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What the NFL Draft Can Teach Us about Digital Marketing Strategy

in Media Planning & Buying, Mobile Marketing with tags , , , Both comments and trackbacks are closed.

Every year around this time, talent evaluators, pundits and regular fans create mock NFL drafts , placing the best college football players with the team they think could most benefit from each player’s skills. When draft day hits, everyone turns out to be wrong, but the mock drafts are a lot of fun and there is no harm done.

The same cannot be said for the people who are actually responsible for the real draft – General Managers, scouts, coaches – because if they bet wrong, their jobs are at stake. So to help ensure they make the right picks, they look at every little detail about a player they are interested in.

This might sound familiar to marketers, who also look at reams of data to make big advertising investments in the players (e.g., search, display, paid social, etc.) that they predict will achieve the most ROI.

The NFL Draft and digital marketing have another thing in common – both systems are susceptible to a fundamental – and very human – flaw: we think we know more than we do.

With all of the details a team can acquire about a player they are interested in – footage of their every play, intelligence tests, coach referrals, try-outs, and stats and data – NFL decision makers believe that they know with a great deal of certainty which players will perform best. In many cases, this means that teams trade their picks to get in a better position to acquire a player they think is valuable before another team scoops them up.

According to Vox, the decision makers aren’t as accurate in judging talent as they would believe. Vox points to work done by Cade Massey and Richard Thaler showing that there is only a 52 percent likelihood the first player picked at a given position will be better than the second player. They advocate a strategy of trading down, not up, so you get more picks that cost less (as salaries go down as you go lower in the draft).  In other words, it’s better to diversify, because if you peg your success on one or two players, it can be a disaster for you team.

So why do teams still go after the number one draft pick? This is known as the overconfidence effect, which Vox explains, “as people are given more information, the accuracy of their analysis hits a ceiling, but their confidence in it continues to increase.”

The overconfidence effect also applies to marketing. We have sophisticated attribution models, we can look at cross-device behavior, and we are using mixed media modeling to ensure that marketing dollars are spent in the most efficient ways possible. Still, no matter how big our Big Data is, we are still in the dark about rich territories of our business where we haven’t invested in building sophisticated data and metrics.  Mobile search, for example. We all know that people are using their phones to find stores, check prices and make purchases, but the industry is in the dark about how to assign ROI to these behaviors.

It is the digital marketing overconfidence effect. Despite evidence of consumer behavior and the benefit of diversifying investments and experimenting with new formats, marketers are underinvesting in mobile.  This is because they are overconfident in the data that measures desktop, paid search, affiliate marketing and e-mail.

Is it possible that it is more risky to invest in just those programs that can be measured and less risky to diversify? It is an uncomfortable thought, because it feels very reassuring when a model tells you what to do. But the world, especially the digital world, often moves faster than the models and in unpredictable ways.

To twist an old joke we’ve been telling for years, the models are telling us that this is FINALLY the year of mobile. The problem is that it was actually last year. Marketers just missed it, because it couldn’t be measured  the way they – and their CFOs – wanted to.

In many ways, we are in the future business, and the future is notoriously hard to predict, even if you’re an expert. Your best bet is to make more small bets—using data to help guide you, of course—and the ones that don’t work out can be forgotten, like the last year’s mock draft.

Cover photo via Vox