[Note from the author: See an update to the thinking presented in these articles in my more recent writing on A Quantitative Approach to Product Market Fit and the follow up Unit Economics and the Pursuit of Scale Invariance. I no longer work at Social Capital, if you want to reach me you can email me at firstname.lastname@example.org]
Here at Social Capital we spend a lot of time conducting diligence on potential investments. In the next few weeks we’re going to discuss how we think about quantitatively assessing product-market fit. The approaches we discuss here are useful beyond diligence for investment. Many of our portfolio companies use these concepts as part of their operating process to keep tabs on their growth and evolving product-market fit. Hopefully you find it useful!
Diligence is designed to help us understand both what currently exists and what may come to be as well as to try and help the entrepreneur on their path. While we definitely talk to many entrepreneurs in the pre-product stage, most of our time ends up being spent with entrepreneurs who already have a product with users/customers. In this case, part of diligence involves developing an objective understanding of demonstrated product-market fit. While every company is different, we have a few standard ways of looking at core traction metrics and we’d like to share these with you in the hopes that it helps to give you insight into your own business. It should go without saying that there are many other aspects to diligence including, but not limited to, team, market, vision, competition, core technology, etc. and that we will only be discussing a particular subset of diligence in these posts.
A tentative outline for how we will present our approach is as follows:
- Accounting for user growth
- Accounting for revenue growth
- Empirically observed cohort lifetime value (revenue)
- Empirically observed cohort lifetime value (engagement)
- Depth of engagement and quality of revenue
- Epilogue: The 8-Ball and GAAP for Startups
These topics apply in slightly different ways for consumer businesses vs. enterprise SaaS businesses so we’ll generally treat them separately, but the framework applies to both types of business.
Today I’m going to focus on the first of these topics.
Accounting for User Growth
To get started, let’s pretend we have a consumer company that intends to get lots of users via some novel social/mobile/content product strategy. For these types of companies the most common graph that we see in pitches is a graph of users going up and to the right. Sometimes companies try to show us a graph of “cumulative registered users”, which is clearly a vanity metric. A user who has registered and is not active in your product is probably not getting much value and is probably not a good indication of product-market fit.
Given that cumulative users is rarely shown in serious pitches, in the current era we typically see 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. However we always go one level deeper to understand the nature of the growth. For the purposes of illustration, let’s pretend that we have some sort of mobile application. For the definition of “active” you should use whatever definition best encapsulates active for your application. This can be as broad as “opened the app” or as specific as “carried out a particular action”. 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 time 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.
Manipulating the above yields the following:
MAU(t) - MAU(t - 1 month) = new(t) + resurrected(t) - churned(t)
Which is to say that MAU growth receives positive contributions from new and resurrected users and receives a negative contribution from losing users to churn.
Here’s how we prefer to look at the MAU growth accounting quantities for the above fictional company. If you’re going to show us your MAU growth you’d do well to show it to us in this manner.
The bars show the three terms on the right of the equation above that contribute to MAU growth. I also overlaid two ratios that are useful. 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
This term was coined in a deck from Mamoon Hamid from earlier this year. We’ll talk more about this concept in a later post when we discuss revenue. Also, this should not be confused with the normal finance quick ratio that measures the ability of cash and near cash assets to pay off liabilities . 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 which you might assume if you only looked at the top-line number or at presumable feature/functionality changes in the past 12 months.
In terms of how the above looks, we’d classify this 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 and 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.
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. I kept the axes the same to emphasize 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.
All else being equal, the second example would be a more attractive company to us 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. Indeed, to make this operational, several of our portfolio companies implement it on a rolling 28 day basis (to remove day-of-week effects). Also, this approach works just as well with weekly active users as it does with monthly. Typically early stage consumer products already have trouble generating m/m retention, much less w/w retention so the w/w views will show very high churn and may not be useful. But if your product is extremely sticky and is already retaining at a very high level on a monthly basis then it might be time to explore generating the next level of engagement at a weekly level. For something as sticky as FB it even makes sense to get it down to the daily or even sub-daily level.
In the next post we’ll apply this framework to subscription revenue which is useful both for enterprise SaaS as well as consumer subscription businesses.
Published in Startups, Wanderlust, and Life Hacking