When Too Many Customers Becomes a Problem
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AbstractMany store managers look for ways to attract customers to their stores, assuming that a
fraction of them will purchase in the store, however, intuitively the marginal profit is expected
to be decreasing, and potentially negative. By using machine-learning algorithm to process
video data from store cameras, and in combination with traditional sales data, we test this
hypothesis. Exploratory analysis show that consumers’ purchase volume decreased, as
expected. However, surprisingly the marginal profit increased in some cases. Using a structural
model, we provide evidence to support a new mechanism of consumer behaviour. We suggest
that store congestion can decrease price sensitivity, which can yield higher marginal benefits.
Using counterfactuals, we are able to test different price strategies, and generate
recommendations for managers to take advantage of congestion in their stores.
All Author(s) ListCisternas F, Xu Kaiquan, Choi Pakyan
Name of ConferenceMarketing Science Conference
Start Date of Conference20/06/2019
End Date of Conference22/06/2019
Place of ConferenceNYU-Stern and University of Roma Tre - Department of Business Studies
Country/Region of ConferenceItaly
LanguagesEnglish-United States

Last updated on 2020-09-04 at 17:20