First published April 4, 2013 in Mediapost’s Search Insider
It’s somewhat ironic that, as a die-hard Darwinist, I find myself in the position of defending strategy against the onslaught of Big Data. Since my initial column on this subject a few months ago, I’ve been diving deeper into this topic.
Here’s the irony.
Embracing Big Data is essentially embracing a Darwinist approach to marketing. It resists taking a top-down approach (aka strategy) by using data feedback to enforce evolution of your marketing program. It makes marketing “antifragile,” in the words of Nassim Nicholas Taleb. In theory, it uses disorder, mistakes and unexpected events to continually improve marketing.
Embracing strategy — at least my suggested Bayesian approach to strategy — would be akin to embracing intelligent design. It defines what an expected outcome should be, then starts defining paths to get there. But it does this in the full realization that those paths will continually shift and change. In fact, it sets up the framework to enable this strategic fluidity. It still uses “Big Data,” but puts it in the context of “Big Testing” (courtesy Scott Brinker).
To remove the strategy from the equation, as some suggest, would be to leave your marketing subject to random chance. Undoubtedly, given perfect feedback and the ability to quickly adapt using that feedback, marketing could improve continually. After all, we evolved in just such an environment and we’re pretty complex organisms. But it’s hard to argue that a designer would have designed such flaws as our pharynx, which is used both for eating and breathing, leading to a drastically higher risk of choking; our spinal column, which tends to become misaligned in a significant portion of the population; or the fact that our retinas are “inside out.”
Big Data also requires separating “signal” from “noise” in the data. But without a strategic framework, what is the signal and what is the noise? Which of the datum do you pay attention to, and which do you ignore?
Here’s an even bigger question. What constitutes success and failure in your marketing program? Who sets these criteria? In nature, it’s pretty simple. Success is defined by genetic propagation. But it’s not so clear-cut in marketing. Success needs to align to some commonly understood objectives, and these objectives should be enshrined in — you guessed it, your strategy.
I believe that if “intelligent designers” are available, why not use them? And I would hope that most marketing executives should fit the bill. As long as strategy includes a rigorous testing methodology and honest feedback does not fall victim to egotistical opinions and “yes speak” (which is a huge caveat, and a topic too big to tackle here), a program infused with strategy should outperform one left to chance.
But what about Taleb’s “Black Swans”? He argues that by providing “top down” direction, leading to interventionism, you tend to make systems fragile. In trying to smooth out the ups and downs of the environment, you build in limitations and inflexibility. You lose the ability to deal with a Black Swan, that unexpected occurrence that falls outside of your predictive horizon.
It’s a valid point. I believe that Black Swans have to be expected, but should not dictate your strategy. By their very nature, they may never happen. And if they do, they will be infrequent. If your strategy meets a Black Swan head on, a Bayesian approach should come with the humility to realize that the rules have changed, necessitating a corresponding change in strategy. But it would be a mistake to abandon strategy completely based on a “what-if.”