Why Signal Planning Is the Most Compelling Media Planning Innovation in Decades

Social and programmatic buying fueled its rise

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There are still a lot of brands that define their consumers based on age, gender, income and education, which is how audiences have been measured since Arthur Nielsen started measuring radio listenership in the ’40s and TV viewership in the ’50s. Back then diaries were sent to U.S. homes and respondents were asked to write in what they watched and listened to. Up until recently diaries were still a big part of measurement.

Partly because advertisers needed standardization, and partly due to limited computer processing and technological innovation, most brands defined their consumers by broad definitions, like adults 25-54, household income $75k+ with a high school education. The problems with that kind of definition have been examined in marketing research journals ad nauseam. It’s time to put that approach to rest and welcome the age of signals.

Barry Lowenthal
Illustration: Alex Fine

The age of signals began with the rise of programmatic buying, which was the first time behavioral data was able to inform media buying decisions at scale in real time. Social accelerated the use of signals and enabled custom audiences and even more precise targeting. Social also accelerated the purchase funnel and moved the purchase journey to mobile. Now shopping and first-party data has made signal planning the most compelling media planning innovation to come along in decades.

A lot of media planning theory has been hypothesized and even tested (some might say successfully) over the years. Approaches like pulsing (for ongoing promotions), bursts (for launches), recency theory (for ads that appear immediately before purchasing decisions), and pillars and planks (to describe promotions supported by always on media schedules) have been used to describe how people buy products and when marketers should flight their media.

All of those descriptions and approaches made sense in the absence of real data. But now we have that data, so we need to move on. We need to start using signals to schedule media, not predications based on surveys and panels. Instead, we should be using real signals, activated by consumers, to identify where they are on their individual journey to purchase (and no, we no longer use a funnel to describe that journey).

When we think about prioritizing consumer opportunities we always start with current customers. They’re the people that promote the brand, and they need the least amount of convincing (i.e., the least amount of investment) that a brand’s products are great choices. These people give off lots of signals of what they’re thinking about and whether they want to buy more. They tell us by logging in to the brand’s website or searching for the brand by name. They also talk about the brand on social media, and they’ve installed the brand’s app on their smartphones.

All these signals tell us they’re interested and what they’re interested in specifically. We think these are strong signals and worth paying a lot to capture and read. It doesn’t matter their age, income or where they went to school. In fact, no brand should really care. All they should care about is that they’re ready to buy again.

There are also people who are not our customers but they’re shopping the category and looking at our brands. These people have gone to our website. They’ve searched for our products on Amazon. They did research about our products on Google. These signals are also strong and valuable. And again, all I care about is that they’ve been doing research on my brands. It doesn’t really matter how old they are.

The next ring out from the center (i.e., the current client’s bull’s-eye) are people that are shopping the category, but they haven’t decided which brand to buy. These people are also important because we can persuade them to consider our brand. And these people also give off their own signals. We can learn from smart TV data that they’ve been watching a lot of home improvement shows, or we can use location data to tell us they’ve been visiting a lot of bridal stores.

Location data, in particular, provides valuable signals, especially when merged with credit card data. Data from Pinterest can tell us they’ve been collecting a lot of kitchen pins. All these signals indicate interest and clearly place people near the start of their shopping journey.

If brands have lots of money, they may consider talking to consumers when they anticipate they’ll be entering a category, even before their signals indicate shopping behaviors. Various life stage signals like giving birth could indicate future interest in a 529 college saving account, or getting married could indicate future home construction projects.

Today we can buy these signals and identify exactly where consumers fall on their purchase journey. We can reach them as they’re demonstrating intent. None of these signals would be available to us in real time using survey-based information. Instead, we’re getting much more valuable information; we’re learning they’re in-market or about to be in-market for our products.

Welcome to a more precise age, one with little waste and where signals remove static.

Barry Lowenthal (@barrylowenthal) is president of The Media Kitchen.

This story first appeared in the Sept. 18, 2017, issue of Adweek magazine. Click here to subscribe.