How retailers are leaving billions on the table pricing flagships like failing shops
Store closures are a common sight on our streets. Last year, the Centre for Retail Research estimated there would be 17,350 store closures in the UK in 2025. But this doesn’t tell the whole story. While overall footfall, especially in suburban locations, is dropping, major city flagship stores and high streets are seeing notable increases. New data has revealed that during the 2025 festive season, for example, “footfall in London’s West End reached its highest level since 2020”.
Regional differences in footfall are to be expected. Yet many retailers price products in high-performing urban flagships identically to struggling suburban stores. Consequently, they are leaving billions on the table. An Oxford Street flagship store paying five times more rent than a suburban outlet shouldn’t operate on the same pricing strategy. But as footfall rises in city centres and collapses elsewhere, ‘fair’ pricing has quietly become financial negligence.
With thousands of more store closures expected this year, retailers are missing a far bigger story than decline: pricing failure. Instead of introducing blanket pricing and blunt clearance strategies – which destroy margin – teams need to adopt a location-led pricing strategy guided by AI-driven markdowns. In doing so, they can turn geographic variances into profit.
Viewing store closures as margin recovery, not brand destruction
Let’s look at a typical liquidation playbook for a closing store. The store announces its closure, slashes 70% off all its products and, as a result, destroys brand equity. The impact has far-reaching consequences for both the brand’s reputation and its balance sheet. But how could an AI-driven markdown strategy transform this liquidation process?
Rather than simply declaring a store is closing and then immediately discounting products, a retailer could start the process 90 days earlier. With an AI platform continuously monitoring factors like sell-through velocity, inventory cover and historical sales data, the retailer could access insights highlighting high-performing inventory that should be transferred to thriving locations. Consequently, this would enable it to liquidate strategically.
The potential result of this strategy? The shop could close at 40% instead of 70%, preserving better margin and, crucially, maintaining brand integrity.
The inventory reallocation pricing gap
It’s not only about how products are discounted in the shop that is closing. If aretailer was to close 66 stores (like Macy’s), where does all of the stock go? Currently, it’s dumped into remaining stores at identical prices. But this is a massive missed opportunity.
Imagine if a coat failing in a store in a small regional town was transferred to a flagship store in Manchester. A traditional retail strategy would maintain the price, treating the product as the same across the board. But what if the coat got repriced £20-40 higher depending on the demand signals for that location? That’s exactly the price calculation that AI could recognise and suggest to retailers. Even though the product is the same, it’s moving to a different market, and so this should mean it sells for a different price.
Competitive closure creates pricing power
If a retailer’s competitor closes its local store, then the retailer’s pricing should change immediately. Less competition instantly gives a brand pricing power. But how a brand responds depends on the wider context of where the store is.
If you’re the last retailer standing in a dying centre, then you need to liquidate faster. But if you’re the sole player after a competitor exits a thriving high street, then it’s time to raise prices and reduce promotions.
Again, this requires an agile mindset coupled with using advanced AI technology. AI inventory and pricing platforms, for example, can monitor competitive closures and auto-adjust local pricing to capitalise on market shifts that most retailers miss entirely.
The store tiering revolution
Retailers are finally admitting what’s been obvious for years: not all stores are made equal. ‘A-tier’ urban flagships operate as brand showcases, with premium pricing and slower markdowns strategies. Last year, for example, JD Sports opened its ‘largest store in the world’ at Manchester’s Trafford Centre, with a focus on elevating the in-store experience for customers. ‘C-tier’ locations, on the other hand, exist to turn inventory fast before closure. But how do you know what category to tier stores?
By assessing various markdown cadences, promotional participation and margin targets, AI-driven platforms can automatically tier stores based on their performance data and adjust their pricing strategy accordingly. Brands still have one overarching estate to manage, but this means they can employ multiple strategies for individual stores to optimise their overall performance.
The digital knock-on effect
Evaluating regional performance shouldn’t be limited to a store’s physical footprint alone. Each store also creates a ‘halo effect’ that influences local web demand and overall digital performance. For example, closing a store may not only reduce in-store revenue but could also negatively impact online performance in that region.
A store’s physical presence drives brand visibility, trust, convenience (for example, returns, click & collect, etc.), and local marketing awareness – all of which can support web traffic and conversion. Additionally, major in-store events (like a 70% clearance) can materially influence customer behaviour online. Customers who see deep discounting in-store may delay online purchases, expect similar markdowns digitally, or shift their buying patterns entirely.
AI turns geographic variances into profit strategy
Store closures will continue to be a feature on UK high streets. But brands need to reframe how they view them. What matters is not so much the store closing itself, but how the retailer responds with its pricing strategy. Instead of employing traditional liquidation and blanket discount strategies, a modern strategy accounts for geographic differences and dynamically adjusts discounts and markdowns to best preserve margins.
At the heart of this approach is AI. AI-driven markdown platforms transform location performance data into dynamic pricing strategies that recognise urban premiums, optimise closure liquidations, price transferred inventory for destination markets and respond to competitive changes in real-time. It’s the difference between treating your estate as one homogenous entity and recognising that geography is your most underutilised pricing lever.
Retailers recognise that not all stores are made equal. Their pricing strategies should do the same.
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