Churn Risk & Retention Measurement for B2B SaaS
Apex Analytics identifies which accounts are at risk of churning, what behavioral signals are driving that risk, and whether your retention plays are actually moving the number. We work on data you already have.
Book a 30-Minute CallMost SaaS companies know churn is a problem. Few know which accounts are about to churn, what's driving it, or whether their retention plays are actually helping. We work through all three, in sequence.
We model churn risk across your account base, identify the behavioral drivers behind it, and deliver a prioritized list of accounts to act on. You get a clear picture of what's driving risk, not just a score.
Typical timeline: 2 weeks
Once you're intervening, we measure whether it's working, and for which accounts. Standard reporting conflates correlation with causation. We use causal inference to separate signal from coincidence and tell you where to double down.
Typical timeline: 2 to 3 weeks
Predict. Intervene. Measure. Repeat. We embed into your retention motion as an ongoing analytical layer, so every quarter you're making smarter decisions than the last.
Ongoing, with monthly reporting
We model churn risk across your account base using behavioral signals: onboarding completion, feature adoption, seat utilization, and more. You get a ranked list of at-risk accounts and the specific drivers behind each one.
Risk scores tell you who. Behavioral drivers tell you why. Together, they tell you where to intervene, and with what. We hand you a prioritized action list your CS team can work from immediately.
We apply causal inference methods to measure whether your interventions are actually working, not just correlated with retention. You find out what's earning its budget and what isn't, with revenue impact attached.
The Full Pipeline
See both analyses run on Loopify, a fictional B2B SaaS company, using simulated data and real methodology.
Loyalty program
Over-credited
19 pts of reported churn reduction was noise
Onboarding sequence
Under-credited
Performing better than it looks
Save offer
Accurately measured
Reported and causal estimates align
This pattern shows up consistently: programs that target high-risk accounts get under-credited by standard reporting, and programs that run on self-selected low-risk accounts get over-credited. Standard measurement steers budget in the wrong direction.
Apex Analytics was founded on a simple observation: most SaaS companies are making retention investment decisions based on numbers that don't mean what they think they mean. Churn went down after we launched that sequence. Retention is higher for customers who use the feature. Those correlations feel like signal. They usually aren't.
Our background is in applied econometrics and causal inference, the methods developed specifically for measuring true cause-and-effect relationships in observational data. We built Apex Analytics to bring that rigor to retention, starting with identifying which accounts are actually at risk, and following through to measuring what actually reduces churn.
We run a churn risk diagnostic on your data in two weeks. You get a prioritized account list, the behavioral drivers behind it, and a clear path to intervention.