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Related Experiment Video

Updated: Aug 30, 2025

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
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Using gravity model to make store closing decisions: A data driven approach.

Mohsen Bahrami1, Yilun Xu2, Miles Tweed3

  • 1MIT Connection Science, Institute for Data, Systems, and Society (IDSS), Massachusetts Institute of Technology, 77 Massachusetts Ave, E17, Cambridge, MA 02139, USA.

Expert Systems with Applications
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

Closing underperforming stores can be risky. This study introduces a new model to predict revenue loss, showing chain store performance depends on store interactions, not just individual store success.

Keywords:
CBG, Census Block GroupCOVID-19 pandemicClosure decisionDDM, Dynamic Decision ModelingEconomic recessionFinancial crisisGIS, Geographical Information SystemsGWR, Geographically Weighted RegressionHuff gravity modelIBLT, Instance Based Learning TheoryMCI, Multiplicative Competitive InteractionNAICS, North American Industry Classification SystemNYC, New York CityOLS, Ordinary Least SquaresPSO, Particle Swarm OptimizationRL, Reinforcement LearningSME, Small and Medium sized EnterpriseStore closing

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Area of Science:

  • Retail analytics
  • Spatial modeling
  • Operations research

Background:

  • The COVID-19 pandemic has led to financial difficulties for many businesses, necessitating store closures.
  • Decisions on store closures are often based on individual store performance, potentially overlooking broader chain impacts.
  • Existing research lacks comprehensive models for strategic store closure decisions.

Purpose of the Study:

  • To develop and validate a novel model for predicting the impact of store closures on chain revenue.
  • To provide a data-driven approach for optimizing store closure decisions in retail chains.
  • To analyze the interconnectedness of stores within a retail chain.

Main Methods:

  • A modified Huff gravity model with a new attractiveness factor was proposed.
  • A forward-backward approach was utilized for model training and prediction.
  • Large-scale spatial, mobility, and spending datasets from New York City department stores were analyzed.

Main Results:

  • The proposed model's store closure recommendations may differ from those based solely on single-store performance.
  • Store closures can lead to significant revenue loss due to complex inter-store customer flow dynamics.
  • The performance of a retail chain is an emergent property of store interactions, not merely an aggregation of individual performances.

Conclusions:

  • The developed model offers valuable insights for strategic store closure decisions.
  • Managers can utilize this approach to mitigate revenue loss associated with downsizing.
  • Understanding store interdependencies is crucial for effective retail chain management.