Commercial analytics / Enterprise reporting
Sales Intelligence & Opportunity Discovery
Understanding sales performance. Exploring where accounts could grow.
For the past two years, I've developed and owned Enterprise Sales Analysis, from its original data model to Power BI reporting used by approximately 150 people daily. I then extended that data foundation into Purchasing Habits to investigate customer buying patterns and potential growth opportunities.
Inside the reporting
From the sales overview to the details behind it.
Explore a fictional reporting environment: compare periods and segments, drill into account and item performance, or investigate purchasing patterns among similar accounts.
Select a period to see the KPIs, account matrix and margins respond together. Then choose an account.
| Account | Revenue | YoY | Orders | Direct % | Op. % |
|---|---|---|---|---|---|
| Coffee & Café | $687K | +8.0% | 1,832 | 25.4% | 14.4% |
| Fast Casual | $598K | +12.0% | 1,496 | 22.7% | 12.2% |
| Regional Retail | $511K | +6.0% | 1,304 | 24.3% | 13.8% |
| Fast Casual | $495K | +2.0% | 1,161 | 20.4% | 8.6% |
| Coffee & Café | $461K | -5.0% | 927 | 21.6% | 8.8% |
| Regional Retail | $399K | +16.0% | 1,035 | 25.4% | 15.3% |
All accounts and financial values are fictional. Select an account to investigate.
Select a period on the chart to cross-filter the KPIs, account table, margin breakdown and drill-through.
Commercial decision Select a period or account to investigate the drivers behind performance.
Original data modeling, SQL queries, and Power BI development, followed by integration with shared Analysis Services models. The upstream semantic models are not all my own work.
An extension of the same sales data
Two questions behind opportunity discovery.
Purchasing Habits uses two complementary comparisons. First, a behavioral profile considers how accounts order—including cadence, recurring purchasing mix, catalog breadth and location patterns—to identify potentially comparable customers. A separate catalog and vendor comparison then examines overlap, adoption and purchasing gaps among relevant peers in the same market segment or vertical. These differences guide further investigation, not automatic recommendations.
Consider purchasing frequency, product mix, breadth and participating locations—not revenue alone.
Check category and vendor gaps against customer needs, existing suppliers and practical constraints.
These are prompts for investigation, not forecasts of new sales. The fictional comparison does not reproduce the deployed similarity weighting.
Decision Studio / Continue exploring
