Retailers have years — possibly decades — of experience with consumer purchase data. The best have learned to harness that resource to target offers to their best customers. And yet, many are missing out on another huge opportunity: mining that data for insights into optimizing store layout for a better bottom line.
In the grocery channel, retailers traditionally rely on demographic and sales information when trying to improve movement out of a particular category or department. When changes yield less-than-satisfactory results, they may be unaware that the data used was simply too coarse to provide meaningful insights about their high-value purchasers.
Best practices focus instead on the analysis of customer-specific basket data to reveal behavior by priority shoppers.
To illustrate the point, LoyaltyOne cites a recent engagement with a grocery chain. The retailer was looking to boost low mid-week sales in late afternoons and early evenings. Studying customer-specific transaction data revealed a set of items bought by a specific segment that represented an opportunity for growth: high-margin prepared meal components and healthy products.
Using this key insight, the retailer targeted certain suburban stores that over-indexed on this segment and created a convenient one-stop shopping area near the front offering fresh-made meal solutions.
The retailer also made operational changes and additions to its marketing and POS based on the basket data insights. The end result: double-digit ROI, far exceeding the retailer's expectations.
Use the form below to download your copy of the article: Using Basket Data to Optimize Store Layout. You may also use this form to ask questions or provide feedback on this Business Tip.