A large US publisher needed to increase audience revenue without increasing customer churn. They had annual price increases and wanted to understand price elasticity across their customer base to improve future yield.
Mather Economics developed a test and learn process using econometric models and A/B testing. Mather completed econometric models of past customer behavior following price increases and estimated price elasticity for the Publisher’s current customers. Mather selected accounts for the target and control groups, suggested prices by customer, tracked customer behavior, and reported the performance of the groups by week
The test results showed that significant differences exist in price elasticity within the customer base. Important factors for predicting an individual customer’s price elasticity are their tenure, their digital engagement, and their product mix. Demographics, acquisition channel, geographic location, and several other factors were also found to have significant effects on price response. The A/B test results validated the econometric model predictions. Applying these insights to the annual price increase process improved the yield by 40% or approximately $8 million per year.