标识流失往往在参与失败引发风险份额变动后很久才显现。 数字健康公司面临的问题并非通用版本。结果风险签约速度快于公司判断自身能否承担的速度——12个月测量窗口对上11个月销售周期——因此忽略风险收入份额的答案必然出错。分析必须从参与率和毛利率而非收入入手。
标识流失往往在参与失败引发风险份额变动后很久才显现。 数字健康公司面临的问题并非通用版本。结果风险签约速度快于公司判断自身能否承担的速度——12个月测量窗口对上11个月销售周期——因此忽略风险收入份额的答案必然出错。分析必须从参与率和毛利率而非收入入手。
多数流失在测量窗口关闭前就已锁定——发生在入职和首个参与率决定归因结果是否产生的时期。等到记录标识流失时，所述原因很少指向早期未能将注册成员转化为持续PMPM收入。
有效切分应按队列和早期参与率而非退出原因。首个时期达到归因结果的成员此后留存表现不同，且队列间差距大于后期支持或定价差异。
第二个有效切分是风险收入份额而非标识。流失大量低PMPM成员与流失少数高PMPM成员产生相同标识流失，却需要不同应对。
这三者同时出现才是标志。单独一个通常指向其他问题。
✓ 风险收入份额在注册成员间独立于参与率变动
✓ 标识流失在按首次归因结果时间定义的队列间差异显著
✓ PMPM收入需持续新增注册成员才能维持总ARR不变
通常会让情况更糟的做法。 在测量窗口结束时按风险条款谈判续约，这是应对参与不足最晚且效果最差的时机。
It is for you if you run or finance a digital health company 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 一家数字健康公司. It is a sample profile rather than a customer, and it is engine output translated from English — 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 · Customer Value Architecture · sample company profile
The move. Reduce downside exposure from $7.1M outcomes shortfall to $4.2M while maintaining upside participation in 34 health-plan contracts.
The leak it closes. $2.9M gross profit protected annually through downside cap (difference between $7.1M shortfall at 38% vs $4.2M shortfall at 25%)
The assumption it rests on. Health plans accept 25% downside cap without demanding 15-20% PMPM reduction to compensate — the engine put the probability at 0.7.
| Investment required | $0 incremental — policy change executed by existing legal, finance, and account management teams within current $14M annual burn |
| Expected return | 5.2× on zero incremental investment — derived from $4.2M FY2026 bookings protected relative to status-quo downside exposure |
| Revenue, year 1 | $57.8M ARR (25% at-risk share = $15.5M at-risk revenue vs $23.6M status quo) |
| Revenue, year 2 | $61.4M ARR (assuming 80% contract renewal at 25% cap) |
| Revenue, year 3 | $68.2M ARR (assuming 85% renewal and 10% PMPM stabilization) |
| Exit criteria | Abandon this move if >3 of 8 Q4 2026 contract renewals demand >15% PMPM reduction to accept 25% cap, OR if outcome-prediction accuracy falls below 70% on 10k cohort by Month 9; pivot to fixed-fee PMPM model with optional 15% upside sharing only |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to 价值创造蓝图, one of 29 engagements the platform runs. For 数字健康公司 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. 在此阅读完整报告 if you would rather see the depth first.
基准不如趋势和结构重要。小客户可接受的比率对大客户可能是致命的，任何没有队列支撑的数字都只是装饰。
这会把流失问题转为利润问题，通常只拖延一个周期。除非已知原因并能在该周期内修复，否则不值得做。
直接与获客比较：现有基盘一个点留存对上买一个点新收入的成本。在达到一定规模后，留存通常便宜数倍，因此值得先分析再启动新一轮获客。
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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