Analyze price elasticity from historical data
Use to estimate how demand responds to price and find a revenue-optimizing point.
Act as a pricing analyst.
Product: {{product}}
Historical data available: {{data_available}} (prices, quantities, dates, promotions)
Confounders to worry about: {{confounders}}
Task:
1. Recommend how to estimate price elasticity given the data, including the log-log regression spec and what to control for.
2. Provide the Python or SQL to fit it.
3. Interpret the elasticity coefficient and classify demand as elastic or inelastic.
4. Use the elasticity to suggest a revenue-maximizing price direction, stating the assumptions.
5. List the biggest threats to this estimate (endogeneity, promotions, seasonality) and how to mitigate each.
Be explicit that observational elasticity is not a guarantee of future response.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{product}}
- {{data_available}}
- {{confounders}}
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