Understanding evolving ‘product-market-fit’ can make the difference between a thriving or dying startup. It is important for startups to keep a tab on their growth.
Every startup is different. But there are a few standard ways of looking at core traction metrics. One of them is “User Growth Accounting” that’s shared in this post.
User Growth Accounting Process
Let’s say there is a consumer tech startup that intends to get lots of users for its app.
For these types of startups the most common graph seen in investor pitches is a graph of users going up and to the right.
Sometimes startups try to show a graph of “cumulative registered users” or “total downloads”. These are vanity metrics.
A user who has downloaded an app and is not active in your product is probably not getting much value.
And is probably not a good indication of product-market fit.
A better way to show is a graph of monthly active users (MAU) going up and to the right.
This is showing 16 months of roughly 12% m/m growth which is quite impressive.
Understanding the nature of the Growth
Consider the following two accounting identities.
MAU(t) = new(t) + retained(t) + resurrected(t)
MAU(t – 1 month) = retained(t) + churned(t)
The first one says that active users today (for the trailing 30 days) are either new users, retained from the previous month or resurrected from some prior period.
Note that this is a mutually exclusive and completely exhaustive classification of current users.
The second identity says that the MAU from last month either came back and were retained or did not and thus churned.
Putting them together:
MAU(t) – MAU(t – 1 month) = new(t) + resurrected(t) – churned(t)
Which means that MAU growth receives
- Positive contributions from new and resurrected users
- Negative contribution from losing users to churn.
Here’s an even better way to look at the MAU growth accounting quantities for the above company.
The bars show the three terms on the right of the equation above that contribute to MAU growth.
What influences growth?
Two ratios help us understand this even better.
The retention rate is month-over-month so this says that our fictional company has a retention rate of ~40%.
Note that because of the accounting identities above, this means that the churn rate is (100% – 40%) = 60%.
The other ratio is (new + resurrected)/churned which is to say the ratio of the area above the line vs. below the line.
This ratio needs to be greater than one if the app is to be growing, otherwise churn is overwhelming growth. We call this the “quick ratio”.
Quick Ratio = (new + resurrected)/churned
For this company the MAU quick ratio is fluctuating between 1 and 1.5. Which is to say that for every 3 new users the company adds it is also losing 2–3 users to churn.
Note that this gives much more information than the MAU graph above.
This is telling us that there is some large month-to-month churn which is being overcome by large contributions from new and resurrected users.
The retention rate has been stable and not showing any particular trend.
On the plus side, it’s not going down as the app is growing, but it’s also not getting any better.
In terms of how the above looks, it’s generally classified as a so-so situation for a consumer app.
Most consumer applications don’t have a very strong mechanism to bring users back month after month. So the quick ratio tends to be just above 1.
The dynamic for each month in a consumer app is typically to add a bunch of users.
And to simultaneously lose a bunch of users with a small additive piece on top from resurrection yielding overall small positive growth.
Why this makes a difference?
To show you the power of this view, there are other ways that these components could conspire to produce the same top-line growth. For instance.
Note, these numbers would produce exactly the same MAU graph shown above.
The axes have been kept the same to emphasise the difference.
In this case, the new component isn’t growing as fast but it’s also retaining much better.
The quick ratio for this company is more in the 1.5–2.0 range which would be very good for a consumer company (for every 3 customers that you are gaining you are losing between 1.5-2 customers, much better than the first company).
Note the spikes in resurrected possibly due to some resurrection campaigns which were not accompanied with a corresponding spike in churn.
The Winning Difference
All else being equal, the second example would be a more attractive company to investors because it is starting from a better base.
With such high retention it would be worth trying to push harder on the top of funnel with new users to drive growth (more aggressive sharing/referral mechanisms, paid acquisition, etc.).
For the first example it’s harder to justify pushing on new users as you would end up losing many of them.
It’s easier to fill the top of funnel than it is to fix some underlying churn problem.
It should also be clear that this accounting can be done on time-frames other than calendar months. This approach works just as well with weekly active users as it does with monthly.
For something as sticky as Instagram it even makes sense to get it down to the daily or even sub-daily level.
Original posted at: https://medium.com/swlh/diligence-at-social-capital-part-1-accounting-for-user-growth-4a8a449fddfc
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