Design, implement, and operate a PMM measurement system. Synthesizes Reforge's metric constellation, OKR (Doerr), and the F.A.C.T.S. + MAYO frameworks. Output is a measurement blueprint sized to the team's data maturity.
When to use
Pre-OKR season. After a reorg. When PMM and sales pipeline numbers do not reconcile. When the team has many dashboards and no shared source of truth.
Steps
1. Audit current state. Inventory data sources, tracking tools, reporting gaps.
2. Define business-aligned goals. Translate company OKRs into measurable PMM goals.
3. Select the metric constellation (Balfour/Reforge). Choose output metrics across retention, engagement, monetization. Decompose each into input metrics.
4. Map metrics to PMM workstreams. Messaging, launches, enablement, CI, campaigns. Assign KPIs.
5. Choose attribution model. Blend MMM (strategic), MTA (tactical), incrementality (confidence). Match to sales-cycle length and data maturity.
6. Design the KPI dashboard. Stakeholder-specific views: exec, PMM team, cross-functional.
7. Set reporting cadence. Weekly / monthly / quarterly tied to actual decision forums. Reports arrive BEFORE the meetings where decisions get made.
8. Integrate data sources. CRM + analytics + ad platforms + PMM tools into unified reporting.
9. Establish baselines and targets. Use historical data. No aspirational guesses.
10. Build feedback loops. Review, refine, retire metrics as priorities shift.
Frameworks
- Metric constellation: Output metrics across retention/engagement/monetization, with input metrics decomposed under each, and tradeoff metrics monitored opposite each.
- OKR: Objectives + 2–5 Key Results.
- F.A.C.T.S. (PMA): Focus, Alignment, Commitment, Tracking, Stretching.
- MAYO (Dock): Motions, Actions, Yield, Outcomes.
- Three-workstream PMM model: Value-Based Messaging / Product GTM / Revenue Enablement.
- Launch KPI hierarchy: Awareness → Engagement → Adoption → Revenue, tiered T0–T3.
Quality gates
- Every KPI traces to revenue, pipeline, or customer value. No orphan vanity metrics.
- Output metrics cover all three dimensions (retention, engagement, monetization).
- Input metrics are actionable — the team can directly influence them.
- Tradeoff metrics monitored alongside primary metrics.
- Attribution model matches actual sales-cycle length.
- Cadence matches decision rhythm.
- Baselines from historical data, not guesses.
- Sales/product/finance agree on shared definitions.
- Quarterly review and retire/replace cycle defined.
Common failure modes
- Single north-star fixation. Hides problems in the other dimensions.
- Output metrics without input metrics. You detect problems too late.
- Vanity metrics masquerading as KPIs.
- Composite metrics that merge dissimilar actions and hide tradeoffs.
- No attribution plan before launch. Tracking added post-go-live cannot prove impact.
- 50+ KPIs creating paralysis. Focus on 3–7 per workstream.
- Misaligned MQL definitions across marketing/sales.
- Short-termism — measuring performance only, ignoring brand and long-term pipeline.
- Static framework that never gets revisited.
- Siloed reporting tools.
- Optimizing conversion rates while absolute counts shrink (Optimise for absolute count of users reaching each stage, not stage conversion rates).
- Calling experiment wins before long-term holdouts evaporate (30–40% of growth experiments with short-term lift show no incremental value at one year).
- Running tests that won't reach significance in a month (Don't test what won't reach sample size in a month — pre/post is fine).
Outputs
1. North-star metric constellation.
2. OKR structure.
3. KPI dashboard spec with stakeholder views.
4. Reporting cadence + review forums.
5. Attribution model selection with rationale.
6. PMM metrics by deliverable type.
7. Baselines + targets.
8. Data integration requirements.