Branch expansion with predictive analytics
Thursday, March 24, 2022
Predictive location, and foot traffic analytics reveal valuable information that helps retailers to choose the right location when setting up geographic expansion strategies.
How are predictive analytics models used to determine the optimal location for a new facility?

Predictive analytics is a type of statistical analysis that uses data mining, statistical modeling and machine learning to extrapolate trends from historical facts and current events and is often used for risk assessment and decision making.

Today, this tool within retail, encompasses loyalty metrics, store size, consumer demographics, inventory management, product assortment, to understand your target market, differentiate your products and services, and determine the ideal locations to expand in a way that maximizes sales and customer satisfaction.

You might be interested in: “How to create the best branch expansion strategy?

How do you get this data to train these models?

Retailers can find relevant data through location analytics, which uses GIS data to understand different geographic and spatial factors, while using statistical techniques to predict the best location for each store.

Other data used to train these models can include internal sales data, demographic details and population profiles. Other macroeconomic indicators are also used, such as the purchasing power of the population, the economic growth rate and the cost of opening new points of sale, among others. Read the complete article here

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