Measuring The Immeasurable: How To Attribute TV Behavior

Measuring The Immeasurable: How To Attribute TV Behavior
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Veteran direct marketers believed that non-DR TV couldn’t be measured. But that was before advances in digital measurement and data analytics. Thanks to these technologies, behavior can now modeled and predicted in online and offline channels.

How does a company pull that off? We asked Alison Latimer Lohse, the co-founder and COO of ConversionLogic, a three-year-old firm specializing in analytics-based attribution.

How do companies measure TV?
We believe in measuring 100% of the media exposures — every single spot that ran at the time it ran, and correlating events: QSR, online ordering, downloading coupons, within minutes, to understand the relationship between stimulation and response. Often, you can use a panel, from 25,000 to a million people to measure their deterministic behavior, but it’s highly biased, and you miss some of the subtlties of how the media work together. You have to see how ads impact build and decay over time.

Do TV ads with Web site URLs qualify as DR?
Consumer are so savvy, they don’t need a URL anymore. If an auto spot comes on, they’re watching the spot and researching Chevy Tahoe through a search engine or going to the Web site on their mobile phone. It’s symbiotic behavior that isn’t so isolated anymore.

Why are some marketers missing this?
Marketers get stuck in historical metrics, meaning you built goals off of information you had been accumulating over the last 10 or 20 years. It’s hard to undo historical ways of measuring. When Web sites first came out, it was the language of the hit — how many hits did I get? — then SEM: how many clicks? But marketers are moving away from that single-channel language to more holistic measurement. Some companies move faster than others; others have a harder time breaking the muscle memory of those practices. But there’s a big opportunity here.

What’s that?
It has nothing to do with measurement — it has to do with getting more focused on data hygiene and data management. Whether you’re measuring TV or radio or digital channels, you have to have a good clean data house.

Brand is important, but so is data, and I believe that will pay dividends long-term.

Does model-based attribution facilitate media buying?
Absolutely. Companies are more intelligent. They’re not looking for insights for insight’s sake, but for activation of media. With model-based attribution, they end up having an attributed CPA, meaning an attributed conversion for every spot based on its effectiveness at driving a business outcome. They can use the attributed CPA to modify the day part, station and program mix, and decide how to switch their programming for the week or the next quarter.

Who’s doing it well?
There are a lot of evolved firms in the ecommerce and subscription-service space, everyone from companies like Uber to prepared-food delivery services to subscriptions like Birchbox. They quickly tap out in digital channels to find new customers, so they have to turn to television. And they’re not rooted in the assumptions and legacy language of how TV works. They want to know the return of every dollar they put in, independently and with other channels.

Can you apply this science to offline channels like billboards?
We’re focused on the time of exposure, so we concentrate on TV and radio. But we’re doing some experimental work tying billboards to mobile exposure. The question is: Can you use mobile data to suggest when someone was exposed to a billboard?

What about addressable TV?
We’re working with partners to connect user IDs and map TV exposure in the user path to see the deterministic exposure of TV spots. There’s some maturity in the technologies that allow that. But we need more critical mass.

What excites you about 2017?
It’s that data is coming in from unexpected places. An obvious example is that we can now determine how mobile or location data enhances media-exposure data. A less obvious one is on the research side. We’re starting to see more addressable data, not 25,000 from a biased survey, but panels of 5-million to 10-million people, where there are actual respondents we can incorporate into the models. So we can take brand metrics and model them. There’s some real potential.

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