In 2004, Michael Lewis took note of how Oakland Athletics Coach Billy Beane adopted an analytical approach to baseball. In his book "Moneyball," Lewis wrote that "People…operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage." Lewis was talking about how the Athletics put together a winning baseball team with one of the lowest budgets in the league, but the lesson, if applied correctly, works equally well for marketing.
Given what a Major League Baseball coach achieved with the help of big data, it's shocking that some marketers have still not yet embraced the concept of granular analysis to capture comprehensive customer marketing interactions in order to make more strategic, cost-effective media-buying decisions. Many in the industry still believe in aggregate data and base decisions on concepts like the advertising outlet that has the biggest audience or the highest click-through rate is the best bang for every company's ad buck. This is a profit-killing fallacy, and when marketers learn to embrace big data and true granular analysis over gut feelings, they'll stop falling for it. Armed with big data and the tools capable of pulling insights and recommendations out of it, marketers can be more strategic with their budgets and therefore generate more revenue.
With recent breakthroughs, we are awash in online and offline data that can deliver a better understanding of what really drives conversions. As consumers increasingly interact with advertisements across channels, devices, and time, marketers who use tools that can track, analyze, and capitalize on these interactions will be more successful and reap greater profits.
The common, expensive misconception about aggregate data
Undoubtedly, many marketers shy away from big data because the term itself is so daunting: big data
. The words alone conjure visions of complexity and cost. Wouldn't it be cheaper and simpler to stick with aggregate data analysis? Simpler, perhaps. Cheaper? No; not if you factor in the massive expense of lost opportunity and costly actions based on incorrect information.
Aggregate data is essentially big data's opposite. Instead of using a platform that tracks and inserts every user interaction (clicks, views, conversions, and other actions) into a database and then mines this data for insights, big data doubters opt for a less computationally intensive approach that simply counts individual elements without putting them in accurate context. In fact, aggregate data provides little in terms of tactical optimization.
This occurred to me recently as I examined the television performance of a client using our platform. The company's media plan crossed a range of cable broadcast stations from the smallest to the largest. The largest stations provided the most revenue, but at the highest cost. The smallest stations couldn't deliver the volume of the larger ones, but they often produced the most profitable investments. Despite that distinction, the conversions delivered by these smaller stations numbered only in the single digits. An aggregate data approach would capture the sale, but not its source. And the marketer would only know that in aggregate, television either works or it doesn't. The marketer would lose any insight related to whether larger or smaller stations were more effective.