Why automating your SaaS KPIs is critical to CMRR growth

If you are like most SaaS and subscription business leaders, you’re ready to ditch those Excel-based models for tracking growth metrics, unit economics, and other key performance indicators (KPIs). Those models can lag weeks behind, are manual, error-prone, and lack the detail for a deep analysis of your growth levers.

While Excel is no doubt a great tool – and admittedly an accountant’s best friend – it’s just not up to task to handle the complexity of tracking and analyzing the SaaS KPIs of a scaling company’s subscription base. That's why we developed Baker Tilly SaaS Intelligence.

For far too long SaaS and subscription companies have been operating in this precarious and treacherous existence of relying on Excel to manage the SaaS KPIs that inform business leaders and as the basis for strategic decision making. All the while, even if those Excel models didn’t fall victim to the typical spreadsheets errors (studies show that ~90% of all spreadsheets contain “significant” errors), they still lacked the ability to provide clarity and expose important trends.

Many leaders still operate in the fog of concepts like generic “Expansion” and “Contraction” MRR/ARR tracking. Intuitively, they all know that “Expansion”, as a concept, is a short-hand for a variety of subscription activities that result in the increase of a customer’s subscription value such as: Add-ons, Cross-Sells, Upgrades, Upsells, Price Uplift, Foreign Exchange gains on multi-currency subscriptions. Similarly, “Contraction” obfuscates the events that led to a decrease in a customer’s subscription value such as: Downgrades, Downsells, Debooks, Price Markdowns or Foreign Exchange losses.

This lack of insight into the underlying subscription activity that explains gains and losses in recurring revenue is only further exacerbated by leader’s inability to dissect and distinguish this activity by various cohorts. To understand where and how to grow the business, you must be able to determine what’s working and what isn’t. You cannot do this with a broad-brush approach. If we’ve learned anything in the age of data science, it’s that the details and their segmentation matter.

At a minimum, you must be able to analyze your subscription activity by Customer cohort (e.g. by Acquisition Date, by Segment, by Industry Vertical) and by Product Line (e.g. Product Family) to identify where you are experiencing successes or failures in order to determine where best to take actions to accelerate or remediate.