What 2026 Means For Retail Location Planning

Key Insights

• Physical stores have moved from transaction points to “immersive brand experience hubs” where it’s all about the experience
• By 2026, agentic commerce will prioritise retailers with stock within a 15-minute radius of consumers, making proximity more critical than traditional footfall metrics
• Location intelligence is now essential for identifying where target customers live, work, and shop, allowing retailers to tailor store experiences and optimise investment decisions
• Retail success in 2026 will depend on how well brands connect their digital and physical environments, using real-world data to continuously plan, activate, and measure performance

The last 10 years have changed drastically in retail. In the 2010’s, we witnessed a shift from shopping in person to shopping online (with online sales now accounting for over 27% of total retail sales), and now, we’re seeing how retail is adapting with AI. 

For retail location planners like us at GMAP Analytics, location planning is no longer simply about where a store is placed, but more so about how its physical point interacts with its online partner. 

We’re predicting that 2026 will see brands using location intelligence to turn physical spaces into a ‘phygital experience’, where consumers can discover and connect with the brand. And we believe that retail location and storefront positioning are more important than ever before...

Factors Affect Retail Store Location

As consumers can always order online, the role of the store has been less about being a transaction point and more about being an immersive experience. It’s now a connection point, a place for the brand to tell its story, a place for customers to feel emotionally invested. 

Naturally, this creates a trade-off between opening high-experience sites and closing underperforming locations. Let’s take a look at some of the key factors affecting retail store location strategy today.

Data-Driven Location Decisions

Retailers are using data analysis tools to group similar shoppers together and choose store locations that match specific interests, such as health and sustainability. By studying what people buy, who lives in the area, and how they behave, brands can pinpoint areas where their ideal customer lives. 

This means retailers can design store experiences, from the products they stock to the services they offer, that appeal to local shoppers. It’s like when we partnered with Holmes Place to develop their location strategy for the launch of their Evo gyms across Europe. Our analysis identified optimal locations based on catchment characteristics, competitor proximity, and local demographic profiles - and ensured each site was positioned where target customers actually lived, worked, and travelled.

Optimising Store Portfolios

To keep costs under control, brands can use tools like our very own RetailVision to plan their store portfolio, deciding which locations to expand and which to close based on growth potential and how competitors are performing. These platforms combine foot traffic data, local population characteristics, and sales figures (three of the most important factors in retail store location planning) to create a complete picture of each store's prospects.

Retailers can test different scenarios to understand what would happen if they closed struggling stores or moved to better locations. This evidence-based approach helps brands avoid the expensive mistake of keeping unprofitable shops open while spotting untapped markets for growth.

Artificial Intelligence and Agentic Commerce 

Agentic commerce will by far be the fastest change in 2026 for retailers. This is where AI assistants will shop on behalf of consumers, evaluating products on learned preferences, budget constraints and real-time availability. 

This changes the location priority from a visibility exercise to a supply chain and optimisation challenge - it becomes a question of “how can I keep what the AI recommends stocked?”.

How does agentic commerce work?

AI agents will use a consumer's location and browsing history to suggest and even purchase products automatically. For a retailer to be "suggested" by an AI agent, having stock in a location near the consumer becomes a competitive advantage. These systems prioritise convenience and speed, meaning a store with available inventory within a 15-minute radius will rank higher than a competitor requiring next-day delivery. 

Rather than focusing purely on customer footfall or demographics, being geographically positioned within the AI's ‘recommendation radius’ is now a huge factor affecting retail location decisions.

Our prediction is that by 2026, retail search will feel like asking a knowledgeable assistant for advice: these systems will search local stock levels and reviews to give a single recommendation. Unlike traditional search engines that present multiple options, agentic AI will curate a shortlist based on price, reviews, sustainability credentials, and delivery timeframes. 

Retailers must optimise their product data feeds, inventory transparency, and real-time stock to ensure AI agents can access and rank their offerings effectively, especially as UK consumers expect roughly 7% of their total online purchases to be made via AI agents by 2030.

And as AI is being used for consumers, retailers are also using AI for location planning. We have more datasets than ever before, so it’s important to analyse them with ample processing power, helping to provide all the information necessary to draw accurate conclusions. 

Connecting Digital and Physical Environments

When you consider how much our lives have changed in recent years, from rarely relying on devices to using them every day, it’s not surprising that retail is evolving too. Location planning now caters to online shoppers who still want, and are actively looking for, a great in-person experience. This highlights the growing importance of location in retail business today.

A shopper who enjoys both requires: 

A combination of online and offline shopping: Retail parks and click-and-collect hubs are thriving by offering the convenience shoppers demand.

Location intelligence: Location planning now uses spatial interaction models to simulate spend at specific stores, helping brands understand how physical sites support digital sales in the same catchment.



How advertising and the planning cycle play into location data

Location data has now become a more reliable signal of consumer demand, allowing brands to advertise when a customer is near a store. By using location data, marketers can reach ‘high intent’ audiences (like gym goers or travel planners) in the moments they are physically active in the market. 

Marketing used to be far more broad, now it’s behaviour-based and as a result, far more targeted - think a fitness brand promoting their range of protein powders to someone just after they leave a gym. 

Retailers are now adopting a "Plan, Activate, Measure" cycle that creates a continuous feedback loop between physical and digital strategies. Real-world movement data (capturing footfall patterns, dwell times, and cross-visitation between locations) is fed back into the planning cycle to inform the next round of site selection or media spend. 

This iterative approach allows brands to test hypotheses about catchment areas, measure the incremental impact of new store openings on foot traffic, and adjust marketing budgets based on actual consumer behaviour rather than demographic assumptions. The result is a more agile, responsive approach to location strategy.

We've embedded this evidence-based approach into our retail location planning consultancy - combining our competitive retail insights and geodemographic data with catchment analysis and performance modelling. 

Whether we're helping sports fashion retailers identify expansion opportunities, analysing store network performance for health and beauty brands, or developing multi-city growth strategies for global retailers, our work centres on turning real-world location data into confident, defensible decisions about where to invest next.

So, how do you turn data into better location choices?

Here at GMAP, our expectation is to see retail rapidly evolve even further over the coming years. Physical storefronts remain absolutely crucial in maintaining brand loyalty and discovery, but decisions for where they’re located are increasingly based on AI and digital demand. 

We believe that with all these recent and rapid changes, location planning and data-driven location intelligence are more important than ever before to ensure a brand makes the right investments. 

If you would like to learn more about GMAP’s location intelligence and planning consultancy services, or our range of tools and software designed to help supercharge your business, please get in touch today!