Store Clustering and Local Pricing: Boost Your Margins with AI

Every store has its own customers, and every catchment area its unique characteristics. Understanding these differences is essential to optimising prices and maximising margins. Thanks to artificial intelligence and store clustering, it is now possible to implement precise and scalable local pricing.

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Why Every Catchment Area Needs Tailored Pricing

The diversity of UK catchment areas is striking:

• Families in suburban areas are highly sensitive to prices on bulk frozen foods.
• Older shoppers stick to trusted household staples, such as Heinz baked beans or Marmite.
• Surf wax sells better in coastal towns like Newquay than in central Birmingham.
• In Yorkshire, a good local ale is often preferred over imported beers—but only if it’s priced attractively.

These local differences are driven by regional preferences, socio-economic profiles, tourist activity, and even home storage capacity.

Local Pricing: An Opportunity to Boost Margins

Retailers often struggle to increase margins nationally. According to an analysis by the Competition and Markets Authority (CMA), the average operating margins of British supermarkets fell from 3.2% to 1.8% between 2022 and 2023. Local characteristics present untapped opportunities for local pricing. They allow retailers to identify elastic and inelastic products, increase traffic with targeted margin investments, and maximise profits by raising prices on inelastic products.

If you want to learn more about price image and the importance of local-level pricing, check out this article.

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Mercio Makes Large-Scale Local Pricing Possible

In an ideal world, retailers would know every local specificity for each catchment area. Some invest in market research to understand local characteristics. But applying these insights to pricing was often impossible with legacy systems. Mercio changes this by enabling precise, margin-generating pricing at scale.

Speaking of limited pricing solutions: if you’re still using Excel to manage pricing, this article is worth a read.

Gradual Implementation with Catchment Typologies

Before implementing fully localised pricing, retailers can start by identifying different types of catchment areas and gradually adjusting prices. Mercio effectively supports this intermediate step.

Data-Driven Store Clustering

Historical Sales Data Analysis

AI identifies popular products and shopping trends, grouping stores with similar behaviours. This analysis sharpens customer knowledge and focuses efforts on the most price-sensitive segments.

Geographic and Socio-Economic Segmentation

Clusters can be built based on population density, income levels, or competitor proximity to adapt pricing to each local context.

Leveraging Customer Purchase Behaviour

Customer data, loyalty cards, and purchase history enable highly precise segmentation and dynamic price adjustments based on customer expectations.

Once clusters are defined, pricing strategies can be applied to each store group according to customer price sensitivity and local competition. Combined with AI-driven product matching, this achieves unmatched optimisation while remaining competitive and maximising margins.

AI + Pricer: An Evolving Clustering Approach

Mercio allows users to create and manage clusters independently, with real-time adjustments and no technical team required. Results are always accessible and analysable at the store level, enabling precise and informed decisions. Users can visualise the impact of price adjustments on each store, ensuring a local pricing strategy while maintaining global coherence.

With an intuitive interface and customisable segmentation criteria, Mercio improves responsiveness while maintaining in-depth performance analysis at every level.

Conclusion: Local Pricing as a Sustainable Performance Lever

Clustering and AI enable more informed and targeted pricing decisions. By analysing historical, geographic, and behavioural data, retailers can better understand market dynamics and adjust prices for each store cluster. Local pricing simplifies teams’ work while increasing their strategic maturity.

In summary: grouping stores into similar clusters allows tailored prices for customers, franchisees, or members, directly improving price indices and margins.

Discover how Mercio can help you implement an effective local pricing strategy adapted to your catchment areas. Contact us for a demo.