Causal Analysis on the Anchor Store Effect in a Location-based Social Network

Anish K. Vallapuram, Young D. Kwon, Lik Hang Lee, Fengli Xu, Pan Hui

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

A particular phenomenon of interest in Retail Eco-nomics is the spillover effect of anchor stores (specific stores with a reputable brand) to non-anchor stores in terms of customer traffic. Prior works in this area rely on small and survey-based datasets that are often confidential or expensive to collect on a large scale. Also, very few works study the underlying causal mechanisms between factors that underpin the spillover effect. In this work, we analyze the causal relationship between anchor stores and customer traffic to non-anchor stores and employ a propensity score matching framework to investigate this effect more efficiently. First of all, to demonstrate the effect, we leverage open and mobile data from London Datastore and Location-Based Social Networks (LBSNs) such as Foursquare. We then perform a large-scale empirical analysis of customer visit patterns from anchor stores to non-anchor stores (e.g., non-chain restaurants) located in the Greater London area as a case study. By studying over 600 neighbourhoods in the Greater London area, we find that anchor stores cause a 14.2-26.5% increase in customer traffic for the non-anchor stores reinforcing the established economic theory Moreover, we evaluate the efficiency of our methodology by studying the confounder balance, dose difference and performance of the matching framework on synthetic data. Through this work, we point decision-makers in the retail industry to a more systematic approach to estimate the anchor store effect and pave the way for further research to discover more complex causal relationships underlying this effect with open data.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
EditorsJisun An, Chelmis Charalampos, Walid Magdy
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-209
Number of pages8
ISBN (Electronic)9781665456616
DOIs
Publication statusPublished - Nov 2022
Externally publishedYes
Event14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 - Virtual, Online, Turkey
Duration: 10 Nov 202213 Nov 2022

Publication series

NameProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

Conference

Conference14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Country/TerritoryTurkey
CityVirtual, Online
Period10/11/2213/11/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Communication

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