Audience Buying 101

  |  October 3, 2011   |  Comments

Three factors that will determine success or failure in the audience-buying game.

In a John Hughes movie, audience buying would be that mysterious new kid who transfers into the local high school and has everyone talking. Some think he's going to save the football team. Others say he's done time in juvie. But pretty much everyone has an opinion - even if they're hazy on the total picture.

It's a familiar spot for those of us in the audience buying biz. And while the technologies and strategy are newer, the practice itself needn't be all that mysterious.

On a basic level, audience buying allows brands to reach consumers directly, rather than - as is the case with content buys and contextual targeting alone - by proxy. Let's unpack this statement.

Let's pretend I run the marketing department at a major life insurance company. In a traditional content buy, if I want to market my life insurance product to 40-year-old married men, I place my ads in places where large numbers of them have been proven to congregate. Say the business section of a major newspaper site. With contextual targeting, my ads will pop up when someone is, say, reading an article about life insurance. In both of these circumstances, I'm trying to reach my 40-year-old married target based on what site they're on or what content they're viewing.

What audience buying allows said life insurance company to do is to deliver advertising directly to these 40-year-old married targets, wherever they happen to be and whatever they happen to be doing. They could be watching a clip of Maria Bartiromo, reading an article about A-Rod's performance in last night's Yankees' game, checking movie times via an iPhone or Android app, or performing any number of daily activities online or on a digital device. What site they're on, what app they're using, or what content they're viewing is immaterial - the ad is delivered based upon who they are rather than on what they happen to be doing at any particular moment.

The great thing about the audience-buying model is that it allows brands to maximize the efficiency of their campaigns by mitigating some of the waste that's inherently built into content and contextual buys. After all, not everyone on the newspaper's business page is a married 40-year-old male, just as not everyone reading about life insurance is necessarily a potential customer.

So that's the theory of audience buying. In practice, the application can be widely divergent. Broadly speaking, success or failure in the audience-buying game depends heavily upon three key factors, which we'll examine in turn.

  1. The breadth and quality of the audience profiles' database. Underpinning any audience-buying solution is its database of audience profiles, which should be powered by multiple data sources including both first and third-party sources. More profiles allow for more precise targeting. So instead of targeting 40-year-old married men, we can, for example, target 40-year-old married men who are in-market shoppers for life insurance or have started to do life insurance research on third-party sites. Of course, simply having a large number of audience profiles is only half the equation. To be useful, the profiles must also be rigorously accurate and up-to-date, dynamically updating based on the aggregation of secure, non-personally identifiable audience information across multiple online and offline channels.
  2. The reach of the audience-buying network. Assuming a quality database of audience profiles, the next factor to consider is the reach and scalability of the audience-buying platform. That is, in the universe of places where ads appear - display advertising, mobile ads, video ads, and paid social media - how many (on a global basis) can be served by the solution in question. Without a critical mass of touch points, audience buying loses much of its effectiveness no matter how good the profile's database.
  3. Solid strategy and experienced approach. With points one and two satisfied, the last piece of the puzzle is the human element. Specifically, the expertise of the campaign manager choosing which audience profiles to target and how to best action that targeting. For example, maybe the life insurance ads will be most successful with 40-year-old married men who went to a public university. Or are pragmatists. Or own an SUV. Based on what the advertiser is trying to sell, the campaign manager must be able to identify the most relevant profiles to target and then, when the campaign is running, make adjustments on the fly based upon incoming data.

Brands aren't the only winners in the audience-buying paradigm. Consumers benefit by seeing more ads that are actually relevant to them. And publishers gain a valuable new sales channel, allowing them to better maximize their inventory.

So should you jettison your existing media strategy and throw all your chips on audience buying? Not necessarily. For example, most advertisers running major audience-buying campaigns are still utilizing contextual and content buys within their overall marketing programs. What has changed is the spending balance as these brands shift more and more of their marketing dollars into the audience-buying arena to take advantage of the greater efficiencies and higher returns that audience buying delivers.

For brands, the question becomes finding the particular new equilibrium that allows them to optimize their results.

Finding a balance? Making room for the newcomer? As scholars of the John Hughes' oeuvre know, it's exactly these elements that lead to a happy ending.


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Eugene Becker

Eugene Becker is VP, analytics for Xaxis. Eugene leads the development and deployment of the data management platform and analytics suite on a global basis. He is responsible for creating insight applications that link disparate data and empower Xaxis clients to realize break-through efficiencies.

Prior to joining Xaxis, Eugene held a similar role as the director of analytics for the Media Innovation Group (MIG), a WPP company. He was responsible for creating new applications based on user-level data and managing day-to-day analytics operations and strategy for the MIG.

Earlier, Eugene was senior vice president of analytics at McCann Worldgroup where he drove innovation in digital analytics and built scalable platforms for data visualization and cross-channel optimization. His experience spans strategy consulting, database marketing, market research, and advanced analytics, serving a roster of blue chip clients including Pfizer, Intel, Exxon, Sprint, and Diageo.

Eugene has a B.A. and M.A. in economics from Northwestern University where he specialized in regulatory economics, econometric analysis, and game theory.

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