"On TV And Video" is a column exploring opportunities and challenges in advanced TV and video.
Today’s column is written by Joan FitzGerald, founder at Data ImpacX.
Major media companies are taking new steps to include sales response performance metrics in their commitments to advertisers. But, TV ad sellers: beware. Most research on sales response understates the impact of TV advertising on sales because it ignores critical information about consumers: whether they can be persuaded by advertising or not.
Without understanding which consumers are “persuadable” by advertising, marketers aren’t targeting their media investments where there is the highest payoff. Digital advertising has long used retargeting to reach consumers based on their response to advertising. In television, targeting persuadable consumers is equally crucial.
Without understanding persuadables, TV ad sellers aren’t getting credit for reaching all possible purchasers and, worse, sales response metrics are diluted by consumers who will never respond to advertising – and shouldn’t have been part of the advertising target in the first place.
Understanding whether consumers can be persuaded is important. During the first “moment of truth” in the retail store aisle – to use a framework by Procter & Gamble – non-persuadables will make their choice mainly or solely based on the lowest price. The hurdle to create brand equity with these consumers using advertising is quite steep.
In much of the sales response research done today, consumers are separated into target vs. non-target groups using demographics such as age and gender, instead of being classified as persuadable vs. non-persuadable. Unfortunately, research has also shown that demographics can be a poor predictor of sales response to advertising. In 2008, Harvey Assael, David Poltrack, Bart Flaherty and Bill Harvey found that only 16% of purchasing could be predicted using age and gender variables.
To understand persuadables, the first step is to use big-data resources such as television set-top box viewing data and smart-TV viewing data combined with more traditional consumer panel data. These data must be deterministically matched with purchase behavior to create a single-source data set where ad exposure and purchase behavior are captured from the same consumers. This way, each purchase event and each ad exposure for each consumer is a data point for the analysis.
Marketers can use regression statistics – the most commonly used statistics for market-mix modeling – on each consumer in the data set. In effect, it’s possible to create a coefficient of response for every consumer. The coefficient of response tells whether each consumer is responsive to advertising, and how responsive.
Finally, it’s a measure of persuadability. You can see the advantage: Each consumer is measured to determine whether they are persuadable by advertising or not. Advertising investments can be targeted to consumers where they will have the greatest impact. Sales response to advertising will be more precise and accurate, giving TV ad sellers the credit they deserve as brands try to move the sales needle with consumers.
The first paper identifying persuadables using single-source data was published in 2008. It’s been almost 10 years since then, but finally identifying persuadables is possible at scale to understand sales response to advertising in a more meaningful, actionable way.
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