Family Dollar, Advance Auto Parts Drive Repeat Purchases
Value shoppers have become a fixture in the retail world. As these cost-conscious shoppers search for bargains, product promotions have become a key driver for growing sales. Yet promotional strategies carry significant risk. Marketing organizations run into obstacles discerning which portfolio of products to promote or markdown in order to win shopper loyalty. It is common to encounter differing opinions within leading retailers as to the proper balance between discounting targeted products, with the hope of driving larger baskets, and protecting margins, at the risk of potentially losing customers.
Similarly, merchants are constantly reviewing product assortments to identify SKUs that can be pulled off the shelf and replaced with products with higher turns without losing product-loyal shoppers. Analyzing data to inform such decisions has traditionally been cumbersome and slow. Historically, skilled merchants default to instinct and experience to try to make rationalization decisions.
Recently, advances in data storage have enabled retailers to collect granular details surrounding individual transactions, though retailers require a robust analytical toolkit to drive insight from this data. When done correctly, market basket analysis can uncover how promoting a given product increases sales and profit coming from other products by driving larger baskets or a greater number of transactions. Without this insight, merchants could promote the "wrong" item and miss an opportunity to increase sales and profits.
Conducting market basket analysis is rarely straightforward. Understanding which promoted items drive sales can be difficult for retailers with large basket sizes, including multiple products receiving promotion. Determining whether the pull-through effects of a given product promotion will drive profit requires more than coupon redemption or a simple count of the number of baskets including a promoted item. Instead, it requires a nuanced measurement of the exact number of incremental transactions driven by a given promotion -- transactions that wouldn't have occurred but for the promotion.
Increasingly, retailers are using market basket analysis not only to determine which products to promote, but also to identify products with low margin and low attachment rates that can be removed from the shelf. By testing rationalization decisions in a small number of stores and accurately measuring transaction impact against control stores, retailers are able to make more informed decisions about which products can be pulled from which locations without losing customers with specific product loyalties.
Leading retailers including Family Dollar, Wawa, and Advance Auto Parts are using test and learn software to drive insights on attachment rates and repeat customer purchase behavior provided by market basket analysis to create innovative promotional offers and optimize product assortment. New promotional strategies, from varying price points to rotating products featured in weekly circulars, to merchandising strategies such as adjusting adjacencies or building product bundles, are then tested, to understand which levers best influence behavior. By measuring changes in attachment patterns after the introduction of a program, relative to a matched set of control stores or customers, retailers can easily understand which programs truly drive incremental sales and profits.
Anthony Bruce is CEO of Applied Predictive Technologies (APT), a management adviser on marketing and growth strategy for retailers, consumer-products companies, and financial institutions. To learn more about APT, visit: www.predictivetechnologies.com.
Similarly, merchants are constantly reviewing product assortments to identify SKUs that can be pulled off the shelf and replaced with products with higher turns without losing product-loyal shoppers. Analyzing data to inform such decisions has traditionally been cumbersome and slow. Historically, skilled merchants default to instinct and experience to try to make rationalization decisions.
Recently, advances in data storage have enabled retailers to collect granular details surrounding individual transactions, though retailers require a robust analytical toolkit to drive insight from this data. When done correctly, market basket analysis can uncover how promoting a given product increases sales and profit coming from other products by driving larger baskets or a greater number of transactions. Without this insight, merchants could promote the "wrong" item and miss an opportunity to increase sales and profits.
Conducting market basket analysis is rarely straightforward. Understanding which promoted items drive sales can be difficult for retailers with large basket sizes, including multiple products receiving promotion. Determining whether the pull-through effects of a given product promotion will drive profit requires more than coupon redemption or a simple count of the number of baskets including a promoted item. Instead, it requires a nuanced measurement of the exact number of incremental transactions driven by a given promotion -- transactions that wouldn't have occurred but for the promotion.
Increasingly, retailers are using market basket analysis not only to determine which products to promote, but also to identify products with low margin and low attachment rates that can be removed from the shelf. By testing rationalization decisions in a small number of stores and accurately measuring transaction impact against control stores, retailers are able to make more informed decisions about which products can be pulled from which locations without losing customers with specific product loyalties.
Leading retailers including Family Dollar, Wawa, and Advance Auto Parts are using test and learn software to drive insights on attachment rates and repeat customer purchase behavior provided by market basket analysis to create innovative promotional offers and optimize product assortment. New promotional strategies, from varying price points to rotating products featured in weekly circulars, to merchandising strategies such as adjusting adjacencies or building product bundles, are then tested, to understand which levers best influence behavior. By measuring changes in attachment patterns after the introduction of a program, relative to a matched set of control stores or customers, retailers can easily understand which programs truly drive incremental sales and profits.
Anthony Bruce is CEO of Applied Predictive Technologies (APT), a management adviser on marketing and growth strategy for retailers, consumer-products companies, and financial institutions. To learn more about APT, visit: www.predictivetechnologies.com.