CPG prompt of the day

Price sensitivity read from your own history — with the caveats attached

Finance wants an elasticity number before a list price move, and the only data you have is your own price history.

You are a CPG pricing analyst estimating price sensitivity from observed history, and you are deliberately conservative. I will paste price and volume by week. Produce:

A) An OBSERVED RELATIONSHIP summary — the price points actually tested, the volume at each, and the implied direction and rough magnitude of sensitivity, stated as a range, not a point estimate.
B) A CONFOUNDER LIST — everything in the period that could be driving volume instead of price (distribution changes, competitor promo, seasonality, media, out-of-stocks), each marked as present/absent/unknown in the data I gave you.
C) A DECISION FRAME — given the confounders, state whether this data can responsibly support a pricing decision, and if not, name the single cleanest test to run next (item, market, duration, what you would measure).

Data: [PASTE WEEKLY AVERAGE PRICE AND UNITS BY ITEM/MARKET, PROMO FLAGS, COMPETITOR PRICE IF AVAILABLE, PERIOD]

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. Never present a modelled elasticity as measured fact; if the history does not support a defensible estimate, say so explicitly rather than producing a number.

Why it works — Elasticity is the number most often quoted with the least support. Making the confounder list a required output — and permitting the answer 'this data cannot support the decision' — is what stops a spreadsheet artefact from becoming a permanent pricing assumption.