// Lessons learned so far from our transition to growth at Buffer //

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Sean Ellis detailed at the Growth Hackers Conference on three key stages of growth(1).  With his experience working with various startups including Dropbox and Eventbrite, he coined a concept “Growth Pyramid”, from product market fit, then transition to growth, and eventually, growth.   

At Buffer, in recent months, we have been focused on the transition to growth.  This post intends to share some key lessons learned so far.  I hope it will be useful for other startups at a similar stage. 

Growth hacking is what you do only after you have growth. You need to prove out that you have a core value prop, an asset that people like and use. ” 

- Keith Rabois, former COO at Square  


 

(1) Define and refine product goals

"What is the core value that the product delivers?  What is the wow experience (or "aha" moment) that we want the user to experience?"

The foundation of growth is delivering real value.  Hopefully these are obvious questions that you can readily answer.  In all, it goes back to what and whose problem are you solving.  The answers should be short and simple, and can be explained to someone that is not in the domain / industry.  I find it helpful to go talk to a few friends who are not in startups, and refine after each pitch until they understand right away in one description. 

(2) Understand existing behavior

"How are people using our product right now? How are previous level of engagement leading to subsequent usage?" 

Before starting to actively track metrics, you need to know what metrics should you be tracking.  That is something we come across when we were about to make a dashboard for internal reference.  

Exploratory analysis is useful in uncovering usage patterns, similar to Facebook’s 7 Friends in 10 Days.  Andrew Chen has written up a great blog post on how to get those insights for your own startup. 

At Buffer what helped us got up to speed was whipping up a simple excel spreadsheet.  Choose any tool that is handiest for you at this stage; the goal is to make sense of the numbers quickly.  At the end of this step, you should know your company’s funnel metrics and current trends for each off top of your mind.  

(3) Segment and implement user analytics

"Which users are most active? Eg, for users that signed up on iPhone or Android, how is their behavior different?" 

Once you have a fundamental idea of how the macro level product usage looks like, it is time to look beyond aggregate data.  Segment data to answer more specific questions.  For example, when segmenting the activation rate of signups from different channels, we discovered that activation rate on our web dashboard almost halved with the re-design back in December (while iPhone activation shot up!).

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This is also a good time to implement user analytics.  Storing events for each user systematically is an investment that pays off.  For example, if you’d like to answer whether users who have received a certain email newsletter are more likely to upgrade or not, user analytics provide a straightforward way to query against an existing, nicely structured database. 

(4) Start running experiments

Velocity and data can change the company culture.  You don’t have to finish all these analysis to start running experiments.  It is a virtuous cycle where experiments can drive more understanding on the existing data as well.  We have seen speed of rolling out experiments picking up since we started to have at least one experiment running at a given point in time.  

 

Other things to watch out

 

Keep track of all questions and experiments.  Exploratory analysis involves looking into user behavior and running different types of analysis.  By definition, some of these analyses may yield interesting insights and most do not.  Don’t despair; keep a log of questions the team asked and the results for each analysis.  Often, hypotheses change in a product, user behavior can evolve and it is useful to reference back past analyses and experiments.  

Set a deadline for exploratory analysis.  There is always another way to slice the data. There is always a better way to build a churn prediction model.  Start running experiments anyway!  When transitioning to growth, think of these analyses more of hypotheses that should be revisited regularly. 

 

Is your startup also at the stage of transition to growth?  What are some of the key lessons from your transition? 

 

(1) For more extensive notes on Growth Hacker Conference and on Sean Ellis’ presentation on Growth Pyramid, check out this blog post by Sandi Macpherson on Quibb.

Special thanks to Joel Gascoigne for reading through earlier versions of the draft. 

Background to Sean Ellis’ “stages of growth” chart, credit to Thundermark