Part 4 asked who your customers are and what they are worth. Part 5 turns to the operating decisions that shape the P&L: what to sell, what price to charge, and how much demand to prepare for.
Commercial analytics often goes wrong when the headline metric improves but the business value does not. A discount can boost revenue while eroding contribution margin. A new SKU can make the assortment look more complete while shifting demand away from stronger products. A forecast can look accurate overall while still leaving bestsellers stocked out and slow movers overstocked.
This part is about using analytics not just to report what happened, but to choose the better commercial decision. We will cover price elasticity and margin optimization, assortment and substitution analysis, and demand forecasting built around the cost of being wrong.
After this part, you will be able to:
- Estimate price elasticity from sales data and optimize for contribution margin
- Evaluate product assortment decisions with substitution and complementarity analysis
- Build demand forecasts for inventory, staffing, and budget decisions
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