Problems › We Keep Losing Customers › Healthcare Providers
Churn is measured at the end and caused at the beginning. This page works through it for healthcare providers specifically — including an unedited excerpt from a real analysis of a healthcare provider.
Churn is measured at the end and caused at the beginning. For healthcare providers, this shows up in a particular place. The numbers that carry the answer are cost per episode and payer mix, and the complication specific to this industry is that downside risk has been accepted on 38,000 lives without the cost-per-episode data needed to price it. The general version of this problem and the one you are actually in have different first moves.
Most churn is decided long before it is recorded — in onboarding, in the first weeks, in whether the customer ever reached the thing they bought. By the time cancellation arrives, the reason given is rarely the cause; it is the most polite available explanation.
The useful cut is by cohort and by early behaviour rather than by exit reason. Customers who reached the core outcome in the first period behave differently forever, and the gap between those who did and did not is usually larger than any difference in product, price or support afterwards.
The second useful cut is revenue rather than logos. Losing many small customers and losing a few large ones produce the same churn percentage and require completely different responses.
These three together are the signature. One on its own usually points somewhere else.
✓ Cancellation reasons are vague and vary widely
✓ Retention differs sharply between cohorts you cannot explain
✓ Acquisition has to keep rising to hold revenue flat
The move that usually makes it worse. Building a save programme at the exit, which is the most expensive point in the relationship to intervene and the least likely to work.
It is for you if you run or finance a healthcare provider and cancellation reasons are vague and vary widely. It is the situation where the numbers are available but nobody has put them in an order that produces a decision.
It is not for you if Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
Below is an excerpt from a real run of this analysis on a healthcare provider. It is a sample profile rather than a customer, and it is unedited engine output — this is the format you get, on your own numbers.
The subject is Cedar Ridge Health Partners, a sample company profile used for testing rather than a customer — 38,000 attributed lives under value-based contracts.
Excerpt from a real Percision run · Quick Market Scan · sample company profile
The move. Sell coordinated care bundles directly to self-insured employers using existing clinic density and ASC capacity.
The leak it closes. Bypasses commercial payer take-rate (estimated 15–20% of premium) and prior-authorization friction, reducing denial leakage on these lives to near zero
The assumption it rests on. At least two of the five largest self-insured employers in the two metros will sign a 3-year direct contract within 18 months — the engine put the probability at 0.7.
| Investment required | $2M over 36 months ($800K Year 1, $700K Year 2, $500K Year 3) |
| Expected return | 6.0–9.0× on $2M investment |
| Revenue, year 1 | $0 incremental (pilot setup and first contract negotiations) |
| Revenue, year 2 | $4–6M incremental (2–3 employer contracts, 4,000–6,000 covered lives) |
| Revenue, year 3 | $12–18M incremental (5 employer contracts, 10,000–15,000 covered lives) |
| Exit criteria | Terminate pilot and redeploy 4 FTEs if fewer than 2 employer contracts signed by Month 18 OR if operating margin on employer channel falls below 6% for two consecutive quarters |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to Value Creation Blueprint, one of 29 engagements the platform runs. For healthcare providers it works through cost per episode, payer mix, panel size and contribution per provider, then produces the sequence rather than a list of options — which move first, what it funds, and the observation that would say the sequence is wrong.
You watch the analysis get built before paying anything. Read a complete report here if you would rather see the depth first.
The benchmark matters less than the trend and the mix. A rate that is fine for small accounts is fatal in large ones, and any figure quoted without a cohort behind it is decoration.
It converts a churn problem into a margin problem and usually delays the loss by one cycle. It is worth doing only where you know the cause and are fixing it within that cycle.
Compare it against acquisition directly: a point of retention on your existing base against what a point of new revenue costs to buy. In most businesses past a certain size, retention is several times cheaper, which is why it is worth analysing before another acquisition push.
Materially, yes. Downside risk has been accepted on 38,000 lives without the cost-per-episode data needed to price it — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are cost per episode, payer mix, panel size, and an answer built on industry-general benchmarks will usually point at the wrong one first.
Less than most people expect. Your last twelve months of revenue and cost split the way you already split it, plus whatever you hold on cost per episode and payer mix. The analysis is explicit about what it is assuming where your data stops, which is more useful than waiting for numbers you may never have.
Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
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