Data-driven marketing takes gambling out of the marketing process. And that’s a problem.
Sophisticated analytical capabilities are transforming marketing along with most other organizational functions—and they absolutely should. However, complete devotion to data-driven marketing (DDM) can result in some “bad beats” for marketers.
This poker term describes a situation in which a player with a better chance of winning, according to the game’s mathematical odds, loses a hand (and a tall stack of chips) to an opponent with a lesser hand. I absolutely love getting smug emails from one of my data-driven poker buddies the morning after I subject him to what he views as a bad beat in our weekly neighborhood game. “Just so you know,” these emails, usually from my friend Tony, read, “your odds of completing that straight against me on the river were less than 20%.” Translation: I should have won that hand. And if you knew how to run the odds (i.e., play poker the right way), you never would have been in that hand.
An accurate translation actually would be peppered with some more colorful language. Tony’s right, of course, but he’s only partially right.
Odds are one important aspect of poker. But the odds don’t determine everything. Players lie, their attention wanes, and they overthink and make bad decisions despite the odds.
The trick to winning poker isn’t just understanding the data. It’s also understanding when it’s profitable to pay less attention to the numbers and more attention to behavioral clues and information that can’t be plugged into a powerful decision-making algorithm (not yet, anyway; but that may soon change). In articles, thought leadership, and conference discussions about marketing, this balance is often framed as a gut versus logic (or Mad Men versus Moneyball) decision. But this framing strikes me as only partially correct.
It’s more helpful to look at DDM in terms of the types of decisions it can strengthen. Phil Rosenzweig, author of the forthcoming book Right Stuff: How Leaders Make Winning Decisions, stresses that all decisions aren’t created equal. And marketers would be wise to consider this framing of decision-making.
The behavior and decision-making ah-has of the past decade (e.g., signal versus noise, thinking fast versus thinking slow, gorillas in the room, etc.) are extremely valuable. But these perspectives and approaches don’t make all decisions better. Data makes many organizational and marketing decision-making processes better, but not all of them. For example, when making strategic decisions, such as whether to acquire a competitor, it’ less important for a leader to recognize how recency bias might affect her thinking and more important for her to firmly grasp the company’s risk appetite and the degree to which she can rally the workforce to buy into the acquisition.
While I hear a lot of conversations about marketing becoming more data-driven, I rarely hear marketers discuss what kinds of decisions are most likely to benefit from advanced analytics and which decisions depend on a broader range of considerations.
As SAS Global Customer Intelligence Director Wilson Raj said in “4 Data Issues Marketers Should Not Ignore,” “The reality is that, obviously, no business will improve just because you have data or Big Data. You have to use it. That’s where the magic comes in.” And a big part of that use consists of figuring out how to integrate data insights into all of the other marketing approaches, processes, and tricks up your sleeve.
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