Organizations that love data-driven decision-making suffer from the same challenge. How do we earn trust from consumers to get the right data about them, all in an effort to serve them better? This classic problem of “if I knew more, I could do more” is most easily solvable by the following axiom: “With the correct incentives, customers will give you their data.”
Customers “sell” their data all the time in the form of loyalty and rewards programs. I will tell you my preferred brands, and you and your partner will get the same revenue and purchase history. For example, if I trade my credit card points for cash, the organization knows that I might have some immediate needs that money can help solve. Likewise, if I exchange all of my points for baby formula, you might predict that my wife is expecting.
Case Study
Most institutions understand that transactions show preferences. For example, purchasing a 32oz box of raisins out of an impulse might show a preference or an “affinity” for raisins. However, rewards or loyalty indicate a much deeper relationship. While a Chicago Bears credit card likely means I will not be buying an Aaron Rogers jersey, an Ann Taylor Loft card might indicate I like stylish but machine-washable clothes. This idea extends to every piece of customer loyalty. Especially now, data acquired from loyalty programs show brands relevant ways to engage with customers.
Further, loyalty and revenue data are predictive. Nothing will change my love for the Chicago Bears, and as such, brands would do well if they can predict I will act like other Bears fans. This lookalike modeling and preference mapping are one of the keys to providing relevant in-context experiences. This is extremely important in financial services. And in the case of payment networks, the payment type, coupled with the particulars of the loyalty arrangement, is a particularly compelling sales pitch.
For example, imagine an airport with a hamburger restaurant. If my competitor has a branded card for that establishment, but I have rewards data to show that there is a largely untapped market for a card of mine (based on customer rewards related to “hamburger eaters” in that geography or in proximity to those hamburger places), I may be able to tell a compelling story and get the hamburger restaurant to shift providers. In other words, loyalty data does not just enhance the customer experience of the card owner; it enhances an organization’s ability to upsell and cross-sell to all involved parties. This “multi-purpose data” approach to insight is the key to a relevant and optimized rewards program.
