Price elasticity used to require an econometrics PhD and a dataset the size of a country. Frontier LLMs changed the math. Here's the prompt structure we actually ship.
RevTune Team
Pricing intelligence
Price elasticity is one of those concepts that sounds intimidating but is conceptually trivial: if you raise the price 10%, do you lose more or less than 10% of your customers?
If you lose less, raise the price. If you lose more, don't.
The hard part has always been measuring it. You need cohorts that saw different prices under similar conditions, controls for confounders, and enough sample size to trust the result. For most SaaS companies, that data either doesn't exist or is too messy to use.
Frontier LLMs are surprisingly good at reasoning about messy, partial data. We feed Claude:
…and ask it to estimate how much demand would change at three candidate price points.
The structure that works well for us has four parts:
That last constraint is what separates useful output from horoscope output. Without it, the model will hedge everything and give you a 5-paragraph essay. With it, you get a number, a confidence, and a citation. Three things you can actually act on.
It's bad at extrapolating beyond the range of your data. If your highest cohort paid $99, asking it about $499 is asking for a hallucination. We constrain candidate prices to within ~50% of observed values, and the answers stay grounded.
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