Cabela's Meets Cross-Channel Analytics Challenge
Using analytics to make predictions has always been a tricky business for retailers: all too often, some unknown factor or chance occurrence turns a “can’t-miss” promotion into an offer that customers find they can, indeed, miss. But if there has always been a wild card or two hidden in the deck, today there seems to be one in every hand that’s dealt.
What’s made analytics so much tougher today? Many retailers already know the answer: the radical changes in customers’ shopping patterns. “Research is finding that the path to purchase is increasingly non-linear, and that buyers are turning to many more sources of information, even in product categories once thought to be of low importance to consumers,” writes Jeff Tanner Jr., Ph.D., associate dean of the Hankamer School of Business at Baylor University and a partner in BPT Partners LLC, in Illuminating the Path to Purchase. “Today’s economy has increased the risk associated with even the simplest purchases, raising the importance of online search, referrals, online reviews, and other sources of pre-purchase information.”
Shoppers’ zig-zag purchase paths and multiple influencers have left many retailers’ ability to accurately track customer behavior far behind. Retailers have long sought a single, integrated view of the customer, but they are really feeling the lack of it now. “That same customer that abandoned a cart online then went in and purchased in the store or dialed the call center,” writes Tanner. “But the marketing manager without access to data from all channels doesn’t know that and then makes a multitude of mistakes, perhaps even e-mailing a special discount on the product that was already purchased through another channel.”
Integrating Both Channels and Touchpoints
Outdoor and sporting goods retailer Cabela’s takes these analytics issues seriously, starting with an understanding of the importance of not just gathering data but using it. “One of the decisions you have to make is that you’re going to make decisions with data,” says Tanner. The retailer has also translated its beliefs into action, creating an integrated view of customer activity by implementing the Integrated Web Intelligence (IWI) application from Teradata.
Along with WebTrends to collect online data, the IWI application integrates data from Cabela’s 30-plus retail outlets, catalogs, its call center, in-store kiosks and the online store.
But wait; there’s more. In addition to transactional data, Tanner notes that improving customer interactions requires Web browsing data, traditional market research data and data from third-party sources. Cabela’s, with customers that include hunting and fishing enthusiasts, uses a combination of loyalty program and its own bank card data for acquiring customer purchase information, along with third-party data from state wildlife agencies and magazine subscription lists.
This multi-source approach to data management also enables more integrated workflow management. For example, a store manager wondering if he has enough staff scheduled for the coming weekend could look at website activity. “He would know that certain online activity levels presage ‘X’ number of visits to the store, allowing him to forecast the appropriate in-store sales associate level,” says Tanner.
It Never Hurts to Ask
Retailers that want to improve their analytics capabilities can also make more effective use of progressive profiling, which builds from the “notion that we’re in a dialogue with customers,” says Tanner.Today’s technology can support the concept of an ongoing conversation, with each transaction a datapoint within that conversation, allowing retailers to “do more modeling of individual customers.”
He gave the example of the My Coke Rewards program, which asked members who logged in three or four questions, then followed up with different questions the next time the member visited the site. “They were looking at your interests, the offers you chose, and even asking why you said ‘no’ when you said it,” he says.
“Retailers need to get more creative about gathering the kind of data that allows them to flesh out their picture of the customer,” Tanner adds. “This isn’t just about demographics and psychographics. If a supermarket customer who never buys liquor suddenly buys six bottles of an upscale wine, but no meat, that person might be throwing a party and having it catered – but the supermarket didn’t know that and missed out on that opportunity. With a fuller understanding of the customer, they might have been able to create an offer, bundle it together and turn that wine sale into something bigger.”
Retailers always need to be thinking in terms of building a more complete picture of the customer without being lulled into what Tanner terms a data trap. “Retailers have a lot of data, all these snapshots of the customer, and they think they know who that customer is,” he says. “But if you take a million photos of an elephant’s butt, you still don’t know what the front looks like.”
What’s made analytics so much tougher today? Many retailers already know the answer: the radical changes in customers’ shopping patterns. “Research is finding that the path to purchase is increasingly non-linear, and that buyers are turning to many more sources of information, even in product categories once thought to be of low importance to consumers,” writes Jeff Tanner Jr., Ph.D., associate dean of the Hankamer School of Business at Baylor University and a partner in BPT Partners LLC, in Illuminating the Path to Purchase. “Today’s economy has increased the risk associated with even the simplest purchases, raising the importance of online search, referrals, online reviews, and other sources of pre-purchase information.”Shoppers’ zig-zag purchase paths and multiple influencers have left many retailers’ ability to accurately track customer behavior far behind. Retailers have long sought a single, integrated view of the customer, but they are really feeling the lack of it now. “That same customer that abandoned a cart online then went in and purchased in the store or dialed the call center,” writes Tanner. “But the marketing manager without access to data from all channels doesn’t know that and then makes a multitude of mistakes, perhaps even e-mailing a special discount on the product that was already purchased through another channel.”
Integrating Both Channels and Touchpoints
Outdoor and sporting goods retailer Cabela’s takes these analytics issues seriously, starting with an understanding of the importance of not just gathering data but using it. “One of the decisions you have to make is that you’re going to make decisions with data,” says Tanner. The retailer has also translated its beliefs into action, creating an integrated view of customer activity by implementing the Integrated Web Intelligence (IWI) application from Teradata.
Along with WebTrends to collect online data, the IWI application integrates data from Cabela’s 30-plus retail outlets, catalogs, its call center, in-store kiosks and the online store.
But wait; there’s more. In addition to transactional data, Tanner notes that improving customer interactions requires Web browsing data, traditional market research data and data from third-party sources. Cabela’s, with customers that include hunting and fishing enthusiasts, uses a combination of loyalty program and its own bank card data for acquiring customer purchase information, along with third-party data from state wildlife agencies and magazine subscription lists.
This multi-source approach to data management also enables more integrated workflow management. For example, a store manager wondering if he has enough staff scheduled for the coming weekend could look at website activity. “He would know that certain online activity levels presage ‘X’ number of visits to the store, allowing him to forecast the appropriate in-store sales associate level,” says Tanner.
It Never Hurts to Ask
Retailers that want to improve their analytics capabilities can also make more effective use of progressive profiling, which builds from the “notion that we’re in a dialogue with customers,” says Tanner.Today’s technology can support the concept of an ongoing conversation, with each transaction a datapoint within that conversation, allowing retailers to “do more modeling of individual customers.”
He gave the example of the My Coke Rewards program, which asked members who logged in three or four questions, then followed up with different questions the next time the member visited the site. “They were looking at your interests, the offers you chose, and even asking why you said ‘no’ when you said it,” he says.
“Retailers need to get more creative about gathering the kind of data that allows them to flesh out their picture of the customer,” Tanner adds. “This isn’t just about demographics and psychographics. If a supermarket customer who never buys liquor suddenly buys six bottles of an upscale wine, but no meat, that person might be throwing a party and having it catered – but the supermarket didn’t know that and missed out on that opportunity. With a fuller understanding of the customer, they might have been able to create an offer, bundle it together and turn that wine sale into something bigger.”
Retailers always need to be thinking in terms of building a more complete picture of the customer without being lulled into what Tanner terms a data trap. “Retailers have a lot of data, all these snapshots of the customer, and they think they know who that customer is,” he says. “But if you take a million photos of an elephant’s butt, you still don’t know what the front looks like.”