Digitizing Local Search: An Empirical Analysis of Mobile Search Behavior in Offline Shopping
35 Pages Posted: 8 May 2015 Last revised: 25 Jul 2022
Date Written: July 24, 2022
Abstract
Mobile devices have become increasingly prevalent in the context of consumers’ offline search and shopping behavior. Compared to desktop PCs or laptops, the unique characteristics of mobile devices—including portability and real-time location sensing—can substantially influence consumers’ local offline search behavior. In this paper, we theorize and investigate the interplay between consumers’ real-time location and their search behavior when shopping offline. We develop two location-specific measures based on mobile device usage to analyze the association between a consumer’s real-time location and their search behavior: (1) present store distance and (2) present store density. We apply these measures to a unique dataset from a leading mobile product information app that includes 67 million unique search activities of 2.5 million consumers searching for 1.8 million different products. Our analyses provide strong evidence that store distance and store density can explain significant variation in consumers’ search behavior: detailed searches for product-specific attributes (search depth) increase with decreasing store distance and increasing store density. In addition, the number of jointly searched products within the same category (search breadth) increases with increasing store distance and increasing store density. Furthermore, we find that durable products reduce the main effects of store distance and density with search depth while amplifying search breadth relationships. These results can help offline retailers to better understand consumers’ location-specific demands for products, which could be used for more precise mobile in-app targeting and product assortment decisions.
Keywords: Consumer Search, Mobile Devices, Location-Based Services, Big Data, Random Coefficient Model
JEL Classification: M31, D83, C23, C25, L86
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