At the end, the museum signed a year-long SaaS commitment with DigiWorks. So how did DigiWorks do it?
The secret, McNabb says, is to take your data and use it to build an authentic one-to-one relationship with your customers. You need to know your customer, build loyalty by listening to your customer and then deliver the right offer to the right person at the right time.
"We need to help retailers get to precision: 20 percent more precision translates into 80 percent more revenue," McNabb says. "Your customer told you they bought something? Talk to them about the second transaction. They bought something for the living room? How can we get them to buy something else for the living room?"
Know Your Customer Using Transactional Data
The key to getting to know your customer is locked in your existing transactional data. You have a name, address and specific information about what your customer wants or needs to buy. With a single transaction, you can see what your customer spent, but also what they spent it on. That information can help you determine what the customer's next likely purchase might be.
"All your customers' interactions with your business — whether through your website or in person, on the phone or through social media — provide you with data to learn more about them," she says. "What tools are they using to learn about your business? What is an individual customer's preference for how he or she makes a purchase: mobile device or tablet, in the store or through the website?"
For the Norman Rockwell Museum, DigiWorks took the transactional data of all purchases and then used weighting patterns and data rules to set a high, medium and low price of product recommendations. It then used A/B testing to discover patterns in how people clicked on offerings within an email — which content and images attracted them.
By doing so, DigiWorks built up data on segments of the museum's overall consumer base so it could reliably recommend unique products to a first-time purchaser built on the patterns of many first-time purchasers. For anyone with an existing transaction history, it provided unique recommendations based on that history as well as the patterns it had identified.
"Picture that you have a portion of total consumers that literally every single one of them was getting a one-to-one recommendation that was unique from the others," McNabb says. "For anybody that hadn't purchased before, they were put back into an A/B test. The bottom line is that when you look at those one-to-one recommendations and then walk through the results of the campaign — we were able to look back across several years — the amount of growth was 150 percent higher than the previous year."
Sign up for CIO Asia eNewsletters.