Drivers of Price Elasticity

ConversionBijmolt, T. H. A.; Van Heerde, H. J.; Pieters, R. G. M. · 2005Journal of Marketing Research
Topicsprice elasticity·meta-analysis·pricing·promotional pricing·endogeneity·cpg·durables

Imagine approving a pricing plan built on older benchmarks. Price elasticity means the percentage change in sales, market share or chance of being chosen for a 1% price change. Which benchmark fits your categories and pricing decisions?

Price elasticity benchmarks for sales, share, and choice depend on data and model choices.

Data chart

The updated average price response is stronger than the earlier benchmark

Average percent change in sales, market share or chance of being chosen for a 1% price increase; a negative value means a decrease.

-1.76%Earlier review benchmark published in1988-2.62%Updated review benchmark published in2005

The updated review's average price response is stronger than the earlier benchmark.

Table 4 and Conclusions, pp. 152 to 153: exact average responses -1.76% in the 1988 review (367 estimates published through 1985) and -2.62% in this review (1,851 estimates from 81 studies). The reviews cover different sets of studies; the comparison is not a controlled time trend.

Data chart

Durables respond most strongly to price at both category stages

Predicted percent change in sales, market share or chance of being chosen for a 1% price increase, holding other study differences at their averages; a negative value means a decrease.

DurablesIntroduction or growthMaturity or decline-5.38%-3.81%Groceries shoppers are lesslikely to stockpileIntroduction or growthMaturity or decline-4.10%-2.62%Groceries shoppers are morelikely to stockpileIntroduction or growthMaturity or decline-2.71%-2.60%

Durables have the strongest predicted price response at both category stages.

Table 2 Panel A, p. 147; predicted responses hold other study differences at their averages. Exact introduction/growth and maturity/decline pairs: durables -5.38%, -3.81%; groceries shoppers are less likely to stockpile -4.10%, -2.62%; groceries shoppers are more likely to stockpile -2.71%, -2.60%. Stockpiling means buying extra to store for later; the durable category has only 33 estimates.

Action guide

  1. Validate pricing models before using benchmarks.Ask your analytics team to include advertising and sales promotion and to test whether prices respond to demand before approving forecasts.
  2. Use recent evidence from matching categories.Ask your pricing team for forecasts reflecting stronger sales response in newer data.
  3. Consider low introductory prices when launching a new category.Raise prices as the category matures.
  4. Keep your regular price increases below inflation when inflation is high.This may support sales or share.
  5. Separate promotional and checkout price forecasts across time horizons.Ask your pricing team to model regular price separately from checkout price divided by regular price.
  6. Pilot fewer promotions if customers buy deal to deal.The study did not test this plan. Check profitability before expanding it, since Procter & Gamble found fewer deals need not raise profit.

Evidence

  • Models correcting for prices set in response to demand predict a -3.74% change in sales, share or choice per 1% price increase, versus -2.47% otherwise, holding other study differences at their averages.
  • Across 1,851 estimates, a 1% price increase is associated with a -2.62% change in sales, share or choice, versus -1.76% in the 1988 review. Sales response strengthens about one percentage point every 25 years, but share and choice show no reliable trend. This suggests buyers increasingly time and size purchases around deals, with less of the promotion gain coming from other brands.
  • Durables respond more strongly to price than groceries. Both respond more strongly in introduction or growth than maturity or decline. Private labels and manufacturer brands show no reliable difference in price response.
  • Higher inflation is associated with stronger price response. Income growth has no reliable effect, so price increases look no safer in booms. Developed regions show no reliable difference, which may support similar pricing strategies across them.
  • Promotional price means checkout price divided by regular price. Per 1% price increase, predicted short-term responses are -3.63% for promotional price versus -2.36% for checkout price, and long-term responses -3.17% versus -3.78%, holding other study differences at their averages.
  • Omitting advertising or sales promotion overstates price response. Data source, weekly or monthly data, model form, quality or distribution controls, estimation method and accounting for differences between consumers do not reliably change estimates.

Key takeaway

Match pricing benchmarks to category, period and model before approving a plan.

Limitations

The studies cover consumer markets from 1956 to 1999, mostly groceries in developed countries, and measure sales, share or choice, not profit. The evidence excludes sharp income declines and extreme inflation or economic growth.

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, 42(2), 141 to 156. [https://doi.org/10.1509/jmkr.42.2.141.62296](https://doi.org/10.1509/jmkr.42.2.141.62296).