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
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.
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. More intriguing examples include crowd-sourced data that can give customers answers to important questions such as “what can I learn from other consumers?” or “how do I compare with other consumers?”
A look at startups that create new forms of value using big data is instructive. Opower allows customers to share their utility bills with Facebook friends to determine how they rank in relation to other customers like them. INRIX, aggregates traffic data from customers’ mobile phones and other sources to provide real-time traffic reports. Zillow combines information from an array of sources to provide consolidated insight about home attributes and values, competitive properties, and other market characteristics to buyers, sellers, and brokers. These companies are big-data natives. Their success should be a wake-up call to all businesses: Today, there is no business that is not an information business.
Every company should ask three questions to examine how its big data can create customer value:
What types of information will help my customers reduce their costs or risks? Multi-billion dollar businesses such as Yelp, Zagat, TripAdvisor, Uber, eBay, Netflix, and Amazon crunch quantities of data including ratings of service providers and sellers in order to reduce customers’ risk. Currently, these good-bad-ugly ratings provide generic evaluations of sellers on standard scales. But increasingly customers are looking for more specific answers to questions such as what do customers like me think of this product or service. Answering such granular questions requires a much deeper understanding of what customers are looking for, and how they see themselves. That is an opportunity for the next generation of big data value creation.
What type of information is currently widely dispersed, but would yield new insight if aggregated? Is there any incidentally produced data (such as keystrokes, or location data) that could be valuable when assembled? InVenture, a fascinating new startup operating in Africa, is turning incidental data on smartphones into credit ratings that allow base-of-the-pyramid customers access to loans and other financial products. In an environment where most of the population has no credit history, and therefore no credit rating, even rudimentary phone usage data serves as a handy proxy (people who organize their contacts with both first and last names are more likely to repay loans).
Is there diversity and variance among my customers such that they will benefit from aggregating others’ data with theirs? For example, a company selling farm inputs (seeds, fertilizer and pesticides) can collect data from farmers with dispersed plots of land to determine which combinations of inputs are optimal under different conditions. Aggregating data from many farms operating under diverse soil, climatic, and environmental conditions can yield much better information about the optimal inputs for each individual farm than any single farmer could obtain from his own farm alone, regardless of how long he had been farming that parcel.
Big data has helped marketers address fundamental questions whose answers have long been out of reach. But the true contribution of big data will reside in creating new forms of value for customers. Only this will allow marketers to turn data into sustainable competitive advantage.