B2B SaaS Metrics Evolution and Usage
SaaS Metrics & Benchmarks
Clayton Whitfield founded SaaSOptics to provide B2B SaaS founders and operators a platform that made it easier to capture, calculate and make better metrics informed decisions. Clayton shared that the core metrics that form the foundation of B2B SaaS company value have not evolved significantly over the 12 years since he founded SaaSOptics, but the understanding and comfort with the metrics have evolved. Clayton also highlighted that because there are no "standards" governing body in the industry, so there are multiple variations and interpretations of the exact input variables of the core metrics. The discussion evolved into a "hammer and nail" analogy, where if you only focus on one metric, say Customer Acquisition Cost Payback Period (CAC Payback Period), and do not understand the inter-dependencies of other key metrics, such as Rule of 40 or Customer Lifetime Value to CAC Ratio or even Gross and Net Dollar Retention can lead to incorrect decisions. Next, Clayton shared the importance of "Cohort" analysis, and why calculating metrics based upon groups of customers that share a common trait, such as industry or timeframe they became a customer. As an example, if Financial Services is a well-represented industry segment across your customer base, it would be instructive to understand the Gross and Net Dollar RetentionRates of all customers in the Financial Services industry that became customers in 2016 vs 2017 vs 2018, etc. Lifecycle Renewal Curves allow you to see the churn rates after annual term renewals in year two vs year three versus year four. This is an often-overlooked cohort-based metric that can directly inform Customer Success resource allocation and materially impact retention rates for underperforming cohorts. As an example, Clayton highlighted a cohort that became customers in 2017 that represented a time when the customer on-boarding process was less robust, and thus customers were churning at a higher rate than in later years after the new customer on-boarding process was enhanced. As a result, the company allocated more Customer Success resources to that cohort to re-train and support that customer cohort to course correct the lower than average retention rates for that specific cohort. Next, we discussed the difference between leading versus lagging indicators in the metrics ecosystem. Clayton highlighted why usage data, made available via a solid product analytics infrastructure can be a great leading indicator of customer churn risk. Another example of a good leading indicator is Net Promoter Score (NPS), which has a strong correlation to Gross Dollar Retention. A caveat is to measure NPS for both the buyer and the user which normally provides different NPS scores and inform you where to prioritize increased Customer Success and/or sales resources. The discussion evolved into the importance of having a well defined Key Performance Indicator framework that identifies the top tier leading indicators, by function that have a direct, causal relationship to the industry standard lagging indicators such as Rule of 40, CAC Ratio, Gross and Net Dollar Retention and Customer Lifetime Value to CAC. Finally, we discussed the stage appropriate use of metrics. As an example, we discussed Customer Lifetime Value to CAC Ratio, which is a metric that requires the understanding of customer churn over time. If a B2B SaaS company only has 12-24 months of operating industry, the churn rate is not established with any historical significance, which will result in an artificially high Customer Lifetime Value which can lead to investment decisions based upon false positives. Speaking with the founder of a B2B SaaS company, turned Chief Customer Officer who uses metrics daily to increase customer satisfaction, retention, and thus company value is a great listen!