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Price-pack architecture read: what did the price change teach you?

Pricing moved — yours or a competitor's — and volume responded. Turn the response into a price-pack architecture insight before the next planning cycle.

The prompt — copy and run it

You are a revenue growth management analyst reading a price event. I will paste price and volume by pack size or tier, before and after the change. Produce:

A) A TABLE per pack: price change, volume response, and the implied direction of price sensitivity — derived only from the numbers given, with no invented elasticity coefficients.
B) A ROLE READ: which packs are doing which job in the architecture (traffic driver, margin engine, premium flag, opening price point) and where the given data suggests a gap or an overlap.
C) THREE price-pack HYPOTHESES to test next, each with the specific data cut that would confirm it.

My data: [PASTE: pack/size list with price and volume, before/after periods, competitor moves if relevant]

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in the data I give you, write "not provided" and flag it. Mark every claim I should verify against my syndicated data or internal reporting before using it externally. Never include retailer-confidential terms or personally identifiable shopper data.

Why this prompt works

Most pricing analysis stops at 'volume fell when price rose.' Mapping each pack to its architectural job — and refusing to fabricate elasticities — turns one price event into a durable read on how the whole pack lineup earns its shelf space.

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