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%.