Data Mining Maximizes Warehouse Club Profits - dummies

Data Mining Maximizes Warehouse Club Profits

By Meta S. Brown

Perhaps you have shopped at one of the warehouse clubs, retail chain stores that offer members-only shopping in large, no-frills stores. Warehouse clubs have bare concrete floors, plain functional shelving, and limited choices of products and package sizes. Their check-out lanes don’t offer bags, let alone baggers, to pack up your purchases.

Warehouse clubs set themselves apart from typical retailers by opening their doors only to shoppers who are willing to pay annual membership fees. Why create this barrier to entry? Some point out that the membership creates a bond between the shopper and the store, a motivation to return and maximize the value returned for the membership fee. And then, you have the data.

Because warehouse club shoppers must present membership cards to make a purchase, these retailers know exactly who buys what. They can track every transaction in full detail. They know the identity of the shopper, because prospective members must provide proof of identity. They know what the shopper buys. They know the time and location of each purchase. They know the prices the shopper paid and whether any special promotions were involved.

So, warehouse clubs have more accurate and complete information about their shoppers than any other physical stores. In fact, they may have better information than their online competitors.

Rich resources of consumer shopping data, as well as identity and demographic data, enable warehouse stores to mine their data and provide exceptionally high-quality information to support decision making. Mining shopper data can reveal

  • Characteristics of high-spending shoppers: How often and when they shop, which products they purchase, and other demographic details.

  • Product affinities: Groups of products frequently purchased together.

  • Relationships among different offerings: Do people who come in for gas stick around to buy groceries? Do they spend more or less than others? Do they buy similar or different products? What about those who purchase gas, eyeglasses, or prescription drugs? Which transaction comes first, and does that say anything about subsequent purchasing patterns?

  • Geographic details: Where do the shoppers live? How far do they travel to shop? How do product preferences and behavior patterns vary from region to region?

Good data-collection and data-mining practices provide warehouse stores with accurate and detailed information about shopper behavior, which they can use to make informed decisions about which products to offer in each store, what prices to charge, and other matters.

They can also combine shopper data with other business data to learn about productivity, process improvement, and product quality. (Benefits extend beyond data mining when the data is used to inform customers about product recalls, or to simplify returns and other customer service matters. Certain data — like , for example, aggregate data about purchasers’ demographics associated with specific product categories — can even be sold to create an additional revenue stream.)

What does this mean to a warehouse club financially? The Costco warehouse club chain now has more than 70 million members and reported revenues of over $100 billion for the 2013 fiscal year.

Nobody claims data mining is the only reason for that (Costco publicly emphasizes the importance of good hiring, treating employees well, and training and promoting from within), yet data mining enables Costco to build on those fundamentals based on detailed information about customer behavior and preferences, at a local and even individual level.