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Price Elasticity Variations across Locations, Time and Customer Segments: An Application to the Self-Storage Industry

The demand for services such as self-storage varies across locations, over time, as well as across customer segments. Service providers try to leverage these variations and maximize profits by adopting dynamic pricing policies. Implementing dynamic pricing, however, requires accurate estimates of price elasticities at a granular level. Using data from a leading self-storage retailer in Europe, we estimate a Bayesian Dynamic Hierarchical Linear Model (DHLM) to obtain price elasticities across 67 stores, for 21 bi-weeks, and high-valuation vs. low-valuation customer segments. Our estimation procedure accounts for price endogeneity, which is essential when using the estimated price elasticities to set the dynamic pricing policy. We find evidence of different price elasticities between stores and over time, which supports the practice of local dynamic price setting, as well as strong differences between customer segments. Overall, high-valuation customers are more price-sensitive than low-valuation customers. In addition, while the price elasticity of high-valuation customers remains stable, low-valuation customers become less price sensitive over time. This implies that a markup policy should be followed for low-valuation customers, but a more stable pricing regime may suffice for high-valuation customers. Last, we show the benefits of our model compared to a benchmark model with time-invariant price elasticities in determining the pricing policy, and discuss how our model can be applied to other industries that practice revenue management.