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GIS driven analytics approach  to scientifically identify locations for opening a new store helped a retailer save 90% of time investment in whitespace identification

Challenge

INDUSTRY OVERVIEW

As retail businesses grow and expand, opening new stores in strategic locations becomes crucial. A new store's success can be impacted by a variety of elements, including population density, income levels, foot traffic, and more.

Data-driven decisions can be made by identifying white spaces on a map and analysing population density, income levels, and other pertinent information. This could increase their profitability and success.

THE PROBLEM

Given the significant time, money, and resource commitment required, various difficulties are encountered while opening a new store in an area. Few are as listed:

  1. Determining the appeal of a property
  2. The use of incomplete data to identify catchments
  3. Dependence on the field team for location surveys

Finding the most important parameters and how they interact to affect sales can be difficult, even with the right data at hand.

Why were we brought in?

To open a successful and profitable store in any given location, it becomes crucial to address the issues mentioned in the previous section.

Ganit has created a new store opening tool (NSO) that programmatically identifies all open spaces in current regions and ranks them at the national level. This GIS-driven analytics platform assists retail stores in choosing the ideal locations for new stores by analysing data on a variety of factors, including population density, income levels, foot traffic, and demographics.

White spaces are places with potential for new businesses but no existing competitors, and retail stores can gain a competitive advantage by identifying these areas.

Our approach

Methodology

The tool developed by Ganit takes into account a vast amount of data to leverage the right factors affecting the success of a new store which involves data collection from publicly available sources such as Here API for identifying points of interest such as the number of schools, retail outlets, ATMs and so on, Census data to capture the literacy rate, sex ratio and various other sources.

The sourced data is then fed into the new store opening tool, which uses advanced algorithms and machine-learning techniques to identify the best locations for new stores based on various parameters. Uber’s H3 grid system has enabled us to divide the cities into smaller hexagons which are used to improve the accuracy of data scraping and modelling. The tool then gives out a probabilistic ranking of white spaces/stores based on success rate.

Features of the tool

  1. Ability to draw a geometric shape – rectangle, polygon, circle
  2. Ability to measure the distance between two points, specifically,
    - width of the road
    - distance from nearest DC
  3. Display all prospective areas in a selected region
  4. Display KPIs
    - Population
    - Affluence
    - points of interest, e.g. number of schools etc.
  5. Alert score based on presence of competitors

A valuable difference

Impact

  • Reduced average cycle time from property identification to ribbon cutting to 15 days.
  • A web tool to identify white spaces within an area, where new stores can be opened basis estimated success potential
  • Better prediction of risk factors
Success stories

See the impact that we make on our
cross-industry client base.

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