Leaders need a fast read on revenue health, customer activity, and cash risk (late settlements), with enough detail to act by region/segment/product.
- Three-page Power BI report over transactions + customers
- Star schema:
Transactionsfact withDate,CustomersandProductsdimensions - Time intelligence via dedicated
Datetable (marked as Date) - KPIs: Total Revenue, Active Customers (30D), AOV, Late Settlement %, Avg Days to Settle, Cohort Retention (30D)
- Total Revenue: £1.3M
- Active Customers (30D): 271
- AOV: £158
- Late Settlement %: 44.7%
- Average Days to Settle: 8.72
- Cohort Retention (30D): 58.5%
- Late Settlement Revenue: £563.9K
- Target high-impact groups to cut late payments.
Evidence: Rank segments/regions/products by Late Settlement % and Late Settlement Revenue.
Action: Focus process fixes where both are high (payment-reminder cadence, invoice clarity, term tweaks). - Shrink time-to-cash in the worst product/segment cohorts.
Evidence: Track Avg Days to Settle by month with a slicer on Product/Segment; spot the top laggards.
Action: Run a 2-week experiment (e.g., pre-due reminders, different net terms) in the top 1–2 cohorts; success = ↓ in Avg Days to Settle trend. - Boost repeat usage where retention lags. Evidence: Compare Cohort Retention (30D) across Segment/Region/Product; sanity-check against Active Customers (30D). Action: Trigger lifecycle messaging for underperforming cohorts (win-back nudges, onboarding tips); success = +Δ in 30D retention.
- Open
/powerbi/dashboard.pbixin Power BI Desktop. - Point to
/data/customers.csv,/data/products.csvand/data/transactions.csv(or your source tables). - Refresh; export three screenshots to
/images/:kpi.jpg(KPI page)risk&retention.jpg(Risk & Retention page)tables.jpg(Tables page)
- Open
.csv,.jpg,.pdf, and.mdfiles using compatible applications.
Two-page consulting report in slides/consulting-report.pdf. Contains KPI overview, breakdowns, insights, actions and definitions.
Synthetic sample data can be used to demo structure; replace with your live source as needed.
Credits: Built with Power BI + DAX.


