In today’s fiercely competitive business landscape, staying ahead means not only understanding your customers, but also keeping a close eye on your competitors. Traditional methods of gathering competitive intelligence—such as surveys, audits, or third-party reports—tend to be slow, static, and often out of date.
That’s where location data comes into its own. Real-world, real-time, and rooted in how people move and behave, location data offers businesses a dynamic new edge to benchmark competitors, uncover market gaps, and make smarter, faster decisions.
What is Competitive Intelligence?
Competitive intelligence involves collecting and analysing information about your competitors—where they operate, how they perform, who their customers are, and what trends they’re responding to. The aim? To anticipate market shifts and gain a strategic advantage.
While competitive intelligence has typically relied on public reports or mystery shopping, these approaches often miss hyperlocal insights. This is where location intelligence steps in.
Why Location Data is a Game-Changer for Competitive Intelligence
Location data provides context. It tells you where things are happening—where competitors are opening new stores, where footfall is rising, where your target audience lives or shops, and even where there’s unmet demand.
Here’s how it enhances competitive intelligence:
Benchmark Competitors at a Hyperlocal Level
Using geospatial tools and AI-powered insights, you can benchmark competitors at the level of individual stores. You can assess:
- The presence of existing stores’ network. What catchments they are serving and where does the whitespace exist? Where are the competitors clustered? Why certain locations are chosen and why certain locations are avoided?
- Customer demographics at each location, how does the pricing and product offerings vary at each location, and its customer demographics
- Proximity to key landmarks, high streets, or transport hubs, how do footfall patterns vary by time of day, week, or season?
- Overlap and cannibalisation risk among their stores. How far are their target customers willing to travel to the store? Where are shoppers coming from?
- Evaluate competitor performance with revenue prediction at a specific location for a specific product based on the location and audience overview
This level of detail helps you understand what’s working for them and where their vulnerabilities lie. Let’s elaborate on each of these points.
Mapping Competitor Store Networks to Spot Whitespace Opportunities
Using location data, brands can analyse where competitors are clustered and where untapped markets (whitespace) exist. For example, Zudio, a budget fashion brand under Tata Group, has rapidly expanded to Tier 2 and Tier 3 cities by identifying markets where competitors like Max or Pantaloons had low penetration. As of early 2024, Zudio had over 400+ stores, many in cities where other fashion brands hadn’t established a strong presence. This whitespace identification helped them scale quickly and cost-effectively.
Studying Local Demographics to Benchmark Competitor Positioning
Competitor analysis isn’t just about store locations, it’s also about who they are serving. Location intelligence allows you to benchmark which demographic segments your competitors are targeting in different catchments. For instance, DMart adjusts its product pricing and SKUs based on neighbourhood income levels. In comparison, Reliance Smart Bazaar targets a more premium audience in similar zones. Retailers can benchmark such strategies using real-time demographic overlays to fine-tune their own assortments or identify underserved segments.
Footfall Patterns and Location Efficiency Benchmarking
Footfall data is a powerful lens for assessing competitor store performance without needing internal data. Using footfall heatmaps, brands can see which competitor locations attract higher footfall and at what times. For instance, high-street locations like Connaught Place in Delhi or T Nagar in Chennai show massive weekend surges compared to malls. Quick commerce brands and QSRs often benchmark peak footfall times of rivals to optimise store hours, delivery hotspots, and staffing.
Detecting Store Overlap and Cannibalisation Risks in Competitor Networks
Location data helps reveal if a competitor is cannibalising its own stores. For example, Domino’s India has over 1,800 outlets, and in dense urban markets, some stores are less than 1 km apart. With location intelligence, you can study catchment overlaps, shopper travel patterns, and store spacing to understand how efficiently your competitor is operating, or over-extending. This insight helps you benchmark how close you can set up shop without falling into the same trap.
Predicting Competitor Store Performance Using Revenue Proxies
Even without access to internal sales data, you can estimate a competitor’s store performance using location-based indicators, such as average footfall, category demand, local income levels, and purchasing behaviour. For example, a recent GeoIQ case study for a retail chain used location AI to benchmark competing stores’ revenue potential by overlaying footfall data with affluence indicators and category saturation. The result: the brand avoided high-rent areas with low estimated ROI and opened in a micro-market where competitors were underperforming.
Final Thoughts
Competitive intelligence today is about knowing not just who your competitors are, but exactly where and how they operate. How are they performing, what are the factors contributing to their success or failure, and more.
Location data gives you the tools to benchmark competitors, identify strategic gaps, and act decisively. Whether you’re launching a store, testing a new market, or defending your turf, location intelligence keeps you one step ahead.
