PM.
Commercial — Case study blueprint

Sales & Customer Performance

A commercial performance model connecting revenue, margin, product mix and customer value.

SQL ServerSQLPower BIDAXPython
Business problem

Revenue growth can conceal margin erosion, weak retention or excessive dependence on a small number of products and customers.

Objective

Build a reusable sales model that moves from headline KPIs to product, customer and cohort decisions.

Decision

Prioritise campaigns by margin-adjusted customer value.

01

Data source

Microsoft’s sample transactional and data-warehouse databases for realistic SQL analysis.

Visit Microsoft AdventureWorks ↗
02

Data preparation

  • Restore the sample database and profile transactional tables.
  • Create clean sales, product and customer views in SQL.
  • Define returns, discounts and cost treatment before calculating margin.
03

Data model

  • FactInternetSales at order-line grain
  • DimCustomer, DimProduct, DimPromotion and DimDate
  • Cohort and customer-segment helper tables
04

Analysis

  • ABC customer and product segmentation
  • Cohort retention and repeat-purchase analysis
  • Revenue-to-margin bridge and mix variance
05

Dashboard

Embed-ready dashboard area

Designed for Power BI, Tableau, Looker Studio or Plotly.

The live report will sit here after publication. Until then, the portfolio keeps a clear, truthful build status.

Discuss the project ↗
06

Key questions & KPIs

  • Separate high-volume products from high-contribution products.
  • Use ABC segmentation to expose customer concentration risk.
  • Measure retention by acquisition cohort rather than a single repeat-rate average.
RevenueGross marginAverage order valueRepeat purchase rate
07

Business recommendations

  • Prioritise campaigns by margin-adjusted customer value.
  • Protect availability for products that contribute most to profit.
  • Investigate cohorts with strong first orders but weak repeat behaviour.
08

Limitations

  • AdventureWorks is a sample business, so conclusions demonstrate method rather than a live company decision.
  • Customer lifetime value requires assumptions about future retention and margin.
09

Next steps

  • Build SQL transformation views.
  • Validate the semantic model and core DAX measures.
  • Create an executive summary and a diagnostic detail page.

From blueprint to published analysis.

Build the pipeline, validate the model, document the findings, then add the live dashboard and code.

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