TL;DR: I built ChurnLens after buying a SaaS whose CIM reported 2.3% monthly churn when the raw subscription data showed 9.4% — a gap worth $340,000 that I only found four months after wiring the money. ChurnLens exists so buyers compute churn from the raw CSV instead of trusting the seller's methodology.
I'm going to tell you the worst due-diligence story I know. It's mine. If you're about to buy a SaaS business, read this before you wire a single dollar.
Two years ago, I found a SaaS business listed at $1.1M. $42K MRR. Three years of growth. The CIM showed a clean 2.3% monthly churn rate. The seller was transparent — or so I thought. He handed over a ProfitBird summary, cohort charts, and a churn dashboard screenshot. Everything looked healthy. My due diligence checklist had 40 boxes. I checked all 40. The deal closed.
I was a careful buyer. I'd read every SaaS acquisition guide. I knew the right questions to ask. I asked them. I got good answers. I trusted the numbers because the numbers were presented by someone who understood how to present numbers.
"The seller's churn methodology is standard. The CIM number is close enough. If there were a material gap, my 40-point DD checklist would catch it."
Four months post-close, MRR started declining. Not crashing — bleeding. Slow, steady, the kind of decline you don't notice until it's too late. I pulled the subscription data and ran the churn calculation myself for the first time.
How did the seller get 2.3% when the real number was 9.4%? He hadn't lied. He'd used every legitimate-looking methodology trick available: he excluded downgrades from churn (revenue disappeared but the logo stayed). He reported the best cohort month as "representative." He excluded involuntary cancellations (failed payments). He counted reactivations as continuous subscriptions. Every single one of these choices is defensible in isolation. Combined, they turned 9.4% into 2.3%.
I hadn't asked for the raw CSV. I'd asked for a churn number. I got the number the seller wanted me to have. And I paid for it.
After that deal, I did something obsessive. I pulled the raw subscription CSV from every SaaS acquisition opportunity I evaluated — not the summary, not the dashboard, the raw export with customer ID, MRR, plan, status, dates. I built a spreadsheet to compute churn five different ways: logo churn, revenue churn, net churn, gross churn, and cohort-adjusted churn. I cross-referenced concentration risk. I flagged inactive paying accounts.
The churn number in the CIM is always the seller's best-case scenario. The only number that matters is the one you compute yourself from the raw data. If you're trusting a summary number someone else computed, you're not doing due diligence — you're doing trust.
I ran this analysis on 14 more deals over the next year. In 11 of them, real churn was meaningfully higher than reported. In 3, it was the same. Zero times was real churn lower. The gap averaged 4.2x. Every deal where I caught the gap, I either renegotiated the price or walked. Every deal saved me more than the cost of the analysis by a factor of 100.
The spreadsheet worked, but it took me 4 hours per deal. And I kept making the same analysis manual mistakes. So I automated it. Then I showed it to two other acquirers I knew. They asked to use it. Then their friends asked. That's when I realized this wasn't a spreadsheet — it was the thing I wished I'd had before I wired $1.1M for a business worth $760K.
ChurnLens is that spreadsheet, systematized. Upload the seller's raw subscription CSV. Get every churn metric computed five ways. See concentration risk, annual-plan churn exposure, inactive accounts, and a composite Revenue Quality Score — in minutes, not hours.
If you're evaluating a SaaS acquisition right now, you probably hold at least one of these beliefs. I held all three. They cost me $340K.
Get the 23-point buyer-side churn audit checklist. Ask the seller for the raw CSV before you trust any summary number.
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Hidden churn is revenue decay that headline metrics conceal: customers on annual plans who have already stopped using the product, paid accounts sitting inactive, or revenue concentrated in a few logos about to leave. A SaaS business can show flat MRR while its real retention is collapsing. ChurnLens surfaces these signals before you buy, so you price the deal on true revenue quality.
ChurnLens analyzes five dimensions: revenue concentration, logo retention, annual-plan churn risk, inactive paid accounts, and MRR decline patterns. Each is weighted into a single 0-100 revenue-quality score benchmarked against comparable SaaS businesses. The score tells an acquirer whether reported MRR is durable or propped up by customers who are one renewal away from leaving, all before the deal closes.
Purchase price is usually a multiple of recurring revenue, so overstated retention directly inflates what you pay. A business with 20% hidden annual-plan churn is worth far less than its MRR implies. Buyers who skip churn diligence discover the decay only after closing, when it is too late to renegotiate. ChurnLens gives that visibility during the evaluation window instead.
Watch for revenue concentrated in a handful of accounts, a widening gap between signups and active users, annual contracts that never renew, and MRR that grows only through discounting. Each pattern signals fragile revenue. ChurnLens automatically flags these red flags from uploaded revenue data and ranks them by how much they threaten the durability of the recurring revenue base.
| Risk dimensions scored | 5 |
|---|---|
| Revenue-quality score range | 0-100 |
| Built for | Acquirers, PE, founders |
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