Google jumps into behavioral marketing, tailoring ads that consumers receive based on recent surfing behaviors.
About 10 years ago, I was having a conversation with some business colleagues regarding the challenges of targeting specific consumers with specific advertising messages. In the heady days of 1999, the few of us who worked in interactive advertising were seeing the future unfold before us. Not only were we able to create advertising designed for consumers to participate in, but we were also working with a medium that should allow us to better direct the right advertisements to the right consumers.
During that conversation, I brought up a model that I believed would be a win-win for consumers and advertisers. The future scenario went something like this:
My original reasoning for this model was that huge improvements were needed to ensure consumers saw commercials with some relevantly personal meaning to them. Up to that point, mass marketing approaches had little to do with offer relevancy; instead they settled for grabbing the greatest number of eyeballs possible regardless of whom they belonged to. While this approach may work to a certain degree for branding campaigns, it's still a pretty inefficient way to reach the best consumers for an offer.
A consumer-targeted advertising model may seem like a panacea for both consumers and advertisers, but it has a fatal flaw: it's very difficult for consumers to accurately predict where their interests lie for future marketing opportunities. In short, as consumers we often don't know what we want as much as we think we do.
To that point, last month Google rolled out its own brand of targeted advertising, designed to tailor ads that consumers receive based on their recent surfing behaviors.
Using information from its content network, Google expects to offer a higher level of relevancy in matching both text and display ads against consumers' needs and interests. It's important to note that Google won't use data from Google searches as part of the targeting criteria.
Unlike existing keyword-based contextual models that Google uses in an attempt to match ad content against Web page content (periodically yielding some questionable matchups), the new targeting approach starts by tracking topics of search interest made by consumers (tracking is purely anonymous). It then uses that information to sort consumers into different groups based on 30 broad subject categories and 600 subcategories.
With its new approach, Google remains sensitive to consumers' privacy and security concerns. It includes an online tool, the Ads Preferences Manager, that allows consumers more granular control of how they are being targeted by allowing them to customize criteria used to reach them and to view, delete, or add additional categories. And of course, Google offers the option to opt out of ad-targeting cookies.
Google isn't offering an earth-shattering new targeting solution at this point. Keep in mind, it has the data infrastructure and a vast number of partner sites it can use to get up to speed very quickly. And it doesn't hurt that it has the money and resources to make some mistakes along the way.
In some ways, the future I envisioned a decade ago is starting to take shape. In other ways, the realities of how advertisers and consumers interact have made these models a little harder to define. All in all, we've come a long way from the one-size-fits-all advertising models that once dominated our industry.
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Rob Graham is the CCT (chief creative technologist) of Trainingcraft, Inc., where he heads up development of customized training programs for a wide range of digital marketing, entrepreneurial development, and digital media clients.
A 20 year veteran of digital media, Rob has served as the CEO of a multimedia development company; an interactive media strategist; a rich media production specialist; a Web analytics consultant; a corporate trainer and seminar leader; and a chief marketing officer.
When he isn't on the road presenting training workshops, Rob teaches at Harvard University, Emerson College, and the University of Massachusetts - Lowell where he teaches classes on Digital Media Development, Web Store Creation, Software Programming, Business Strategies, and Interactive Marketing Best Practices.
He is the author of "Fishing From a Barrel," a guide to using audience targeting in online advertising, and "Advertising Interactively," which explores the development and uses of rich-media-based advertising. He has been an industry columnist covering interactive marketing, digital media, and audience targeting topics since 1999.
Hong Kong, May 5-6, 2015
Gartner Magic Quadrant for Digital Commerce
This Magic Quadrant examines leading digital commerce platforms that enable organizations to build digital commerce sites. These commerce platforms facilitate purchasing transactions over the Web, and support the creation and continuing development of an online relationship with a consumer.
Paid Search in the Mobile Era
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12:00pm ET/9:00am PT