Diligence at Social Capital Part 2: Accounting for Revenue Growth
In the previous post of this series we looked at growth accounting for users by showing monthly active user (MAU) growth accounting and how it could uncover different underlying dynamics for a growing user base. Today we will take that framework and apply it to revenue growth. This will be particularly useful for thinking about recurring revenue as would be exhibited in an enterprise software-as-a-service (SaaS) business or in a consumer subscription business.
Consider a company that makes revenue via monthly subscriptions. For the sake of concreteness, let’s consider a B2B SaaS company. For such a company, in addition to understanding MAU growth we will also want to understand the growth of monthly recurring revenue (MRR). Note, we’d still likely be interested in MAU accounting. If a customer falls out of MAU it is likely that they will also fall out of MRR. For now, let’s focus on the MRR growth. In this case, the most common figure we see is a cumulative MRR chart going up and to the right.
Similar to the MAU case we will now break down the components of MRR growth. There is some added nuance in this case. In MAU accounting a user either comes back or does not (i.e. is retained or churned). When counting dollars, a single customer may be retained or churned but they may also be retained as a customer by spending more or less in the second period relative to the first. As such, we separate out expansion and contraction into the growth accounting. The explicit identity is:
MRR(t) = new(t) + retained(t) + resurrected(t) + expansion(t)
MRR(t - 1 month) = retained(t) + churned(t) + contraction(t)
Note that if a customer spends $10 in the first month and $12 in the second month we are counting $10 as retained spending and $2 as expansion spending and similarly for contraction. We are counting dollars as churned only if the customer went to zero dollars spent and resurrection only when the user comes back from a churned state. The above can be rearranged as follows:
MRR(t) - MRR(t - 1 month) = new(t) + resurrected(t) + expansion(t) - churned(t) - contraction(t)
Which are the five components illustrated here:
Once again, we compute the dollar based “quick ratio” which comes out to between 1 and 1.5 depending on the month and the dollar based retention rate which is here ~40%.
Recall that in the consumer MAU case, a quick ratio of 1.5 was about average for a consumer business. For recurring revenue however, this is not so good.
Recurring subscription revenue is default-retained in contrast with recurring visitation which is default-not-retained. As such subscription revenue tends to have a much lower churn rate and higher quick ratio.
To give more examples, if you think about consumer subscription businesses (such as Spotify or Netflix), they generally shouldn’t churn much and this should be reflected in a high quick ratio. Comparatively, pure consumer transactional retail businesses (such as, say, Nordstrom online) tend to churn a lot on a monthly basis as customers don’t have a strong pull to purchase on successive months. If you consider subscription businesses that rely on landing-and-expanding (such as Slack) you would hope to see a lot of expansion revenue as each customer expands the number of paid seats.
For enterprise SaaS companies, we prefer to invest in companies with a quick ratio greater than 4.
If you have a quick ratio of <2 then your churn is probably too high and you have something to fix. For more information about how this fits into our overall approach to enterprise saas investing, see Mamoon Hamid’s deck on the topic from early 2015. Here’s the key slide from that deck illustrating some actual SaaS companies.
The two companies on the right are in our portfolio and we passed on the two on the left. Note that the top left Company A has a pretty good story. Company A would say that they have a ton of expansion revenue which definitely suggests some really good aspects of product-market fit. However, it’s largely eaten up by contraction revenue leading to a situation where they have to generate expansion MRR just to outrun contraction MRR to generate net growth.
I also recommend Bobby Pinero’s post on this topic discussing SaaS metrics metrics that they used in Intercom’s fundraising process.
Accounting for Everything Else
Now that we’ve shown growth accounting for both MAU and MRR it should be clear that we can do this accounting for any quantity that is important for the business. For example, say you have a social network consumer app and perhaps you think that MAU isn’t stringent enough and you really want users to be in your app every day. In that case, merely measuring a user as active or not is not sufficient and you’d like to know if a user was really active vs. only slightly active in the month. This can be accomplished by measuring days active in the month which was dubbed L28 at Facebook. For instance, a user of L28=10 was active 10 days of the last 28. If you sum up L28 across all users in a month (as measured on the last day of the month) you get the total sum of DAU across the month. You could compare the total sum of L28 in one month and compare it to the next month and do growth accounting on it. A user expands/contracts if they have a higher/lower L28 this month relative to last month and so on. This would provide a “monthly growth accounting of DAU”.
If you’d like an example unrelated to active users or revenue, consider something like link sharing on Twitter. If you are working on link sharing at Twitter your goal is probably to grow link share events and you could use the above approach to account for the growth in link share events by users who are sharing more/less than they were in a prior period.
To review, growth accounting is a framework that can be used in just about any situation where a group of users/customers are accruing value to your business in some form or another (revenue, their attention via DAU, contributing content to the system, etc). One clear shortcoming of growth accounting is that the churn figures don’t specify whether the churning customers are recent additions to the business or if they are older customers who have been getting value for a while. That is to say, this approach doesn’t provide a very clear picture on the life-cycle of users. In order to clarify that we should look at cohorts and how lifetime value is realized. We’ll do that in the next post.
Edit: For reference, here’s the full table of contents.
Published in Startups, Wanderlust, and Life Hacking