Short version
If you’re an engineer or designer looking for a new role, and interested in working with me- please send me a note and introduce yourself. (You don’t have to be in the Bay Area).

Here’s my email: voodoo@gmail.com. I’ll reply back to you personally and we can chat more! Also, feel free to forward this on to anyone else that might fit.

Here’s the longer version :)
Dear readers,

A year ago, I wrote a post on Growth Hackers that’s now become the most popular post ever on this blog, in the 5+ years I’ve been writing it. The term has become a little bit of a buzzword, for better or worse. Thanks Sean.

One of the most frequent questions I get is, “How do I learn how to do this?” Unfortunately, there’s no book for this, and the field constantly changes. So go get hands-on experience: Write hundreds of A/B tests, design creative new landing pages and build out new sharing flows. Write the SQL and build out the analytics backend to calculate your product’s viral factor and improve it with new platform integrations. Integrate email, Facebook, Twitter, and other platforms for growth. Launch a product in Asia and supercharge it with internationalization and partnerships. Basically, go work with a competent growth team and learn as much as you can.

This stuff is hard to learn, and opportunities are rare. So today, I’m happy to announce that I’m going to be hiring talented folks for a new growth team that’s 100% focused on mobile, and I will be working with them personally on a new product. I’m focused on technical folks – engineers and designers who code – who want to learn about metrics and growth.

If you fit the profile, shoot me a note and introduce yourself. Doesn’t matter where you are in the world, as long as you can build products. If you’re in the Bay Area, even better- I’ll buy you coffee.

Here’s my email address: voodoo@gmail.com

Write me please attach a resume/CV, a portfolio, etc. I’ll write you back personally.

Thanks,
Andrew

[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.

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

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.

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.

Upcoming May 3 conference in San Francisco
My friends Erin Turner and Gagan Biyani are putting on the second Growth Hackers Conference in San Francisco on May 3rd.

Here’s more details and a discount code:
You can get 30% off with my discount code Andrew30 on the Eventbrite page here.

Conference speakers and agenda
They have a stellar agenda planned, including the following folks:

• Keith Rabois (Investor at Khosla Ventures, former COO of Square, SVP Slide, VP LinkedIn, Paypal)
• Sean Ellis (coined term “growth hacker,” CEO Qualroo, interim Marketing Executive Dropbox, Eventbrite, Xobni)
• Stan Chudnovsky (Co-founder, IronPearl, Ooga Labs, & Tickle)
• Greg Tseng (CEO and cofounder of Tagged)
• Noah Kagan (CEO & founder AppSumo, VP Marketing Mint.com, Facebook)
• Mike Greenfield (Growth Hacker in Residence at 500 Startups, Co-founder Circle of Moms & TeamRankings, Analytics at LinkedIn)
• Gagan Biyani (Co-founder of Udemy, Growth advisor at Lyft)
• Akash Garg (Director of Engineering for Twitter Growth Team, Co-founder & CTO Hi5, CTO of Bebo)
• Elliot Shmukler (VP Product & Growth for Wealthfront, Senior Director of LinkedIn Growth Team)
• Matt Hudson (Product Manager, Growth at YouTube)
• Josh Elman (Founded Twitter Growth Team, early Facebook, Zazzle, LinkedIn)

Hope to see you guys there.

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.

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!

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.

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

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

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

Let’s discuss those weaknesses.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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