{"id":3663,"date":"2024-11-01T10:51:37","date_gmt":"2024-11-01T10:51:37","guid":{"rendered":"https:\/\/www.footfallcam.com\/blog\/?p=3663"},"modified":"2025-11-03T10:12:50","modified_gmt":"2025-11-03T10:12:50","slug":"case-study-retailer-leverages-ai-model-to-close-non-performing-stores-strategically","status":"publish","type":"post","link":"https:\/\/www.footfallcam.com\/blog\/2024\/11\/case-study-retailer-leverages-ai-model-to-close-non-performing-stores-strategically\/","title":{"rendered":"Case Study: Retailer Leverages AI Model to Close Non-Performing Stores Strategically"},"content":{"rendered":"\n
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A leading retailer faced the annual challenge of\u00a0identifying and addressing non-performing stores<\/strong>. Despite overall business success, the company had to remain vigilant, addressing issues related to\u00a0poor operations, data accuracy, and potential job losses<\/strong>\u00a0at underperforming locations. Closing a store is a complex, costly decision that impacts employees, customers, and the brand. To make the best decisions, the retailer sought to leverage\u00a0AI-powered insights<\/strong>\u00a0to discern when the store location was the primary issue and ensure closure was the last resort.<\/span>

The retailer adopted an\u00a0AI model<\/strong>\u00a0specifically designed for in-depth location analysis. This data-driven approach offered a clear, objective view of each store\u2019s potential, enabling the real estate director to navigate this difficult task with\u00a0greater confidence and reduced uncertainty<\/strong>. The AI model became an essential part of the executive team, delivering actionable insights and saving considerable time in the decision-making process.<\/span><\/p>\n\n\n\n

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Pain Points<\/span><\/h3>\n\n\n\n

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1.\u00a0Operational and Data Challenges<\/strong>: Poor operations and inaccurate data led to the misidentification of underperforming stores, impacting profitability and operational efficiency.<\/span>

2.\u00a0Risk of Workforce Reduction<\/strong>: Closing a store directly affects employees, so making accurate, well-supported decisions is crucial to minimise unnecessary job losses.<\/span>

3.\u00a0Costly and Impactful Decisions<\/strong>: Store closure requires significant capital and resource allocation, making it essential that closures are only executed when location performance cannot be improved by other means.<\/span><\/p>\n\n\n\n

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The AI model provided the following critical insights and analysis capabilities:<\/span><\/h3>\n\n\n\n
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Location Potential Evaluation<\/span><\/h3>\n\n\n\n
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The AI assessed historical data,\u00a0foot traffic<\/strong>, and\u00a0local demographics<\/strong>, enabling the retailer to determine whether low performance was due to the location itself or other factors. By analysing the store\u2019s potential based on its surroundings and competitor proximity, the AI identified which stores could realistically become profitable with the right adjustments versus those where closure was the best option.<\/span><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Operational Performance Analysis<\/span><\/h2>\n\n\n\n
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The AI model analysed store performance against\u00a0similar locations<\/strong>\u00a0across the portfolio, highlighting disparities that pointed to operational inefficiencies or other issues, like inventory or staffing, rather than location alone. This feature helped differentiate between stores that needed operational improvements and those where closure was necessary.<\/span><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Predictive Data-Driven Decision-Making<\/span><\/h3>\n\n\n\n
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The AI provided\u00a0predictive insights<\/strong>\u00a0into future store performance by examining trends in footfall, customer spending, and nearby developments, allowing the real estate director to make pre-emptive adjustments. By simulating different scenarios, such as changes in staffing, layout, or product offerings, the AI offered insights into possible outcomes and supported informed, data-driven decisions.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Strategic and Efficient Store Closures<\/span><\/h3>\n\n\n\n
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With the\u00a0Store Closure Optimisation Report<\/strong>, the real estate director was able to:<\/span>

1.\u00a0Implement Strategic Closures<\/strong>: Based on predictive data and operational insights, the retailer closed only locations where\u00a0location was the confirmed primary issue<\/strong>, ensuring that closures were a last resort.<\/span>

2.\u00a0Preserve Jobs Where Possible:\u00a0<\/strong>By distinguishing operational challenges from true location-based issues, the retailer reduced the number of closures, preserving jobs at stores with untapped potential.<\/span>

3.\u00a0Improve Overall Store Portfolio:<\/strong>\u00a0Through AI-driven insights, the retailer was able to\u00a0optimise its store portfolio,<\/strong>\u00a0eliminating high-risk locations while bolstering investment in profitable stores, enhancing overall profitability.<\/span><\/p>\n

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