is tremendous if you get a product exactly right. Given all the growth opportunity, and given the plug-in revenue models, the main bottleneck for building a great company doesn’t seem to be the business model at all.

In fact, the business model seems like a second or third order problem. So again, I argue, let’s stop asking about it.

At over 450 million uniques per month, let’s stop wondering what Twitter’s revenue model will be. Obviously it will be some form of advertising, and maybe they’ll experiment with freemium or transaction fees somehow. You can debate if you think they will ultimately be a $100B company or a $10B one, but let’s skip the conversation on whether or not they’ll fail because they don’t have a business model.

The new question to ask
If you agree with me that business model is no longer a first-order question, then what’s the real question to ask? The thing that makes the business model work is really about getting to the scale where the business model becomes trivial.

Let’s ask a more important question:

Could this product engage and retain 100s of millions of active users?

For the first time ever, hitting 100+ million active users is actually realistic. First off, how incredible is that? In recent years, many startups have done it, such as: Zynga, Facebook, Twitter, Groupon, Linkedin, etc. I think we’ll also see Dropbox, Pandora, and others get there too.

For an early stage company, asking this question is really just a test of the team’s ambition, their initial market, and an evaluation of their product/market fit. Obviously if their product isn’t working, they won’t even be close.

Once a startup has product/market fit and is scaling, then the answer to this question revolves around marketing and technology competence. Also, the product might have to evolve as the initial market gets saturated- like Facebook with college and Twitter with their early adopter audience.

To sum this all up:

• Making money as a business is important, but commoditized
• You can plug into 100s of options for monetizing an audience, if you have one
• We’re working with 20X the internet audience compared to the dotcom bubble, and 1/10 the cost of starting a company
• Facebook is hitting $5B in revenue via sheer growth, not monetization innovation
• You should aim to hit 100 million active users, and get an off-the-shelf monetization solution later
• Evaluate new companies on market size and ability to grow to 100 million actives, rather than monetization methods