What’s the Best Approach to Pipeline Analytics?
Standardize stages and KPIs, analyze by cohort, enforce hygiene, and run a weekly operating rhythm on one scorecard to improve forecast accuracy and growth.
Core Pipeline KPIs (Start Here)
| Metric | Formula | Target/Range | Why it matters | 
|---|---|---|---|
| Stage Conversion | Downstage ÷ Upstage | Stable or ↑ vs baseline | Finds drop-off points | 
| Cycle Time | Days in stage | <= p50 / watch p75 | Highlights delays/approvals | 
| Pipeline Coverage | Pipeline ÷ Quota | 3–5× by segment | Capacity planning | 
| Win Rate | Closed-won ÷ Qualified Opps | Upward trend | Effectiveness of selling motion | 
| Forecast Accuracy | |Actual − Forecast| ÷ Forecast | ≤ 10% variance | Exec confidence | 
Pipeline Hygiene Rules (Keep the Data Honest)
| Rule | Definition | Guardrail | Owner | 
|---|---|---|---|
| Entry/Exit Criteria | Evidence to move stages | Validators + reason codes | RevOps | 
| Aging Thresholds | P75 age alerts per stage | Recycle or escalate | Sales/CS Managers | 
| Close-Date Discipline | Limit push count per opp | Manager approval beyond N | Sales Ops | 
| Reason Codes | Loss/recycle coded list | Monthly review loop | Enablement + RevOps | 
| Territory/Segment Tags | Required on account/opp | Picklists + ownership | RevOps/Data | 
Cohort Analysis That Reveals Truth
| Cohort | Why it’s useful | Typical insight | 
|---|---|---|
| Segment (SMB/Mid/ENT) | Different cycle times & ACVs | Coverage target varies 3–5× | 
| Region (NAM/EMEA/APAC) | Calendar & compliance effects | Stage hygiene gaps by region | 
| Source (Paid/Partner/Outbound) | Lead quality & intent | SQL→Opp conversion variance | 
| Product/Use Case | Packaging & ICP fit | Win rate driver analysis | 
| Rep/Team | Coaching & enablement | Playbook adoption gaps | 
Operating Rhythm (Meetings That Move Numbers)
| Cadence | Focus | Output | Owner | 
|---|---|---|---|
| Weekly | Stage conversion, aging, coverage by cohort | Actions for one constraint | RevOps + GTM leaders | 
| Monthly | Constraint memo & pilot plan | 30–60 day experiment | RevOps/Analytics | 
| Quarterly | Coverage planning & forecast lookback | Target updates + enablement | CRO/Finance | 
90-Day Pipeline Analytics Rollout
| Step | What to do | Output | Owner | Timeframe | 
|---|---|---|---|---|
| 1 — Define | Stage criteria, SLAs, data dictionary | Glossary + hygiene rules | RevOps | Weeks 1–2 | 
| 2 — Instrument | Dashboards for core KPIs + cohorts | Scorecard v1 | Analytics | Weeks 3–4 | 
| 3 — Operate | Start weekly review + alerts | Action list per team | GTM Leaders | Weeks 5–6 | 
| 4 — Pilot | Run a 30–60 day fix on the biggest constraint | Lift vs baseline | RevOps + Channel Owners | Weeks 7–12 | 
| 5 — Scale | SOP + enablement + targets refreshed | Standardized operating model | RevOps/Enablement | Weeks 12–13 | 
Frequently Asked Questions
No—begin with stage criteria, conversion, and cycle time. Add attribution to optimize source/channel budgets after the core is stable.
As few as possible while preserving forecasting accuracy. Most teams succeed with 5–7 selling stages plus recycle states.
Speed-to-lead and routing fixes often lift MQL→SQL conversion within weeks once SLAs and alerts are active.
RevOps owns definitions and instrumentation; Finance validates calculations; GTM leaders own actions by cohort.
Publish a data contract with field definitions, system of record, and refresh cadence. Investigate variance weekly until ≤10%.
