For nearly 50 years, Cabela’s has been selling its hunting, fishing, and outdoor gear through mail-order catalogues; it now produces more than 100 different catalogue titles and sends out 130 million copies annually. But the company, which is based in Sydney, Nebraska, has been looking to cut down on mailings to unlikely prospects as paper and postage costs rise.
So over the past couple of years, Cabela’s has been relying heavily on predictive analytics software that draws on the mountain of data the company has generated from shoppers at its global website and its 30 stores across North America. Using tools from SAS Institute, a private company, Cabela’s built a model that ranks customers from those with the best buying history to those with the worst. Next, it adds more than 15 predictive variables, including a customer’s preferred product categories and zip code. Each customer is then assigned a score from 1 to 100—the higher the “star rating,” the greater the revenue projection. The score determines whether and when Cabela’s mails that customer each of its catalogues.
The goal is “to determine how much each customer is going to spend with us over the next 12 months,” says Corey Bergstrom, director of market research and analysis for the $2.6 billion company. Some high-value customers get special perks, such as more sophisticated telephone support. “If you deserve the white-glove type treatment,” Bergstrom adds, “we need to know who you are so that we can go the extra mile for you.”
Since introducing the model, Cabela’s has quadrupled the rate of responses to its catalogue. In other words, people who receive them are now four times as likely to buy something. It’s reached a point, says Bergstrom, where “we can we can predict what a customer is going to purchase next.” For instance, someone who purchased duck decoys might be in the market for a shotgun cleaning kit.
Never before have companies been able to gather such volumes of data about their customers. Yet predictive analytics software is a fairly small part of a $1.4 billion global market for business intelligence software, according to the market research firm IDC. That’s probably because implementing the software effectively requires a commitment from everyone from the CEO down to the customer service people, says Bergstrom: “It’s about building a culture of leveraging one of the biggest assets most companies have—data.”
Predictive analytics works by mining the troves of data captured by companies. First, sophisticated computer algorithms or mathematical equations are used to create a statistical model. Plug in variables such as age, gender, and credit report scores, and these algorithms can automatically calculate potentially predictive relationships—patterns that point to the future. Armed with this information, companies can better anticipate everything from inventory gaps and product returns to sales levels and loss of business—prophesies that can boost a company’s bottom line.