Problems › We Have Too Many Products › HealthTech & Digital Health
Proliferation costs are real, mostly invisible, and land on the products that were paying for everything. This page works through it for digital health companies specifically — including an unedited excerpt from a real analysis of a digital health company.
Proliferation costs are real, mostly invisible, and land on the products that were paying for everything. What makes this harder for digital health companies is structural: outcomes risk is being signed faster than the company can learn whether it can carry it — a 12-month measurement window against an 11-month sales cycle. Any credible answer therefore has to hold at-risk revenue share and engagement rate in the same view, which is exactly where most internal analysis stops because the two live in different systems.
Product lines accumulate because each addition is individually justifiable and nothing is ever removed. The cost is not in any one of them; it is in the complexity they collectively impose — inventory, changeovers, support knowledge, sales attention, forecasting error.
That cost is borne disproportionately by the profitable core, because that is where the capacity being fragmented lives. Which is why rationalisation often increases total profit even when the removed lines were nominally contributing.
The analysis worth doing ranks lines by contribution against the constraint they consume, then asks which of the tail exists for a reason — a strategic customer, a channel requirement — and which exists because nobody has looked.
These three together are the signature. One on its own usually points somewhere else.
✓ A minority of lines produces the large majority of revenue
✓ Nothing has been discontinued in several years
✓ Operations complexity is rising faster than volume
The move that usually makes it worse. Cutting the tail by revenue rank alone, which removes lines that were cheap to carry and keeps ones that quietly consume the constraint.
It is for you if you run or finance a digital health company and a minority of lines produces the large majority of revenue. 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 digital health company. 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 Vantabridge Health, a sample company profile used for testing rather than a customer — $62M ARR, 340,000 enrolled members.
Excerpt from a real Percision run · Quick Market Scan · sample company profile
The move. Convert 180 existing employer relationships into $11.7M incremental outcomes-contingent revenue by Month 24 without new-plan procurement.
The leak it closes. $6.5M device leakage reduced by shifting kit cost to employer opt-in, improving gross margin 7 points on employer cohort
The assumption it rests on. 180 employers accept outcomes-contingent terms at 45% at-risk share — the engine put the probability at 0.7.
| Investment required | $0.6–0.9M total (2 FTE employer specialists @ $180K fully loaded each × 18 months + $120K enablement tools) |
| Expected return | 13.0× on $0.9M investment ($11.7M incremental revenue by Month 24) |
| Revenue, year 1 | $3.9M incremental employer outcomes revenue |
| Revenue, year 2 | $11.7M cumulative incremental employer outcomes revenue |
| Revenue, year 3 | $18.5M cumulative if employer cohort grows 15% YoY |
| Exit criteria | Terminate move if employer conversion rate <25% by Month 12 OR if employer at-risk share demanded exceeds 50% OR if device-kit leakage reduction <10 points by Month 18. |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to Matrix Strategy, one of 29 engagements the platform runs. For digital health companies it works through at-risk revenue share, engagement rate, gross margin and logo churn, 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.
Contribution per unit of the binding constraint, then a check on strategic dependencies. Revenue rank alone gets this wrong in both directions.
Some will, and the analysis should price that before the decision rather than after. Usually the revenue at risk is smaller than the complexity cost being removed, but it should be a finding rather than an assumption.
It is rarely tracked, which is why it grows. A workable proxy is the trend in operating cost per unit of volume; when that rises while volume rises, complexity is the usual explanation.
Materially, yes. Outcomes risk is being signed faster than the company can learn whether it can carry it — a 12-month measurement window against an 11-month sales cycle — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are at-risk revenue share, engagement rate, gross margin, 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 at-risk revenue share and engagement rate. 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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