The annual plan uses one price elasticity assumption to forecast revenue across brands. Before approving it, ask when the underlying data were collected, which price change was measured, and whether the model separated pricing decisions from customer demand. The pooled evidence shows that mismatched choices can materially alter the estimate. Make benchmark quality part of the pricing review, not an invisible analytics decision.
Treat every price elasticity benchmark as conditional, not universal.
Price elasticity, how much sales fall when price rises 1%, varies with the era of the data, the product's life-cycle stage, the time horizon, which price was measured, and how the model was built. A benchmark from one context misleads pricing decisions in another, so validate elasticities before making pricing decisions.
Data chart
Price elasticity estimates are markedly larger when models separate customer demand from managers' own pricing reactions.
Key takeaway
Price elasticity benchmarks travel poorly; match the evidence to the decision before using it.
Source
Bijmolt, T. H. A., Van Heerde, H. J., & Pieters, R. G. M. (2005). New Empirical Generalizations on the Determinants of Price Elasticity. Journal of Marketing Research.
Evidence strength: Strong (pools 1,851 published business-to-consumer price elasticity measurements from 81 studies using data collected 1956-1999, mostly groceries). Covers brand- and individual-product sales, share, and choice response; does not establish profit, ROI, or payback and is less certain for durables and economies with extreme conditions. Durables account for only 33 of the 1,851 measurements.