  A particular phenomenon of interest in Retail Economics 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 analyse
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 on 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 GreaterLondon
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 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.
