Expansion Plans Data Science Models

Business intelligence and technology tools enable retail businesses to deeply analyze the behavior of their customers inside and outside their stores, in order to identify the ideal location for future expansion plans.

Thursday, September 30, 2021

The data science models are the third phase of location and mobility intelligence analysis, helping retailers to assess the market potential of areas of interest for new store openings and identify optimal potential locations for expansion. These are divided into three steps:

Step 1: Defining the customer profile

Using different approaches, we define the customer profile based on the business within the desired geographic locations, taking into account economic characteristics such as income level and consumer preferences on social networks such as Facebook, Google, Twitter, etc.

Step 2 - Identification of the characteristics of the area of interest by gathering all relevant information such as:

  • The population and its characteristics (income, spending composition). 
  • The economic and business evolution of the market of interest. 
  • Consumer preferences on social networks. 
  • Brand awareness in social media.

You may be interested in: "Walmart Vs. Soriana: Consumer Foot traffic Analysis".

Based on the previously defined customer profile, the number of potential customers is estimated both in locations where there are already points of sale, as well as in areas where there is interest in expansion. Combining these data layers with sales information (tickets, invoices, etc.) from each store, predictive models are generated that estimate the sales and revenue potential of potential new openings.

Step 3 - Evaluation of the commercial potential of the areas of interest

The characteristics of each potential area of interest are analyzed, considering the performance of each store, market saturation and the number of total potential customers detected. The results are compared with the characteristics of the locations where established stores already exist and where they do not, taking into account:
  • The detailed profile of each area of interest 
  • An analysis of the competition 
  • The health and trends of the market of interest 
  • Monthly, bi-monthly, quarterly, etc. evolution of local stores, both in number of stores and employee performance.
  • Main competitors 
  • Customer loyalty on social networks. 
  • Google search trends.  
  • Route to market for new store openings  
  • Sales estimates for each potential new store.

Also read: "Outdoor advertising: How data analytics helps".

The final product is a listing of cities of interest ordered by their business potential, including a sales estimate for each potential new store.
In order to maximize the efficiency of the analysis, tools such as predictive models and artificial intelligence are implemented to answer:
  • What is the maximum potential of my brand? 
  • What business problems can be solved with store location analytics
There are several applications for this type of solutions, as it is possible to evaluate whether a specific store would receive enough foot traffic, whether there are similar businesses nearby, whether the location where the store would be located is more suitable for another type of business, among others. The results provided by these analyses provide key information for making critical business decisions.

At PREDIK Data-Driven we support any type of sector or industry to implement this type of intelligence in their operations to maximize their revenues, position their brand and optimize expenses in their expansion processes.

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