Yesterday's Ad Age carried a 3-minute clip from IAB CEO Randall Rothenberg's speech on audience measurement.
Rothenberg argued for looking past the glum economy to what he called a "crisis of complexity", with research at the root. Citing an October McKinsey survey of 350 marketers which found 80% allocating media by guesswork and off last year's numbers, he suggested that research professionals are making audience measurement too complicated for marketers. Then he went on to suggest that the solution lies in "business process standards and measurement".
I wasn't there to hear the rest of Rothenberg's comments, so this observation may miss further context. But I see where he's coming from. He runs a trade association whose job it is to promote interactive / digital media as an advertising option. One way this happens is by standardizing measurement
so the medium is easier to buy. Today, advertisers can measure traffic more or less well enough, and care more about measuring engagement (as a means of getting foks to click on the holy "buy" button of course). Notwithstanding efforts to solve this (like Eric Peterson's -- see this white paper
and this more recent post
), this is hard because engagement can mean many things and user interactions (or lack thereof) can correlate imperfectly with each of these meanings. Hence complexity, frustration, and his call for standards.
While I agree with Rothenburg's diagnosis, I disagree with the priority for his prescription. Yes, standards are important, but they take a long time to agree on and the processes that generate them can bog down if the issues at hand get too far ahead of their economic relevance. For my part, I think if we're to realize the full potential of interactive media and advertising for more effective and efficient marketing, there are two more immediate imperatives.
First, no reference to measurements and data about engagement should be made without first starting with the specific user behavior to be promoted and the possible options for doing so. The data and analyses are only useful in that context, and it's only in that context that we can judge if we're being too simple, too complex, or "just right" in how we're trying to answer questions. So let's break engagement down -- do we mean more reading by users, users giving us information in exchange for suggestions, registration for more personalization, content contributions, user recommendations (forward to a friend, for example, or otherwise)? If we do this, marketers can "shop" more easily for the kind of media they believe drives the kind of engagement that they need to convert at a higher rate.
Second, marketers and researchers need to meet in the middle in terms of education
. Researchers need to better understand the specific engagement objectives and solutions their work addresses -- magnitudes, implications, investment requirements, feasibility, necessary analytic precision; marketers need to get smarter about the guts of how interactive media and advertising actually work, and the implications of those mechanics for the data they use and the actions they might take. To this end, I'm in the middle of reading Avinash Kaushik
's excellent book, Web Analytics: An Hour A Day
. I'll follow with a more detailed summary / review, but I strongly recommend the book for marketers despite its somewhat narrow title -- in addition to a great exposition of the nitty gritty of how things work, the book offers lots of practical advice about how to keep analytics manageable, in perspective, and focused on actions.
IAB has a role in driving both of these imperatives forward, and it already does.
But we've reached the point in digital marketing where we need to move beyond whether
(as argued here
), to how
. For IAB and others, rather than orienting publications and sessions around specific media or measurement per se
, these might be organized around business issues -- "driving awareness, engagement, conversion" and only presenting data and analysis in the context of how it supported business decisions related to these issues. Limitations and complexities in data and analysis should be balanced with references to whether they matter and to practical workarounds or cross-checks when they do. The ideal session or publication would present integrated stories of problems, options, analysis, and results, and through this help us keep complexity in appropriate perspective. And, standards-setting efforts will proceed faster and generate better outcomes with a better-informed "electorate" of marketers.