Introduction
TCS Optumera's pricing module uses AI to recommend regular prices, promotions and markdowns for retailers. For a grocery chain, the objective function matters as much as the model. Optimising for short-term margin can raise prices on items where shoppers seem insensitive, lifting profit this quarter. But grocery shoppers judge a store's prices by a small basket of "known value items" like milk, eggs, bread, rice and cooking oil. If those drift up, the store's price image suffers, and shoppers slowly move to discounters or rivals. The question asks whether Optumera's pricing engine should optimise for short-term margin or long-term price perception. I'll clarify, identify the trade-off, understand the product, form a hypothesis, choose metrics, design an experiment, plan analysis, set a decision framework and recommend.
Short-term versus long-term trade-offs are hard because the long-term effect is slow and noisy. The key is to find a measurable leading indicator of the long-term effect, then test with enough time and the right control groups.
Step 1
Clarifying Questions (3 minutes)
- Client: Assume a regional grocery chain with 300 stores in the US or UK, competing with a discounter (Aldi or Lidl) and a large national chain.
- Scope: Regular (everyday) pricing on about 20,000 SKUs; promotions handled separately.
- Current setup: Optumera recommends prices maximising gross margin subject to simple rules (price endings, competitor price bands).
- Problem trigger: The client's margin is up 1.2 points over the year, but customer surveys show declining "good value" perception and footfall is flat.
- Decision owner: The client's pricing director, with Optumera's product team designing the default objective.
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