Remember that this is not the first time a data revolution has changed the game for marketers, and previous transitions were not always smooth. In the mid-1980s, the introduction of barcode scanning enabled companies to gather information at the checkout register. Before that, data was limited; companies knew what they shipped, and they could ask people what they bought. But with the advent of scanners, they could actually see what was happening at the point of sale. In the early years of the technology, this new awareness led to a number of missteps. Executives became overly focused on the impact of price promotions on sales and lost sight of marketing fundamentals: brand equity and brand building. Over time, however, companies developed more sophisticated statistical models and refocused their energy, and scanners became the biggest boon to consumer marketing and retail in the last 30 years. (Today, this point-of-sale knowledge has been extended to include loyalty card data, which provides retailers with insight into what individual households put in their shopping baskets, and into online shopping behavior.)
Like the flawed marketing ROI models of the barcode’s early years, the newest big data analyses can be misleading. Many retailers say, “I know everything about what moves off my shelves. I know a lot about my customers who have loyalty cards. But when we put more things on the shelves that resemble the things they’re already buying, we don’t see the growth that we were expecting.”
What’s missing? It’s likely that by focusing on the newest source of data, the retailer has unintentionally developed a one-dimensional view of its customers. What it needs is the broadest possible view of a customer’s path to purchase. We sometimes call this perspective “a day in the life.” It means understanding more completely how your interaction with a customer fits in with all the other interactions he or she has with other retailers or (as the case may be) other businesses, shopping channels, or activities. Without that insight into what is prompting a customer to go somewhere other than to you, your growth initiatives can become a crapshoot.
Learning to Walk
The first steps you take to acquire, harmonize, and mine new data sources almost always lead to exciting new insights. As you gather these insights, it will be important to be open to new approaches and to challenge sacred cows. You may learn things about your customers that cause you to question certain products, services, or strategies. It can be a lot to take on. Rather than recommending that companies go whole hog with analytics all at once, I often advise them to undertake a few pilot projects. It will benefit them to learn to walk before trying to run: They can pick a product, a geography, and a problem that they want to focus on, and demonstrate to themselves that the return on effort and cost justifies the new undertaking.
For example, a global energy giant decided to tackle the question of quantifying and improving its return on marketing investment using more advanced analytics. Senior leaders selected two business units within three countries, spanning developed and developing markets, to conduct pilot projects. The conceptual framework and goals for each project were the same, but operating gas stations in Europe and selling motor oil in Asia required different data sets and analytics tools. This diversity enabled the corporation to gain experience with a wider set of possible approaches, and to determine which ones should be employed in which situations. In addition, they shared their success stories with other business units and countries to build enthusiasm for the initiative. The result was a sophisticated yet pragmatic program that was eventually rolled out, accepted, and used around the world.