ChurnLens FAQ
How do I detect zombie MRR?
Zombie MRR is recurring revenue from customers who have stopped actively using your product. Signs: declining login frequency, falling API calls, reduced seat count, support tickets going quiet. ChurnLens's zombie detector surfaces these.
Detailed answer
Zombie MRR is recurring revenue from customers who have stopped actively using your product. Signs: declining login frequency, falling API calls, reduced seat count, support tickets going quiet. ChurnLens's zombie detector surfaces these. In the context of ChurnLens (SaaS revenue quality & churn risk due diligence), this is one of the most common questions from churn due diligence tool users.
Related questions
- What is churn due diligence tool?
- How does ChurnLens differ from alternatives?
- What should I look for in a churn due diligence tool?
Why this matters
This question matters because it gets to the core of how ChurnLens works. Understanding the answer helps you make better decisions about your churn due diligence tool workflow.
Deeper context: How do I detect zombie MRR
This question — 'How do I detect zombie MRR?' — is one of the most common questions a SaaS acquirer asks during diligence, and the answer a seller provides is almost always simpler than the underlying reality. The short answer is a useful starting point, but a buyer making a seven-or-eight-figure decision needs to understand why the answer is what it is, what assumptions are baked into it, and how it changes under different definitions.
The deeper truth is that any single-number answer to this question hides more than it reveals. The right answer depends on the target's customer segment (SMB vs mid-market vs enterprise), pricing model (monthly vs annual, seat-based vs usage-based), cohort vintage (are newer customers retaining better or worse than older ones?), and the definitional choices the seller made when computing the number they put in the data room. A benchmark range is a sanity check — the reconstruction from the revenue ledger is the actual diligence.
When you encounter this question in a live deal, the workflow is: (1) get the benchmark range to establish what 'good' looks like, (2) request the revenue ledger and recompute the metric under a standardized definition, (3) segment by cohort and customer type to find the variance behind the blended number, and (4) compare the reconstructed figure to the seller's reported figure. The gap — and in our experience there is almost always a gap — is the diligence finding.
80%
Overpay for Churn
4.2×
Real vs Reported
$340K
Avg Overpayment
23
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