Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants

Abstract

We study consumer search behavior in a brick-and-mortar store environment, using a unique data set obtained from radio frequency identification tags, which are attached to supermarket shopping carts. This technology allows us to record consumers' purchases as well as the time they spent in front of the shelf when contemplating which product to buy, giving us a direct measure of search effort. We estimate a linear regression of price paid on search duration in which search duration is instrumented with a search cost shifter. We show that this regression allows us to recover the marginal return from search in terms of price at the optimal stopping point for the average consumer. Our identification strategy as well as coefficient interpretation are valid for a broad class of search models and we are hence able to remain agnostic about the details of search process, such as search order and search protocol. We estimate an average return from search of $2.10 per minute and explore heterogeneity across consumer types, product categories and category location in the store. We find little difference in the returns from search across product categories, but large differences across consumer types and locations. Our findings suggest that situational factors such as the location of the category or the timing of the search within the shopping trip are more important determinants of search behavior than category characteristics like the number of UPCs.

The Impact of Increasing Search Frictions on Online Shopping Behavior: Evidence from a Field Experiment

“…The second condition is that upon encountering these added search frictions, price-insensitive consumers would not always choose to incur the search cost to view the discounted items and instead would substitute discounted items with full-priced items. The third condition is that price-sensitive consumers would exert the extra effort required to find discounted items on the website, which is similar to what has been observed in physical settings (e.g., Seiler and Pinna 2017). We offer a simple model of search that generates these predictions under a basic set of assumptions and when search costs are moderate.…”

Consumer search in the U.S. auto industry: The role of dealership visits

“…Because of limited data availability, studies of offline search behavior are very rare. Two exceptions are Jain et al (2016) and Seiler and Pinna (2017). 4 Jain et al (2016) assess the effects of sales assistance and search on purchase incidence and expenditure using video recordings of a retail clothing store's product display area.…”

“…4 Jain et al (2016) assess the effects of sales assistance and search on purchase incidence and expenditure using video recordings of a retail clothing store's product display area. Seiler and Pinna (2017) measure the returns to price search by analyzing shopping cart movements in a supermarket. These two papers differ from ours in two aspects: they focus on the duration of search, i.e., time spent searching, rather than the number of searches and they study consumer search activity within a store, while we focus on search activity across stores.…”

What affects shopper’s choices of carrying devices in grocery retailing and what difference does it make? A literature review and conceptual model

“…There are only a few studies that have examined behavioral differences based on consumers carrying equipment selection, or lack thereof (see e.g. Larsen, Sigurdsson and Breivik 2017;Seiler and Pinna, 2017;Van den Bergh, Schmitt, and Warlop 2011). The shortage of studies focusing on the absence of carrying equipment is rather surprising, especially when the general trend worldwide shows that many consumers visit grocery stores more frequently, and have a greater preference for smaller store formats (Nielsen 2015), therefore preferring the use of carrying equipment to a lesser degree.…”