SaaS Diligence: Growth Accounting for MAU and MRR
Companies sometimes have limited data to assess their product-market fit quantitatively. With this limited data, what other metrics can investors use to gauge product-market fit and traction in due diligence? Focusing specifically on enterprise SaaS businesses, one potential option is to use “trial” data as a leading indicator to go one level deeper to understand the potential growth trajectory and where the business is going.
44% of enterprise SaaS companies use trials to get their customers in the door. Paired with the SaaS quick ratio and growth accounting for MAU and MRR, these trial data metrics can help provide another leading indicator as a “quantitative lens” anecdotal rave reviews from customers. Here, we dig into two pieces of trial data as a leading indicator for evaluating SaaS investments.
- Using unconverted practices to predict growth trajectory
- Looking at the % of customers who convert before the trial end date to assess spikes in product-market fit
Why trials? Trials are fascinating as customers who have signed up for a test have demonstrated initial solid interest and jumped through all the hurdles to signup. In the long run, the split in terms of self-serve revenue from trials versus direct purchase can be as high as 50/50 for enterprise SaaS businesses making it a critical indicator versus other onboarding funnel metrics.
If you had an enterprise SaaS company with a solid net new MRR growth, low churn, and MoM high degree of trial starts (where the unconverted trials would not be reflected in net new MRR), it could mean the company has a high growth trajectory down the road and is more interesting in the long-run.
Below we dive into this in more depth using two pieces of trial data that can serve as leading indicators in evaluating investments.
Accounting as a Leading Indicator
1 — Using unconverted MRR to predict growth trajectory — For companies, the revenue data may not exceptionally be there yet as you only have a few months-sized cohorts. However, suppose you’re getting many trials starts. In that case, it provides an interesting positive leading indicator of what may come and points to latent growth opportunities and the degree of product-market fit.
To see this, we take a fictional SaaS company and start with their MRR growth accounting of new, expansion, and contraction revenue, and then stack unconverted practices MRR on top on a cohort basis (Unconverted trial MRR calculated by # of licenses trial signed up for* ASP) to provide another layer of depth in quantitative due diligence.
Unconverted trial MRR = # of net new sets in the same cohort period that did not convert (converted practices captured in net new MRR)
Stacking unconverted trial MRR on top gives us more information on the business’s growth trajectory by seeing what money has been left on the table through the pipeline of trial starts. This isn’t captured in net new MRR because these trials didn’t convert but are also not caught in the MAUs. For enterprise SaaS companies using sets (and not freemium), you are only a user after you have become a customer.
Above, fictional companies A and B have similar new MRR and canceled MRR MoM and quick ratios. Still, once you stack on unconverted trial MRR, company B shows much latent opportunity for unconverted sets in the pipeline that isn’t reflected in its current new MRR. Given comparable above-average conversion rates, Company B’s high number of raw practices has more market interest, customer awareness, and better initial product-market fit than company A, even with <15 months of limited data. As company B improves its trial conversion rate, which companies do as they mature, it has a more exciting growth trajectory in the long run.
Zooming in, we can further split new MRR by channel into “New MRR from direct purchases” vs. “New MRR from sets” for more granularity (and to keep the new MRR direct-purchase vs. trial ratio in mind) before you stack the unconverted trial MRR data for an enterprise SaaS company. These sub-components help answer where new MRR is coming from, how growth is being driven, and the latent opportunity in unconverted trial MRR.
From an operational perspective, a company can leverage this granularity to better improve their product-market fit by understanding why the product got a customer’s attention but was unable to convert them (e.g., communicating product value, everyday customer use cases) as well as develop LTV on a cohort basis of customers that come versus direct purchases and leverage that data to understand the value of the product to different customer archetypes.
*Note that this framework looks at actual revenue data to model outgrowth trajectory.
2 — Looking at % of customers who convert before the trial end date to assess spikes in product-market fit — Another metric within-trial data to leverage as a leading indicator is the % of customers who convert before the trial end date to set points in product-market fit.
Both Company A and B have an overall trial-to-purchase conversion rate of ~30%. Typically, customers on a trial who convert do so on the day the trial expires (14-days, 30-day period), but if you dive in deeper to understand the day-by-day layer of conversion, the data tells you something different. Here, looking at a specific cohort of trial customers and which day they converted through the 14-day trial period:
In this example, Company A follows a typical conversion pattern where most trial customers convert on day 14 after a trial ends. Company B, however, spikes and has 2x the conversion into a paid customer on the Day 0 of the problem versus Company A. This is a signal that Company B’s product can get to that “wow” moment much earlier, potentially indicating a higher product-market fit.
Using the % of customers who convert the before trial end date (in this case, 12.5%) is a quantitative way to confirm customer validation and rave customer reviews. In this case, company B is a more compelling investment than company A given the spikes in day 0 conversion throughout the trial period.
From an operational aspect, companies can use this framework to gauge the degree of their product-market fit and how well they are communicating their product value to customers. For example, one method is to break down the exact engagement activity during Day 0 necessary to make the customer successful (e.g., setting up the account, provisioning licenses a la Twitter follow five friends magic number) to improve the onboarding flow.
Some other things to keep in mind:
-SaaS Freemium model — The other standard method for SaaS businesses to get customers in the door is the “freemium” model (e.g., Slack, Dropbox Pro). In many cases, companies use freemium. For companies who use both freemium and practices (e.g., Box, Intercom), trial data is another data point to serve as a leading indicator for “business” products versus individual premium users (e.g., Intercom Acquire vs. gated free particular user). It doesn’t address the freemium portion. In this case, using trial data as a leading indicator should also be paired with broader freemium MAU growth accounting user data for a more robust evaluation.
For companies who only use freemium, given the unconstrained “time” nature (as opposed to a constrained 14-day trial duration) and more available user data, MAUs/user engagement metrics/DAUs, are much stronger leading indicators.
Direct sales model — This framework only applies to self-serve sales SaaS businesses instead of a more heavy hands-on natural sales B2B models (Netskope, Greenhouse). Jason Lemkin’s Lead Velocity Rate (LVR) metric applies a similar logic with workable leads at a more mature stage for direct sales enterprise models. Other similar leading indicators are Marketing Qualified Leads and whether they increase over time to gauge future new MRR.
Consumer trial leading indicators
This “practices” framework could also be applied to some consumer companies, but as a leading indicator is much murkier given the broad swath of ways a consumer company can engage users. MAUs/DAUs/archetype engagement metrics are better data points to draw from.
However, one case is where growth tactics can be used as a proxy. For example, Sprig offers $10 off the first order, effectively subsidizing it — so the first order can be considered a “trial.” Using a cohort analysis, we can split all first-time users via promos as a “trial” within MAU growth accounting. From there, seeing the conversion or drop-off rate after the first order or “trial” can indicate —
1 — There is substantial market awareness and interest in the product at the top of the funnel, and there is enough stickiness to go through the signup process.
2 — But, the experience is not compelling enough, whether it is the food or delivery experience. This number can also be captured in the repeat order rate.
Like enterprise SaaS companies, we can use this additional layer of detail to split out first-order vs. recurring orders to assess the business’s growth trajectory. That said, this is a much weaker leading indicator for consumer companies compared to other engagement metrics.
Further validate the above two metrics, using a robust amount of enterprise SaaS company data, the next step is to establish the target benchmarks and standardize across a portfolio of companies to use for investment diligence the:
- The ratio of unconverted trial MRR to new MRR that applies across the board and points to a significant growth trajectory (e.g., the equivalent to SaaS quick ratio of >4)
- Exact X% conversion before the trial end date for successful companies by day X on a cohort basis as well as for each “stage” of the company (<12 months, 12–18 months)
- Validate that the above framework applies consistently across different trial models (14-day, 30-day, no credit card, credit card)
- Dig into the “net new MRR direct purchase” vs. “net new MRR trial” ratio to see if there are any exciting insights there that can be leading indicators