Big data, tracking, affirmation, reassurance – whatever label we give it, the science of analytics has become the driving force behind big business. Especially in marketing and advertising, where we can track visitation, media consumption, engagement, etc., and a bunch of acronyms (CPC, CTR, ROI) to determine campaign success or failure, optimize for future growth, and even direct creative, amongst many other things. And while all this big data is certainly useful as a variable in making advertising and marketing decisions, we’re nearing a dangerous precipice in which industries (that include ours) see big data as foolproof.
Consider our beloved, hometown Cleveland Browns. From 1999 through 2015, the Browns went an abysmal 87-185. Desperate for better (or who are we kidding, even average) results, the team turned to Mr. Moneyball himself, Paul DePodesta, whose analytics-based approach brought success to Major League Baseball’s Oakland A’s. Now this may seem obvious, but baseball is not football. From righty/lefty matchups, BA, WHIP, OBP, OPS, ERA, there is myriad data available over 162 games, which makes baseball very conducive to an analytics-based approach. Unlike the NFL, which plays only 16 games packed with in-game factors that demand immediate response versus a calculated data analysis. For example, data can help you set the ideal base defense, but it can’t calculate Aaron Rodgers’s otherworldly ability to throw a perfect pass across his body, on the run, for 60 yards.
The point being, numbers don’t win games: players do. In trusting the analytics approach, the Browns have cut higher paid veterans and signed less experienced, cheaper players. And while their roster matches the Moneyball criteria, it lacks the on-field intangibles that analytics just can’t predict like leadership, experience, unique skill, etc. The result? A 1-19 record over the new regime’s first 20 games.
Or consider Chipotle. You could argue that big data told the groundbreaking fast casual Mexican chain that, in order to optimize future growth, it needed the one thing every other Mexican chain had that Chipotle didn’t: queso. But what big data couldn’t tell you is that: A) Chipotle is known for its all-natural food sourcing; B) Traditional queso, as we all know and love it, is made with unnatural, processed cheese; and C) All-natural queso is, well…not very good queso. Which leaves Chipotle in the unenviable position of satisfying big data demands while utterly failing big taste buds.
So what do we make of all this? Big data is not the enemy here. There’s no need to stop monthly reporting, analyzing, and the like. The lesson here is in application. Because behind all the cold, factual reams of numbers, there are still, get this: people. Irrational, unpredictable people who, according to neuroscience, will more often make decisions emotionally rather than logically.
This explains why, as part of your brand’s messaging strategy, you can’t just run up to someone and shout the logical reasons for buying your product at them. It (somewhat) explains why the Browns are historically awful. It may even explain why Chipotle’s new queso is so un-tasty.
More than anything, it explains why people who understand other people—who can look beyond the data to see how it is derived and apply those learnings to real-world success—are crucial to your brand.
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– Your PSA for Analytics friends at Brokaw.