The Review Pattern That Did Not Match the Churn Numbers

The situation

The exclusivity period was set to expire in nine days, and Anh had built the whole sale process around that date holding. Anh had spent fifteen years growing a chiropractic clinic group from a single Morrisburg location into a nine-clinic operation across the region, and the last eighteen months had been difficult: two clinics had lost their lead practitioners, patient volumes had softened across the group, and revenue for the current year was tracking meaningfully behind the prior one. Selling now, before the trend lines got worse, had been Anh's own decision, made with eyes open about the business's recent performance.

Siran led the buy-side diligence team for the acquirer, a platform that had spent the past several years rolling up healthcare services businesses across Ontario. Siran had come to acquisitions after a first career as a construction project manager, and it showed in how the diligence process ran: methodical, checklist-driven, and unwilling to move a deadline just because a piece of the file was incomplete. Anh's churn figures, the rate at which patients stopped booking recurring appointments, were a central input into the valuation, and Siran's team had asked for them early and treated them as reliable throughout the process, building the initial valuation model around the retention rate Anh's office reported without independently testing it against any outside source.

Nine days before the exclusivity period was set to lapse, Aram, a reputational risk analyst on Siran's diligence team, delivered a finding that complicated everything. Aram had spent the diligence period systematically reviewing publicly posted customer reviews across the clinic group's locations, tracking complaint volume, sentiment, and the specific language patients used about wait times, practitioner turnover, and rebooking difficulty. The pattern Aram found, a sharp rise in complaints about being unable to rebook with a familiar practitioner starting roughly a year earlier, suggested higher patient churn at several locations than the figures Anh's office had reported.

Anh's overriding concern, once the finding landed, was not proving the churn numbers were technically correct. It was speed and predictability. Anh had already told staff and two landlords that a sale was in progress, could not afford a process that dragged for months while both sides argued forensic accounting, and wanted a path to closing that both sides could actually commit to inside a realistic timeframe. Anh had also seen enough M&A processes among peers to know that a lapsed exclusivity period, followed by a return to the market with a known diligence issue on the table, tends to invite lower offers the second time around, not better ones.

Why this was harder than it looked

Reputational diligence, reviewing how a business's customers actually describe their experience in public, has become a routine part of buy-side work precisely because it can surface patterns that internal metrics miss or smooth over. Churn, as Anh's office calculated it, counted a patient as retained if they had booked any appointment within a defined rolling window, a standard method, but one that can understate a problem if patients are technically still on the books while quietly becoming dissatisfied and reducing how often they come in. Aram's review was not accusing Anh of fabricating numbers. It was showing a pattern that the reported churn rate, on its own, did not fully capture.

The difficulty was that neither account was simply wrong. Anh's churn figures were calculated consistently and matched the underlying booking data. Aram's review of customer reviews was also accurate as far as it went, complaint volume and sentiment had genuinely shifted at several locations. The two data sets were measuring related but different things, and reconciling them required more than picking one number over the other. It required understanding whether the reported churn rate was missing something real, or whether the review pattern reflected a smaller, louder group of dissatisfied patients who were not representative of the broader patient base.

That kind of reconciliation normally takes time, and time was the one thing Anh did not have much of. A forensic review comparing individual patient booking records against complaint patterns location by location could easily run past the exclusivity deadline, and if exclusivity lapsed without a deal, Anh would be back in the market with a diligence finding already on the table for any future buyer to discover independently. At the same time, agreeing to Siran's initial, unadjusted ask, a price cut sized to the worst-case reading of the review data, risked conceding more than the facts actually supported, simply to preserve the timeline.

The tension, then, was not really about who was right. It was about designing a resolution precise enough to be fair but fast enough to fit inside nine days, when the underlying question, how much of the softening was already reflected in the reported numbers versus how much was still hidden inside them, genuinely could not be answered with full certainty in that window. Adding to the pressure, Siran's team made clear that a delay past the exclusivity date would trigger a fresh internal investment committee review of the whole transaction, not a simple extension, which meant a blown deadline risked reopening far more than the reputational question alone.

What we did

1. Got Aram's underlying data, not just the summary finding. We asked for the specific reviews, locations, and date ranges behind the reputational analysis rather than accepting the headline conclusion, which let us assess how broad or narrow the pattern actually was across the nine clinics rather than treating it as a single group-wide problem. Starting from raw data rather than a summary meant we could challenge the finding on its own terms if it did not hold up, instead of arguing against a conclusion we could not independently test.
2. Cross-checked the pattern against three of the highest-complaint locations. Rather than attempt a full forensic reconciliation across the whole group, which would have blown the deadline, we focused a targeted booking-data review on the three locations where Aram's team had found the sharpest complaint increases, to see whether the reported churn there specifically understated the problem. Scoping the review to the locations most likely to show a real gap let us test the finding thoroughly without committing to a timeline the exclusivity window could not absorb.
3. Confirmed the discrepancy was real but concentrated. The targeted review showed that two of the three flagged locations did have higher effective churn than the reported group-wide average suggested, largely tied to the practitioner departures Anh already knew about, while the third location's complaint pattern reflected a temporary scheduling issue that had since been resolved. That distinction mattered directly to the negotiation, since it meant only two of nine clinics, not the whole group, actually carried the risk Siran's team had raised.
4. Proposed a location-specific price adjustment instead of a group-wide discount. Rather than accept Siran's initial ask, a flat percentage cut applied to the whole 38 million dollar valuation, we argued for an adjustment sized to the two locations where the data actually supported one, which kept the correction proportionate to the real finding and preserved most of the value in the seven clinics the reputational review had not implicated at all.
5. Structured part of the adjustment as a holdback rather than a straight price cut. To give Anh predictability, we proposed holding back a defined amount in escrow tied to patient retention at the two affected locations over the following year, rather than negotiating an immediate, larger discount based on an uncertain forecast of how bad the churn would ultimately prove, an approach that also spared both sides from arguing over a single, hard-to-verify projection under deadline pressure.
6. Briefed Anh daily on where the negotiation stood. Because predictability mattered to Anh as much as the final number, we gave short, plain updates after every exchange with Siran's team, so Anh always knew the realistic range of outcomes rather than being surprised by a late swing in either direction. Anh had been explicit that unpredictability, more than a lower number, was what would make the process feel like a failure, so treating communication itself as a deliverable was part of the strategy.
7. Kept the negotiation inside the exclusivity window by narrowing the open issues fast. We prioritized resolving the reputational finding ahead of smaller, lower-stakes diligence items still open on the file, since Anh's primary goal was avoiding a lapsed deadline, and a clear sequencing plan kept both legal teams focused on the one issue that actually threatened the timeline rather than splitting attention across a dozen minor open points that could wait until after signing.
8. Documented the resolution as a negotiated adjustment, not an admission. The final agreement described the price change and holdback as a commercial accommodation reached during diligence, avoiding language that could later be read as Anh conceding the original churn figures were inaccurate or misleading. That distinction protected Anh's credibility with staff, landlords and any future business dealings from being tied to a formal admission of misreported numbers.

The outcome

The deal closed inside the original exclusivity window, at a purchase price reduced by roughly 1.6 million dollars from the initially agreed figure, with a further 900,000 dollars held back in escrow for twelve months, tied to patient retention specifically at the two affected clinics. That combination gave Siran's team the protection they wanted against the risk the reputational finding had raised, without forcing a valuation-wide discount based on a pattern that, on closer review, applied to only part of the business. Siran's team credited the targeted, location-specific approach with giving their investment committee a clear, defensible basis for approving the deal on the original timeline, rather than the vaguer, group-wide justification the initial ask would have required.

Anh did not get the full price originally discussed before the finding surfaced, and accepted that the two underperforming locations had a real, documented problem that the reported churn figures had not fully captured. That was a genuine concession, not a technicality resolved in Anh's favour. But the process stayed inside the timeline Anh needed, avoided a drawn-out forensic dispute that could have cost more in legal fees and delay than the price adjustment itself, and kept the deal moving toward the predictable close that mattered most to Anh throughout. Staff and the two landlords Anh had already told about the sale learned of a completed transaction rather than a stalled one, which mattered to Anh nearly as much as the final number did.

The holdback resolved roughly ten months later, with actual patient retention at the two clinics landing close to the negotiated baseline, releasing most of the held funds to Anh with a modest reduction reflecting the portion of the shortfall that did materialize. Both sides described the outcome afterward as a fair compromise rather than a clean win for either party, which was, given the tight deadline and the genuinely mixed evidence, close to the best realistic result available.

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