Growing growth

Now, you might be thinking: “But Alejandro, wait! If every company has a churn rate, and churn rates limit growth, how do some companies achieve hockey stick growth?”

To which I would respond: “Because their growth is growing.”

There are several ways to increase growth: increasing the marketing budget, optimizing conversions, and creating referral programs can all contribute to viral growth. Let’s analyze the case of viral grow, in which the number of new customers is affected by the company’s total number of active customers. In other words: more customers on the system equals more people referring new customers equals more new customers.

Let’s say that the company is growing virally with a constant (K) factor of 0.20 and that the formula we have applied to calculate the number of new customers is:

New customers (month) = k * Total number of Customers (month-1)

Now, let’s visualize the same example as before (1000 new users per month @ 90% retention), but this time, we’ll throw in some viral grow (with K = 0.20).

From this cohort analysis graph, there are two key takeaways: firstly, a constant factor of 0.20 has caused a 1000% increase in the total number of active customers (~90,000) after 24 months; and secondly, the system is still growing after 24 months–it didn’t reach a saturation point.

So, to compensate for our 90% retention rate, we need to create mechanisms to grow our growth every month.

Now, at this point, you might be saying: “Wow, Alejandro: viral growth is clearly more important than retention. Look at how it’s affected our customer base!”

To which I would respond: “Not so fast.”

Let’s analyze one more case. Our good ol’ cloud computing startup, but with a 50% retention rate. We’ll stick with 1,000 new users per month and a viral growth rate K = 0.20. But regardless of the virality, our company is performing really badly, losing 50% of our customers on every cohort, every month.

After 24 months, our company only has 3,000 active customers instead of 90,000–that’s a 30x difference! Retention truly is key.

But why does retention have such a powerful effect? In short: Because viral growth depends on the number of active customers, so if we keep our users for longer, we’ll have more referrals.

To recap:

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  • Generally speaking, churn limits growth.
  • Retention increases viral growth.
  • Good retention and viral growth are prerequisites for scaling a company to millions, or even billions of users.

A final word on churn rate analysis

It’s pretty common to see more customers cancel a service during the first month of use than later on. That’s why in the following simulation, I provide you with two retention rates: the First Month Retention Rate and the Long term Retention Rate. Using these parameters in our calculations will lead to more precise results.

Conclusion

The purpose of this cohort analysis tutorial wasn’t to give you a detailed class about metrics and cohort analytics; in fact, others discussed the complexity of these statistics in far more depth. Instead, I want to awaken you to the importance of this type of analysis and, more importantly, to show readers their own revenue cohort analysis examples and churn rates with my open source cohort analysis software solution.

If there is just one question to wake you up, it’s the following:

How much of your actual revenue comes from users that started working with you a year ago?

How to do your own cohort analysis

Now it’s your turn! There are two ways to analyze your own business’s retention and churn:

  1. Upload your PayPal Data to the tool I’ve deployed. For full disclosure, please note that by using this tool, your log file will temporarily be placed on a server for processing (deleted as soon as the data is displayed). However, if you prefer, you can always…
  2. Download the open source code and deploy the tool yourself. The README contains detailed instructions for how to do so. If you don’t have a PayPal account, you can hack the code easily to analyze other types of accounts.

Alternatively, you can play around with our simulator and visualize startup growth based on all the parameters discussed above.

Thanks for reading!

The Cohort Analysis simulator can be found at the bottom of the original article, which can be found here. Please also note that some users have had issues with the PayPal Data tool, due to the tool being several years out of date.

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2017-01-29T18:06:20-04:00September 22nd, 2016|Big Data, Guest Blog|

About the Author:

Andrew is a technical writer for Deep Core Data. He has been writing creatively for 10 years, and has a strong background in graphic design. He enjoys reading blogs about the quirks and foibles of technology, gadgetry, and writing tips.

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