How does online shopping affect offline price sensitivity?

2025-06-18

AI Summary

This study investigates whether the adoption of online shopping increases consumers’ price sensitivity in offline channels. Leveraging granular omnichannel transaction data from a major Brazilian pet retailer, we employ a staggered difference-in-differences design to identify the causal effect—first establishing online channel adoption as a key behavioral mechanism driving cross-channel price sensitivity shifts. To address price endogeneity, we propose a high-dimensional fixed-effects estimation algorithm integrated with a discrete-choice logit model. Results show a statistically significant increase in offline price sensitivity across three product categories, most pronounced for low-switching-cost items. Incorporating this empirically identified sensitivity shift into dynamic pricing strategies yields up to a 4.1% profit improvement. The paper contributes novel empirical evidence and a methodological framework for modeling cross-channel price responsiveness and optimizing profitability in omnichannel retail settings.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt online shopping with a retailer become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare ''adopters'' - customers who began shopping online after a period of offline-only purchasing - with ''non-adopters'' who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, using a novel algorithm to handle high-dimensional fixed effects and address price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. Counterfactual pricing simulations show that incorporating these behavioral spillovers into pricing strategies can increase firm profits by up to 4.1%. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity.

Problem

• How online shopping affects offline price sensitivity
• Impact of e-commerce on consumer offline purchasing behavior
• Online adoption as a driver of offline price sensitivity

Innovation

• Discrete choice logit model with individual heterogeneity
• Novel algorithm for high-dimensional fixed effects
• Staggered difference-in-differences approach for ATT