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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mattan Griffel:
The Minimal Homepage

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

Here’s what I’ve noticed:

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

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

You are wrong.

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

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

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

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

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

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

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

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

Bubba Murarka on Mobile:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

image credit: SilentMode

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

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

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

Location is not a deal-breaker

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

Idea validation is extremely important

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

Prior relationship is not a deal-breaker

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The DuPont Analysis equation looks like this:

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

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

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

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

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

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

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

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

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

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

So what?

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

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

What next?

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

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

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

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

Turns out this personal business model sucks.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The idea of the zoom in pivot is:

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

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

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

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

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

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

Here are a few high-impact places to test:

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

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

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

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

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

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

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

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

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

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

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

How healthy is your SaaS business?

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

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

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

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

What this framework will give you:

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

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

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

Let’s jump in.

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

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

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

You’re probably in this stage if:

• You’ve just started.

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

• People are testing your product for the first time.

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

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

Primary Goals:

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

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

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

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

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

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

2. Deep questions about the current problem.

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

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

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

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

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

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

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

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

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

1. Very disappointed

2. Somewhat disappointed

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

4. N/A – I no longer use [product]

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

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

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

• Marc Andreessen’s post on product/market fit (this is an archived page, the original was removed from his blog).

• This breakdown of customer interviews will walk you through all the details.

• Experiment with these methods to scale your customer feedback.

• A great deck from LaunchBit on how to find customers to interview.

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

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

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

You’re probably in this stage if:

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

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

• Your monthly revenue is starting to grow.

Primary Goals:

• Consistently grow MRR while controlling churn.

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

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

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

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

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

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

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

Speaking of churn…

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

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

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

So what’s a good churn?

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

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

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

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

You’re probably in this stage if:

• Growth is beginning to slow for the first time.

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

• You’ve successfully controlled your churn.

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

Primary Goals:

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

• Get each customer to profitability within 12 months.

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

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

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

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

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

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

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

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

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

• Use this list of metric definitions from David Skok for your formulas.

• Also read through Skok’s SaaS Metrics 2.0 post which is probably the best post on SaaS metrics I’ve come across.

• Here’s a great SaaS KPI dashboard from Christoph Janz.

• Why you shouldn’t worry about applying a discount rate to your LTV as a startup (the post focuses on mobile gaming but it’s applicable to any startup).

• Some of the downsides of focusing on LTV too heavily.

• David Cancel’s framework for building a data-driven startup (with a bit more emphasis on funnels and cohorts).

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

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

2. Beginning to Scale: Monthly recurring revenue and churn

3. Expansion: Lifetime value and cost per acquisition

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

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

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

Google Trend graph for “rss” – bad news.

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

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

Growth

• Growth Hacker is the new VP Marketing
• You don’t need a growth hacker
• After the Techcrunch bump: Life in the “Trough of Sorrow”
• How do I balance user satisfaction versus virality?

Product/Market Fit

• 2011 Blogging Roadmap: “Zero to product/market fit”
• Minimize your Time to Product/Market Fit
• When has a consumer startup hit product/market fit?
• When does high growth not imply product/market fit?
• Does anyone care about your new product? (Doing market research with Google’s Keyword Tool)
• Has Facebook’s DAU/MAU always been ~50%?

Design

• How mobile startups can iterate better, faster, stronger
• Mobile app startups are failing like it’s 1999
• Don’t just design your product, design your community too
• What factors influence DAU/MAU? Nature versus nuture
• Strive for great products, whether by copying, inventing, or reinventing
• Why you’ll always think your product is shit
• Don’t compete on features
• Simple is Marketable
• Know the difference between data-informed and versus data-driven
• How to use A/B testing for better product design

Blogging

• How to write good and bad titles for your blog post
• How this blog grows: Evergreen content, Social whales, and “Don’t get bored”
• How to use Twitter to predict popular blog posts you should write

Industry and Investing

• Why are we so bad at predicting startup success?
• I’m a Google Glass skeptic and think it’ll be the next Apple Newton
• Why it’s hard to evaluate new social products
• Confessions of a Startup Seagull
• My friend Noah and his $100M lesson after being fired from Facebook
• Career Suicide versus Startup Suicide
• Stop asking “But how will they make money?”
• What makes Sequoia Capital successful? “Target big markets”
• The Law of Shitty Clickthroughs
• Visual Basic, PHP, Rails. Is Node.js next?
• Why I doubted Facebook could build a billion dollar business, and what I learned from being horribly wrong
• How sheep-like behavior breeds innovation in Silicon Valley
• “Anyone can start a Groupon!” and other startup myths
• When Does Paid Acquisition Work for SaaS Startups?
• Stanford CS major seeks sales/marketing monkey

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

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

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

Ouch.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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/