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I'm a partner in the advanced analytics group at Bain & Company, the global management consulting firm. My primary focus is on marketing analytics (bio). I've been writing here (views my own) about marketing, technology, e-business, and analytics since 2003 (blog name explained).

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July 24, 2014

The Revolution Will Not Be #Mobile -ized

Via the WSJ, Emarketer's latest forecast has mobile ad spending eclipsing newspapers and radio (individually, not together, yet):

Ad spending on smartphones and tablets will this year eclipse radio, magazines, and newspapers, according to research firm eMarketer. Spending on mobile, which includes smartphones and tablets, will jump 83% in 2014 to nearly $18 billion, eMarketer predicts. Newspapers and radio, on the other hand, will draw nearly $17 and $15.5 billion respectively.

And yet, you will not be able to get full value from your investment, because the Revolution will not be Mobilized... until we can track and properly attribute the impact of advertising in this channel.

By various estimates, mobile now accounts for 30% of web traffic, but only 10% of ad revenues.  Some, like Mary Meeker, make the case that this means mobile advertising spending still has lots of room to go.  Here's my critique of her argument a couple of months ago. In that post I described demand- and supply-side factors that inhibit the "equivalency thesis" she outlines.  One of these is understanding the properly attributed influence of mobile in a customer experience.

A couple of weeks ago I listened to Visual IQ's Solutions Consulting lead Andy Dubickas crisply describe three approaches for tracking a user across the desktop-mobile divide, for purposes of attribution analysis:

  1. if your user logs in to one of your digital properties through both your mobile interface and your desktop interface, then you can use the common user ID associated with these log ins to associate the different devices through whatever device tracking technique you use (cookie-ing browsers, or watching for returning hardware- or software-level device IDs, for example). Here's a terrific article by digital analytics guru Justin Cutroni ( @justincutroni ) on how this works.
  2. if you use two devices, but they seem to travel suspiciously together, like logging in from the same place at the same time, and looking at the same thing, then it's possible to guess with some confidence that these two devices both have you behind them. Here's a good article by Tom Manvydas at Experian that explains this a bit further. Experian has a service called AdTruth that provides this capability.
  3. you may be able to get device-to-user match data from other services who do this themselves, if you have a relationship with them and PII issues are addressed. For example, Twitter also collects this information; Mother Jones covered this well late last year.

If you're not ready for this, you can at minimum set up your mobile ad buy with some variation in it -- waves, spikes, whatever -- that allow you to see, either with your naked eye or through modeling, the incremental direct and indirect effects of your investment.  In fact, there's no reason the tighter-tracked approaches described above can't peacefully co-exist with this simple approach, even if it's just a cross-check.

(Apologies to Gil Scott-Heron)

 

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