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Soccer Mom killed by audience, data leakage
I will miss her…her smile, her interests, her taste in products…her spending habits…
When I started my game company, we targeted “Soccer Moms” and tried to find them with all our marketing. This general definition of our consumer helped us decide where to promote our product and which publishers to target with our advertising. At AND 1, we were trying to reach the core influencers within a larger, more general basketball consumer audience. In order to do this we identified specific other interests, like music, cars and video games that when combined with an interest in basketball indicated a high likelihood of being receptive to AND 1 marketing messages.
Today, the soccer mom and her friends 9–5 Joe and the Baby Boomer are dead. My only regret is they worked for the publishers. With these definitions no longer useful what happens to content creators? Is there an advertising technology that will speak for the publishers?
The online advertising world is moving from a focus on publishers and their average audience to targeting specific users as members of distinct and often unique segments across publishers. In short, it will be hard for publishers to get paid for the differentiated value they create in the form of unique data. As the ad industry moves toward audience management, we already hear a lot about data attribution and the revenue consequences of “data leakage.”
I think we are a long way from achieving accurate, cross-publisher and cross channel attribution for a few reasons:
Audiences must be defined and aggregated for advertiser reach i.e. commoditized
The data responsible for the marginal lift in response must be identified and agreed to by both advertiser and publisher
The marginal value of this lift must be attributed to a specific publisher raising questions of cookie vs. context etc.
Measurement must be established to prove out cross channel impact of unique publisher data
The buy side bottleneck today is finding the audience, at scale and the current solution for advertisers is scale in their publisher network and management of the data across the sources. The goal is a multi-dimensional view of the customer on an impression by impression basis. In this world, how you aggregate the data and define “audience” creates value, not the data itself. Publisher data has to be normalized to make it consistent and predictable for advertisers, but this also makes it commodity and potentially undifferentiated across publishers.
There should be an opportunity to factor in the publisher data– proprietary data that lifts inventory value — and to create a rating system for the inventory that avoids the commodity pricing issue. Maybe something multi-factored like a diamond — cut, clarity, color, size — to let people optimize for what they care about — currently optimization only occurs on price and the system is broken for the supply side.
Thanks to Adam Bain for the diamond analogy and am excited to discuss further in the comments.