Combining geospatial data with sales metrics for deeper retail insights

Retailers today aren’t short on data. In fact, many are swimming in it. From daily till transactions to quarterly sales reports, the numbers paint a detailed picture of what is happening in-store. But numbers alone can’t always explain why those things are happening, or where the biggest opportunities lie.

That’s where geospatial data comes into play. By combining geospatial and sales data, retailers can move beyond basic performance reports and start to understand the local forces shaping customer behaviour. This approach is already being used by forward-thinking brands to fine-tune their operations, uncover new growth zones, and make smarter decisions with less guesswork. 

Ikano Insight, known for its deep roots in retail analytics and innovation, has developed the Area Prioritisation Engine, a powerful tool that merges sales performance with location-based insight. Let’s walk through a practical, step-by-step way to use this tech to transform your store performance analysis.

What this guide will cover

We’ll explore how to bring together sales data and location-based insight to drive smarter decisions in your retail business. We’ll start by outlining how to gather and segment your retail sales metrics effectively, before looking at how to layer in geospatial context, from customer catchments to product spend potential. You’ll see how these combined data sets unlock a deeper understanding of store performance, helping to identify high-potential areas, gaps in marketing impact, and local inventory mismatches.

We’ll also cover how to recognise trends and patterns across your store estate, using this insight to tailor marketing, stock, and pricing decisions by location. Finally, we’ll introduce Ikano Insight’s Area Prioritisation Engine and show how it simplifies the entire process, giving you visual, actionable insights on where to invest next for the biggest return.

Step 1: Gather and align your sales metrics

First things first: your retail sales metrics are the backbone of the analysis. This includes:

• Revenue per store
• Footfall data (how many visitors walk through the door)
• Average Order Value (AOV)
• Conversion rates

Segment these figures by individual store, postcode, or catchment area. The more granular the better. Clean, consistent data makes it easier to detect patterns and anomalies later on. If possible, include purchase channels too (in-store vs online), as this adds a valuable layer to your sales analytics for retail.

Step 2: Integrate location-based data layers

Now, enrich those sales figures with geographic context. While some tools focus on foot traffic or event data, the area prioritisation engine takes a more strategic view. It integrates:

• Where your customers live, not just where they shop
• What product categories they’re spending on
• The channels they prefer (online vs in-store)
• Relative category spend potential per area

This kind of retail location analytics helps identify true demand, even in areas where you might not yet have a store. It goes beyond just mapping footfall to uncover meaningful demand patterns and sales headroom.

Step 3: Link sales performance to location insight

This is where the magic happens. Match each store’s sales performance to its catchment-level data, down to hyper-local postcodes. With this view, you can:

• Pinpoint underperforming stores in high-potential zones
• Understand whether your best stores are close to maxing out their opportunity
• Identify performance gaps and act quickly where the biggest wins are possible

Unlike traditional store performance analysis, this method shows not only how a store is doing, but how much better it could be doing.

Step 4: Identify patterns and act on them

Once you’ve matched sales and location data, trends start to surface. These could include:

• Marketing gaps: Areas with high category potential but low sales performance

• Inventory misalignment: Regions where the product mix doesn’t reflect local demand can benefit from solutions like Product Recommendation Engines, which personalise offerings based on customer preferences and regional trends.

• Pricing insights: Local audiences with different willingness to pay

The area prioritisation engine helps spot these opportunities and turn them into tangible actions. Tools like Profiling and Segmentation also help refine your audience targeting and messaging by understanding behavioural and demographic differences across regions.

Step 5: Use the Area Prioritisation Engine

Here’s where things really start to scale. The Area Prioritisation Engine from Ikano Insight takes care of combining geospatial and sales data for you. It simplifies the process with a visual dashboard that shows:

• Where your highest opportunity areas are
• Where customer spend potential isn’t being met
• Where you should target marketing, pricing or even expansion

In other words, it handles the sales and location data integration automatically, so you can focus on strategy, not spreadsheets. It also serves as a model for location intelligence for retailers, by mapping sales gaps against demographic patterns, purchasing habits, and product affinities, essentially conducting deep value modelling to measure opportunity versus performance. You can instantly spot which stores are under-leveraged, which markets are ripe for entry, and where your efforts will have the biggest impact.

Summary: From data to direction

Let’s face it, retail performance analytics isn’t just about having more data, it’s about having the right data, working together. Data like:

• Sales data tells you what happened.
• Geospatial data in retail shows where and why.
• Combining geospatial and sales data allows you to act with confidence.

Retailers who take this integrated approach can adjust faster, compete smarter, and unlock new layers of value hidden in plain sight. And with Ikano Insight’s area prioritisation engine, these insights are not only accessible, they’re actionable.

Ready to dive in?

If you’re curious about how this all applies to your own store network, Ikano Insight offers hands-on support to help you explore the potential of your sales and location data. You can book a demo of the Area Prioritisation Engine to see how it surfaces hidden opportunities and simplifies decision-making.

While you’re on the site, it’s worth exploring their broader set of retail tools, including location analytics, profiling and segmentation, value modelling, product recommendation engines and location planning. Each solution is designed to help retailers fine-tune operations, from understanding who their customers are to knowing exactly where to focus next.

With the right tools and expert guidance, your data doesn’t just report the past, it actively shapes your future. So, whether you’re chasing growth, looking to optimise existing locations, or planning strategic expansion, there’s never been a better time to make your data work harder. 

Get in touch with the team or request a free opportunity report to take the next step.

Written by Matt Craddock

Global Head of Data & Analytics

Matt is a data science leader with expertise in heading up global teams that deliver game-changing solutions. He’s passionate about solving real-world problems with data-driven decisions, and combines hands-on technical skill with commercial insight to help businesses translate complex data into impactful outcomes.