Problems › We Keep Discounting to Win Deals › HealthTech & Digital Health
Routine discounting is usually a proof problem and an incentive problem, and almost never a price problem. This page works through it for digital health companies specifically — including an unedited excerpt from a real analysis of a digital health company.
Routine discounting is usually a proof problem and an incentive problem, and almost never a price problem. 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.
When discounting becomes normal, the price has effectively been reset to the discounted level and the list price is decoration. That has a cost beyond the margin: it tells the market what you actually charge, and it is very hard to reverse.
The causes are consistent. The value is not proven, so price becomes the only variable left to discuss. Or the sales incentive rewards closing over margin, in which case discounting is exactly the rational behaviour. Or discretion is unlimited, and unlimited discretion is always used.
The diagnostic is the distribution. If discounts cluster at the end of a quarter or at particular individuals, the cause is incentive and authority, not price.
These three together are the signature. One on its own usually points somewhere else.
✓ Discounts spike at period end
✓ Discount levels vary widely between salespeople for similar deals
✓ Sales asks for price authority rather than for better proof
The move that usually makes it worse. Lowering list price to reflect reality, which resets the anchor and produces the same discount off the new number within two quarters.
It is for you if you run or finance a digital health company and discounts spike at period end. 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 · Cost Reduction & Efficiency · 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 Pricing & Revenue Optimization, 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.
Cap the discretion and pay on margin rather than on revenue. Discounting is a rational response to a quota measured in revenue with unlimited price authority attached.
No — as a deliberate, structured exchange for something you want, such as term, volume or a reference. As a reflex at the close of a negotiation, it is margin given away for nothing.
Move them at renewal with notice and a reason, and accept that some will leave. The alternative is a permanent two-tier price the rest of the market eventually discovers.
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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