RETAIL LOCATION MODELLING

We guide clients in selecting locations that maximize customer reach and business impact.

What data is required?

At a minimum, we need information on existing store locations, their sales, and cost structures. Ideally, we also include details about store characteristics such as size, assortment, and parking availability. When available, we use time-series data showing weekly, monthly, or yearly sales, along with store opening and closing dates.

We can also integrate data from Google Maps API, travel times, and geographic information on population, demographics, and economic factors to refine the analysis.

How does it work?

The approach depends on the data available and the business objective. For cases with limited data, we apply theoretical models to simulate store openings and closures. When rich historical and time-series data are available, we train predictive models that learn from these past examples to forecast the impact of new locations or store changes.

What is Retail Location Modelling?

Retail locations are powerful customer touchpoints and major investments. Choosing the right type of store in the right location can significantly influence profitability. Retail location models help predict how new store openings will affect overall sales, both in-store and online, while also estimating potential cannibalisation from existing outlets.

These models allow us to evaluate the profitability of proposed new sites or the impact of closing existing ones. We can bring this analysis to life through interactive data visualisation dashboards that show predicted sales and performance across regions. This gives decision-makers the clarity they need to grow their retail footprint with confidence.

Where have we used it?

We have successfully applied Retail Location Modelling in the retail sector to support data-driven expansion strategies and guide investment decisions.