Pipeline Analytics
Reading pipeline health before it bites the quarter.
Workflow
Step 1Pipeline Metrics
ObjectiveDefine what you track.
InputSales process
OutputMetric set
Key activities
Define ACV, velocity, conversion, stage criteria
Decision points
Do metrics reflect reality?
Tools: Metrics frameworkRoles: Analytics · sales
Success: Metrics defined
Quality gate: Go: metrics right. Loop back: vanity.
Why it matters
- Pipeline is the leading indicator of revenue. Analytics turns it into an early-warning system instead of a post-mortem.
Best practices
- Track stage duration — slow stages are warning signs.
- Measure win rate by source and stage.
- Assess health per segment, not just aggregate.
- Feed findings into the forecast.
Common mistakes
- Pipeline dashboards that hide stage problems.
- Aggregate numbers that mask segment risk.
- Analytics without action.
Key questions
- Where does the pipeline get sick?
- What is the realistic close, given stage health?
Agents that drive this
Pipeline AuditorForecast Analyser
Tools we use · alternates in [ ]
Metabase[ Looker Studio, Power BI, Grafana ]HubSpot[ Salesforce, Pipedrive, Attio ]