Analyzing Purchase Decisions Using Dynamic Location Data

Tal Shoshani, Peter Pal Zubcsek*, Shachar Reichman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Retailers’ efforts to monetize consumer location data remain dominated by inefficient protocols (e.g., geofencing) that customize marketing interactions based solely on app users’ current location. Although extant trajectory mining techniques can remedy these shortcomings, they require high-frequency location data, which poses severe risks to consumers’ privacy. The authors present a novel method to extract marketing value from low-granularity urban mobility data and demonstrate its use in analyzing gas station choice to value customers. The data, also used to infer gas station visits, contain 1.06 million location records on nearly 27,000 devices observed near selected retailers including gas stations during a six-month period in Staten Island, New York. The authors pool consumers’ mobility trajectories from several days to dynamically calculate the distance of stores from consumers’ anticipated trajectories. They then supplement the data with station-level daily fuel prices and estimate a conditional logit model to assess how consumers trade off gas prices versus store distance. In addition to a generally high station loyalty, the authors find that consumers strongly prefer not to deviate far from their common trajectories for fueling trips. Applying their methods in a predictive context, the authors infer the value of newly acquired customers to the studied gas stations to be between $3.00 and $7.59.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalJournal of Interactive Marketing
Issue number1
StatePublished - Feb 2024


FundersFunder number
Henry Crown Institute of Business Research in Israel
University of California Berkeley
Israel Science Foundation2160/21
Tel Aviv University
Intelligent Manufacturing Research Center


    • choice modeling
    • geographical distance
    • location-aware marketing
    • mobile location data
    • pricing
    • store choice


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