So you’re a coach. You have a team, a sweet season. You look back and ask, “How do I know which player contributed what to all this sweetness?” and a vision of a 90-year old economist named Lloyd Shapley appears . . . and you wake up and realize you’re just a digital marketer lost in a fantasy world.
As Freud said, a dream is the fulfillment of a wish. Turns out Lloyd Shapley has an answer for you, too.
Shapley is a game theorist who devised a formula called the Shapley value in the 1950’s. It describes a way to assign credit among a group of players who cooperate for a certain end. It has been used to value individual members of sports teams, polluting manufacturers . . . and now, digital marketing tactics.
Shapley value is the gizmo behind the attribution feature Google just added to its Google Analytics Premium product. This new offering is a big improvement on the already-impressive marketing funnel and rules-based attribution available in Google Analytics (free and premium) and vaults Google closer to the enterprise-class orbit occupied by stand-alone providers such as Visual IQ and Adometry.
Truth is, most of your target customers are probably exposed to different messages on different channels. Yesterday a banner ad and a TV spot, today a search ad, two more banners, an e-mail and a magazine spread. More money, more problems. You’d like to know what’s working and what isn’t — that is, what’s contributing how much to that final sale — and thus: attribution modeling.
Solutions such as Google’s pack in as much data as possible about a particular users’ exposure to your ads — what, when and where — and look at user-level data across vast swarms of humans. It then constructs a best-fit description of the info it has, saying things like, “Display ads contributed 21.2% ($45K) to the campaign’s success.”
But as Google’s resident evangelist Avinash Kaushik recently warned, “There are few things more complicated in analytics . . . than multi-channel attribution modeling.”
So in the words of a musical team from the pre-Google era, don’t believe the hype. At least, not all of it. There are still important things no attribution model can do:
- Include all relevant data — offline media and online media viewed on different devices are generally not in the model (although Google’s Universal Analytics is starting to bridge this gap)
- Measure brand or social impact — attribution works best with e-commerce variables such as online sales; it doesn’t incorporate success metrics important to brand advertisers, such as consideration, or positive impact on social conversation
- Go very deep — the models work best at the channel level (e.g., paid search vs. e-mail) and get less useful, even irrelevant, as you drill down into detail (e.g., sites, placements, sizes, creative versions)
- Tell you why — you may find out e-mail contributed a lot more than you thought, but you’re on your own figuring out why
- Be easy to act on — without knowing why something worked (or didn’t), you may find yourself with more questions to deal with (e.g., “Would a cat have done better in that video banner?”), before you’re comfortable shifting budget around
In the end, an attribution model is a lot like the handicap on your favorite sports team: informative, sure, but no guarantee of future success.
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