SaaS Revenue Operations Analytics

Overview

Independent RevOps analytics project on a clearly labeled synthetic CRM and subscription model. Focuses on pipeline conversion, stage aging, coverage, and forecast comparison—not claimed revenue growth from unimplemented recommendations.

Business problem

Where are qualified prospects being lost, which channels create valuable opportunities, and is the current pipeline sufficient to meet revenue goals?

Analysis and approach

• Model CRM entities into an analysis-ready schema
• Define funnel and revenue KPIs
• Analyze stage conversion and aging
• Compare pipeline coverage to targets and draft prioritization recommendations

Skills: Pipeline analysis · Funnel analysis · Forecast comparison · KPI reporting

Evidence and visuals

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  Evidence placeholder — synthetic CRM dashboard export pending.

Findings and outcome

• Stage aging reveals risk that conversion rates alone can hide.
• Channel quality should be judged by downstream opportunity value, not lead volume alone.
• Pipeline coverage must be interpreted with stage mix and historical win rates.

Outcome: A decision-support dashboard framing that identifies funnel bottlenecks, pipeline risk, and opportunities to improve sales prioritization using synthetic CRM data.

Recommendations

1. 1

   Review aging opportunities in late stages weekly.

2. 2

   Reallocate attention toward channels with stronger opportunity quality.

3. 3

   Track forecast error separately from pipeline value.