Unlocking the Secrets of Choosing the Best Supermarket Location in the UK: A Data-Driven Approach

Selecting the ideal location for a supermarket is a strategic decision that can significantly impact a store's success. In the competitive UK market, where consumer preferences, income levels, and population density vary widely, leveraging the right data is crucial. This blog post will guide you through how to use store location data, demographics data, and income data, along with powerful tools like Voronoi polygons, to make informed decisions about where to establish your supermarket.

Understanding the Key Data Sources

Before diving into the analysis, let's explore the datasets and tools you'll need:

1. Store Location Data:
   • UK Store Locations Data provides the geographical coordinates and addresses of existing supermarket stores across the UK. This data allows you to understand the distribution of competitors and identify potential gaps in the market.
2. Demographics Data:
   • UK 2023 Demographics Data includes detailed information on population density, age distribution, and other socio-economic factors across different regions. This data helps identify areas with the target customer base for your supermarket.
3. Income Data:
   • UK 2023 Average Income Data provides insights into the average income levels across different regions. Understanding the income distribution is vital for positioning your supermarket in areas where customers have the spending power that matches your product offerings.
4. Voronoi Polygons and Geolocet Data Service:
   • The Geolocet data service can generate Voronoi polygons around stores of interest, which helps in analyzing the spatial distribution of supermarkets. Geolocet’s service also calculates the population and average income within each polygon, giving you precise insights into the potential customer base and competitive landscape within specific areas.

How to Combine These Datasets for Location Analysis

Combining these datasets enables you to perform a comprehensive analysis to identify the best supermarket location. Here’s how you can do it:

1. Identify Market Gaps Using Store Location Data:
   • Start by mapping out the existing supermarket locations using the UK Grocery Store Locations Data. Visualizing these on a map will help you identify areas with high supermarket density and, more importantly, regions that are underserved.
2. Overlay Demographics Data to Understand Customer Distribution:
   • Next, overlay the UK Demographics Data onto the map. Focus on areas with a high population density and a favourable age distribution for your target market. For instance, if your supermarket focuses on fresh produce and organic products, areas with younger, health-conscious demographics may be more appealing.
3. Incorporate Income Data to Align with Customer Spending Power:
   • Integrate the UK Average Income Data to pinpoint regions where the average income aligns with your supermarket's pricing strategy. High-income areas may be more suitable for premium supermarket brands, while lower-income areas may benefit from discount or value-focused stores.
4. Use Voronoi Polygons for Competitive Analysis:
   • Utilize the Geolocet data service to create Voronoi polygons around existing supermarket locations. Each polygon represents the area closest to a particular store compared to others. Geolocet’s powerful data service can then calculate the population and average income within each polygon, giving you precise insights into the potential customer base within specific areas.
   • This analysis provides a clear picture of the market share and competitive landscape, allowing you to identify regions where your supermarket can thrive with minimal competition and optimal customer demographics.

Case Study: Applying the Approach

Let's consider a hypothetical scenario where you are planning to open a new supermarket in Greater London.

Step 1: Map Existing Stores:

• Use the UK Grocery Store Locations Data to plot all existing supermarkets in Greater London. You'll likely notice clusters in central areas and potential gaps in outer suburbs.

Step 2: Analyze Population Density:

• Overlay the UK Demographics Data to identify densely populated suburbs where there are fewer supermarkets. These areas represent untapped markets with potential customers.

Step 3: Assess Income Levels:

• Filter these suburbs by average income using the UK Average Income Data. If you're targeting a mid-range supermarket, look for areas with middle-class income levels that are currently underserved by supermarkets.

Step 4: Create and Analyze Voronoi Polygons

• Generate Voronoi polygons for the supermarkets in the identified suburbs using Geolocet's data service. Geolocet can calculate the population and income within each polygon to assess the potential customer base and compare it against the competition.
• A polygon with a high population and favourable income, but with no nearby competitors, would be an ideal location for your new store.

Final Thoughts

By leveraging store locations, demographics, and income data, along with tools like Voronoi polygons, you can make data-driven decisions to select the best supermarket locations in the UK. This approach not only helps in identifying lucrative markets but also in minimizing risks associated with high competition.

The datasets and tools provided by Geolocet empower businesses to perform in-depth market analyses, making it easier than ever to find the perfect spot for your next supermarket. As the retail landscape becomes increasingly data-driven, staying ahead of the curve with these insights is not just an advantage—it's essential.