A sales organization had strong pipeline volume but was generating below-benchmark revenue. The gap between pipeline size and actual revenue pointed to a win efficiency problem — but the specific causes were hidden inside the data.
The goal: identify exactly where and why wins were breaking down from, and build a reporting infrastructure that makes revenue performance visible in real time.
Domain: Revenue Operations / Sales Operations
Tools: Microsoft Excel (Power Query, Power Pivot, DAX), Excel Dashboard
Data Sources: SQL databases, CSV files, PDF reports
Scope: Full pipeline win rate analysis (March 2025-March 2026, with focus on Q4 2025 - Q1 2026) with dashboard delivery
Relevance to Payments/Fintech: Directly mirrors merchant acquisition analytics, onboarding win rate tracking, and revenue operations functions within fintech platforms
Step 1 - Data Ingestion (Power Query)
- Imported and transformed data from multiple sources: SQL databases, CSV files, PDF reports
- Cleaned and standardized inconsistent data formats
- Built automated refresh pipeline eliminating manual data handling
Step 2 - Data Modeling (Power Pivot)
- Built Sales Funnel relational data model
- Established relationships between leads, opportunities, pipeline stages, and rep performance tables
- Enabled multi-dimensional analysis across time, region, rep, and deal stage
Step 3 - Metric Calculation (DAX) Key metrics calculated:
Win Rate = DIVIDE([Closed Won], [Total Opportunities])
Discount Percentage = DIVIDE([Price] - [Discount Price] / [Price])
Regional Performance Index = DIVIDE([Region Win Rate], [Benchmark Win Rate])
Step 4 - Dashboard Delivery (Excel) Built Sales Performance Tracker Dashboard with:
- Executive summary KPIs
- Regional performance heatmap
- Rep-level conversion breakdown
- Pipeline stage funnel visualization
- Trend analysis over time
| Metric | Value |
|---|---|
| Total Pipeline Revenue | ₦27.5M |
| Total Sales | 463 |
| Win Rate | 46.3% |
| Discount % | 9.03% |
Finding 1 — Regional Performance Gaps of 15-20 Percentage Points Significant win rate variance across regions — highest-performing regions (50.31%)converting at 10-15 percentage points above lowest-performing regions (36.51%). Not a market problem — a process and capability problem.
Finding 2 — Rep-Level win rate Bottlenecks Bottom-quartile reps were disproportionately dragging overall win rates. The gap between top and bottom performers (~59% Points) was masking underlying pipeline health.
Finding 3 — Pipeline Velocity Was Strong — Wins Were Weak Leads were entering the pipeline at a healthy rate. The failure was happening at the qualification and closing stages — indicating a skills and process gap rather than a demand gap.
Finding 4 — No Consistent Qualification Criteria Reps were progressing deals at different standards — inflating pipeline with low-probability opportunities and creating false revenue confidence.
Focus coaching resources on bottom-quartile reps — specifically at the qualification and closing stages where conversion was breaking down.
Business Impact: Individual performance improvement of 25%-140% modeled across rep cohorts.
Implement consistent deal qualification standards across all regions — ensuring pipeline only contains opportunities that meet minimum probability thresholds.
Business Impact: More accurate revenue forecasting. Cleaner pipeline. Better resource allocation.
Establish regional benchmarks and regular performance review cadence — making regional gaps visible to leadership in real time through the dashboard.
Business Impact: Faster identification and correction of underperforming regions.
- Win rate improvement(projected): 46% → 78%
- Bottom-quartile win rate improvement(projected): 36% → 90%+
- Individual rep performance improvement modeled at 25% to 140%
- Company revenue increase potential: 25-50%
This project directly mirrors key functions in fintech Revenue Operations:
| Sales Ops Concept | Payments/Fintech Equivalent |
|---|---|
| Lead win rate (Pipeline) | Merchant onboarding conversion rate |
| Pipeline by region | Merchant acquisition by geographic corridor |
| Rep performance analysis | Agent/field team performance analysis |
| Deal qualification | Merchant risk and eligibility screening |
| Revenue forecasting | Transaction volume and MDR revenue forecasting |
The analytical frameworks, DAX metrics, and dashboard design are directly transferable to merchant analytics, agent network performance, and revenue forecasting functions in a payments operations environment.
Power Query Power Pivot DAX Data Modeling Sales Funnel Analysis Conversion Rate Optimization Revenue Operations Dashboard Design Multi-Source Data Integration
README.md- This file- dashboard_screenshot - Sales Performance Tracker Dashboard
- Data-Sources
Part of the Emmanuel Bitrus Payments & Revenue Operations Portfolio → Back to Portfolio