PRISM Case study · Retail

How a major retailer used PRISM to see which promotions were paying for themselves.

A major retailer needed a clearer view of what its pricing and promotion decisions were really doing. We delivered PRISM: machine-learning models, what-if scenarios and a dashboard for its commercial teams.

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Completed. The decisions it supported delivered a 2–4% gross-margin uplift.

Scenario view. Illustrative figures, not client data.

Client
A major retailer
Status
Completed
Result
2–4% gross-margin uplift
Delivered as
Machine-learning models, scenarios and a custom dashboard

The problem

Pricing and promotions are hard to judge when the effects overlap.

A price change moves demand, changes how shoppers react to offers and shifts sales between products. The retailer needed to see these together before deciding.

  • Price and volume together

    Price elasticity had to be judged alongside how volume responded.

  • Offers wear out

    Promotional uplift had to be weighed against fatigue over time.

  • Sales move between products

    Cannibalisation could make an apparent gain worth much less.

  • No consistent comparison

    Comparing options needed one consistent view of the likely effects.

What PRISM measures

The effects that decide whether a promotion paid off.

Each one matters on its own. The value comes from seeing them in the same view, for the same decision.

  • Price elasticity

    How much demand for a product moves when its price does.

  • Volume uplift

    The extra units a promotion sold, compared with what would have sold anyway.

  • Promotion fatigue

    How the response to an offer weakens when it is repeated.

  • Cannibalisation

    Sales a promoted product took from other products on your own shelves.

What-if scenarios

Options compared side by side before the team commits.

The retailer's teams could set up alternative pricing or promotion choices and see the likely effect of each in the dashboard.

  • Built for commercial teams. Pricing, merchandising and category teams use the dashboard.
  • Margin as well as volume. Every scenario shows what it does to profit.
  • Decision support. PRISM shows the likely outcome. The team makes the call.

Scenario view. Illustrative figures, not client data.

How it worked

From sales history to a commercial decision.

We covered the full analytical path, from preparing the data to delivering the dashboard.

  1. Data is prepared

    Sales, price and promotion history is prepared for analysis.

  2. Models are fitted

    Machine-learning models estimate elasticity, uplift, fatigue and cannibalisation.

  3. Options are entered

    The team sets up alternative pricing or promotion choices.

  4. Effects are compared

    Likely effects for each option are shown side by side in the dashboard.

  5. The team decides

    Commercial teams review the evidence before making the call.

2–4%gross-margin uplift

What it delivered for the retailer.

PRISM gave the retailer's pricing and merchandising teams a clear view of which price changes and promotions were paying for themselves. The decisions it supported delivered a 2–4% improvement in gross margin.

See PRISM

Bring a pricing question. We will show you how PRISM answers it.

A demo takes about 30 minutes. We walk through a scenario comparison and talk about your categories and your data.

Book a demo See PRISM
  • For commercial directors, pricing managers and category teams
  • Suited to grocery, convenience, pharmacy and consumer brands
  • Need analytics for a different question? See our BI services