When they purchase a membership in Sam’s Club’s eValues program, customers receive customized, targeted offers from the retailer. These “smart” discounts are more appealing than generic promotional campaigns, because they pertain specifically to items the customer already has expressed interest in through past purchases.
How are retailers designing such personalized appeals? In the case of Sam’s Club, it relies on sophisticated data mining techniques to collect information from every transaction. This information not only enables it to target customers better but also supports complex evaluations of the best products to carry and which to eliminate.
In turn, Sam’s Club develops predictive analytics that estimate what any particular customer will buy in the future. As a simple example, a customer who buys newborn baby diapers might receive a coupon for wipes, to encourage immediate purchase, and then later receive a coupon for the next diaper size, to encourage future purchase. Each customer’s offers get loaded onto his or her member card, so there is no need to remember to bring discount coupons on the shopping trip.
Sam’s Club designates its offers into three categories and ensures that customers receive promotions in each: rewards, incentives for products the customer normally purchases, and cross-category offers for items the customer has never bought. The response rates for the targeted items are impressive. Whereas mass marketing usually offers yield a 1–2 percent response rate and segmented offers increase that rate to 5–6 percent, Sam’s Club has achieved a 20–30 percent response rate with its predictive analytics.
With the power of analytics, Sam’s Club thus is inciting tremendous loyalty among customers.
Discussion Question: List some of the benefits of data mining and analytics for retailers, beyond coupon targeting.
Susan Reda, “The Personal Touch,” Stores, January 2011.