Big data holds out big promises for marketing. Notably, it pledges to answer two of the most vexing questions that have stymied marketers since they started selling: 1) who buys what when and at what price? and 2) can we link what consumers hear, read, and view to what they buy and consume?
Answering these makes marketing more efficient by improving targeting and by identifying and eliminating the famed half of the marketing budget that is wasted. To address these questions, marketers have trained their big-data telescopes at a single point: predicting each customer’s next transaction. In pursuit of this prize marketers strive to paint an ever more detailed portrait of each consumer, memorizing her media preferences, scrutinizing her shopping habits, and cataloging her interests, aspirations and desires. The result is a detailed, high-resolution close-up of each customer that reveals her next move.
But in the rush to uncover and target the next transaction, many industries are quickly coming up against a disquieting reality: Winning the next transaction eventually yields only short term tactical advantage, and it overlooks one big and inevitable outcome. When every competitor becomes equally good at predicting each customer’s next purchase, marketers will inevitably compete away their profits from that marginal transaction. This unwinnable short-term arms race ultimately leads to an equalization of competitors in the medium to long term. There is no sustainable competitive advantage in chasing the next buy.
This is not to say firms should never try to predict and capture the next purchase – but that they can only expect above-average returns from this activity in industries where competitors are lagging and where there are still some rewards to being ahead of the game. In many industries, including travel, insurance, telecoms, music, and even automobiles, we are rapidly closing in on equalization of predictive capabilities across competitors, so there is little lasting competitive advantage to be gained from predicting the next purchase.
To build lasting advantage, marketing programs that leverage big data need to turn to more strategic questions about longer term customer stickiness, loyalty, and relationships. The questions that need to be asked of big data are not just what will trigger the next purchase, but what will get this customer to remain loyal; not just what price the customer is willing pay for the next transaction, but what will be the customer’s life-time value; and not just what will get customers to switch in from a competitor, but what will prevent them from switching out when a competitor offers a better price.
The answers to these more strategic questions reside in using big data differently. Rather than only asking how we can use data to better target customers, we need to ask how big data creates value for customers. That is, we need to shift from asking what big data can do for us, to what it can do for customers.
Big data can help design information to augment products and services, and create entirely new ones. Simple examples include recommendation engines that create value for customers by reducing their search and evaluation costs, as Amazon and Netflix do; or augmenting commodity utilities with customized usage information, as Opower does.
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