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Leveraging APT Test & Learn to Make the Most of Your Store Closings
Technology Category
- Analytics & Modeling - Data Mining
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
- System Integration
The Challenge
When retailers close stores, it is uncertain whether the foregone in-store sales will be captured in the online channel, or if the decreased brick and mortar presence will actually drive declines in online sales for the affected markets. This particular retailer had closed several stores within its network and wanted to understand the impact this had on online sales in markets with store closures.
About The Customer
The customer is a large retailer with several hundred stores. This retailer operates in a highly competitive market and has been facing the challenge of optimizing its store network to maximize profitability. The company has a significant online presence, but the relationship between in-store and online sales is complex and not fully understood. The retailer aims to retain as many customers as possible and minimize lost sales when closing physical store locations. By leveraging advanced analytics and data-driven decision-making, the retailer seeks to strategically manage store closures and enhance customer retention.
The Solution
The client first used Test & Learn software to gain a better understanding of both online and offline transaction size, prior to any store closings. Through the software’s rapid analysis of transactional data, the retailer determined that on average, online shoppers had smaller baskets than in-store shoppers. Next, the retailer used Test & Learn to analyze a natural experiment, examining select store closures and their impact on online sales in the affected market. The retailer was able to quantify online sales retention in the period after a store closure, yielding substantial changes to the economics of future closures. APT software segmented these results to reveal how retention varied by product category and by various customer attributes. Analysis indicated that customers who were exposed to fewer ads previously and whose closest store location was further away from a competitor were more likely to be retained. Further, categories containing items that customers did not need to experience for themselves in the store had the highest online sales retention rates.
Operational Impact
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