partner-health-scoring

name	partner-health-scoring
description	Design and maintain a partner health score that actually predicts renewal / churn for EdTech partners. Signal selection, weighting, half-life / decay, red-flag triggers, and threshold-to-play mapping. Reach for this skill when designing a new score, refreshing one that's stopped predicting outcomes, or interpreting a score move.

Skill: Partner Health Scoring

A partner health score is useful when it changes the PSM's behavior. A score that nobody acts on is decoration. This skill keeps the score honest.

Step 1 — Define the outcome you're predicting

A health score is a prediction of something. Pick one:

• Churn risk — probability of non-renewal in the next 90 / 180 days
• Expansion readiness — probability the partner is ready for an upsell / cross-sell motion this quarter
• Adoption trajectory — directional indicator (improving / flat / declining) regardless of absolute level
• Composite "health" — blended view (least precise, most-loved by leadership)

If you're tempted to say "all of them," pick the one that drives the most action. Composite is a fallback when nothing else fits.

Step 2 — Pick the signals (4–7 max; more is noise)

Signal categories and example signals:

Category	Examples
Adoption depth	% of seats actively using the deepest 2–3 features; share of users reaching the "aha" milestone within onboarding window
Usage breadth	active users / total seats (normalized for partner size); rolling 30-day active count vs. baseline
Sentiment	NPS with follow-up answer; CSAT after support resolution; quarterly check-in sentiment score
Business outcomes	the partner's own KPI that the product is supposed to move (test scores, time-to-skill, course completion, etc.) — only when measurable + shareable
Touchpoint cadence	days since last meaningful (non-boilerplate) interaction; PSM-to-partner response latency
Champion strength	number of active champions (1 = single point of failure, 2 = thin, 3+ = robust); champion engagement frequency
Commercial signal	contract anniversary proximity; multi-year vs single-year posture; payment-history flags

Anti-pattern: picking signals that the dashboard has rather than signals that predict the outcome. If a category isn't predictive, leaving it out is correct.

Step 3 — Weight the signals

Equal-weighting is rarely right. Different signals predict different outcomes with different strength. Tune weights based on historical churn / renewal data when possible; use cohort comparison when not.

Anti-pattern: weighting based on which department lobbied hardest. The CS-leadership-loves-NPS-so-NPS-is-30% pattern produces scores that look comprehensive but don't predict anything.

When in doubt, start with these defaults for churn risk:

• Adoption depth: 25%
• Touchpoint cadence (recency): 20%
• Business outcomes (when available, otherwise redistribute): 20%
• Sentiment: 15%
• Champion strength: 10%
• Usage breadth: 10%

Adjust based on what you observe in the first 90 days.

Step 4 — Design the half-life / decay (this is the part most teams skip)

A signal from 6 months ago is not a signal today. Every component needs a defined decay rate. Examples:

• Login frequency / usage — fast decay (half-life ~14 days). Last 30 days matters most.
• Adoption-of-a-deep-feature — slow decay (half-life ~90+ days). Once adopted, stays adopted unless the user leaves.
• NPS score — medium decay (half-life ~60 days). The most recent NPS dominates; older surveys fade.
• Champion-engagement frequency — fast decay. A champion who hasn't engaged in 60 days is no longer effectively a champion.
• Business outcome metric — depends on the metric's natural reporting cadence. Quarterly test scores have a 90+ day half-life.

Rule: if you can't write down the half-life for a component, you haven't designed it; you've just added it.

Step 5 — Red-flag triggers (run alongside the score, not inside it)

A composite score reacts too slowly. Independent red-flag triggers fire a recovery play immediately, regardless of the composite. Examples:

• Active-user count drops >30% week-over-week
• Champion departure (named champion no longer in the role)
• 21+ days of zero meaningful touchpoints during the partner's active season
• Renewal date within 90 days AND decision-maker not yet confirmed alive in role
• 2+ consecutive support escalations to leadership tier
• Partner explicitly states "we're evaluating alternatives"

The composite score is a trailing indicator. The triggers are leading indicators.

Step 6 — Threshold-to-play mapping

The score itself is useless without an answer to "what does the PSM do when it says X?" Define:

• Green (e.g., 70–100): maintain cadence; consider expansion-readiness check
• Yellow (e.g., 50–69): investigate which components dropped; light-touch recovery play; weekly review
• Red (e.g., <50): mandatory recovery play; cross-functional alert; PSM lead daily review until cleared

Plus: which trigger fires which play, independent of the composite color.

Step 7 — Dashboard surface (if it's not visible Tuesday morning, it doesn't drive behavior)

The PSM sees:

• Composite score + week-over-week delta
• The 2–3 component drivers behind the score (drill-down)
• The trigger status: any active red-flag triggers, in plain language
• The recommended play for the current state
• The history (12-week sparkline) for context

If the dashboard requires the PSM to compute anything mentally, redesign it.

Step 8 — Refresh the score quarterly

A score that's never been refreshed is suspect. Quarterly review:

• For each renewed partner, did the score correctly predict the renewal outcome?
• For each churned partner, did the score show red in time to act?
• Did any red-flag trigger fire after the partner was already in churn motion? (lagging → leading correction needed)
• Did any green partner churn? (signal gap; add the missing signal)
• Are any signals correlating but not predicting? (drop them; they're noise dressed as signal)

What this skill does NOT cover

• Building the dashboard UI (route to the learning-analytics-analyst or ravenclaude-core/data-engineer for instrumentation)
• Designing the plays that fire from the thresholds (route to success-playbook-designer)
• Comms-shape variants when the score informs partner-facing claims (route to ferpa-comms-translator)
• The narrative that explains the score in a QBR (route to qbr-composer)