The One Key To Better Targeting On TV
People in the TV ad business are increasingly embracing audience-based targeting. From the addressable solutions that let marketers target specific households, to the handful of networks that have started Open AP with an eye toward helping marketers reach more granular targets, the trend is moving away from standard age/gender demos and index-based buying.
That’s great, right? Everyone wants to be able to reach more of the right customers. But targeting is only as good as the outcomes it produces, so we can’t talk about targeting without also focusing on campaign measurement.
Having the ability to see the relationship between exposure and action enables marketers to easily test different hypotheses and ultimately produce actionable results.
A number of measurement solutions exist in the marketplace. They attempt to account for factors such as seasonality, promotional windows, concurrent campaigns running in different channels, and even world events—but very few actually dig into the TV media itself to discern who was exposed to an ad and what subsequent action they took.
The one key to better targeting, therefore, is understanding the relationship between exposure and conversion as a direct result of seeing a certain ad. Without that information, the insights a brand receives from their measurement company should be treated as directional at best. To put it another way, targeting without granular measurement is like playing a baseball game that gets suspended by rain in the top of the 5th inning. You may have some indication of what the outcome might be, but it’s still considered incomplete.
What are brand marketers to do? Let’s take a look at a few of the options.
Media Mix Modeling: Today, the most common form of measuring a TV campaign is through a media mix model. While marketers should feel confident about the measure of positive or negative ROI as reported by these analyses, they can take many months to generate and offer few if any insights on which aspects of their TV advertising are performing best, which parts need improvement, and what adjustments can be made for optimization.
Time-Based Correlation: The same holds true time-based correlation. While the analytics are returned much more rapidly than those from media mix modeling, time-based correlation can only tell you if there was an uptick in traffic within a certain time frame after seeing your ad. What if you were intending to target moms and it turns out the ad was more effective with dads? Time-based correlation can’t tell you that, and as a result, you’d be missing out key insights that could help you improve your targeting in the future.
Linking TV Ad Viewing To Purchase Data: This is the best option of them all. The solution to proper TV advertising measurement lies in combining viewing data with purchase data. Because to truly gauge the impact of a campaign, marketers must be able to know who saw their ad and what, if anything, they did as a result. With that knowledge, marketers can refine their targets and optimize future campaigns to be more effective.
Brands with an extensive CRM database are in the best position to take advantage of this solution. By partnering with a company like Simulmedia, marketers can match their customer data directly against a TV viewing panel in a privacy-safe manner, and then close the loop by connecting exposure data to their own customer credit card receipts. Having the ability to see the relationship between exposure and action enables marketers to easily test different hypotheses and ultimately produce actionable results.
Because to truly gauge the impact of a campaign, marketers must be able to know who saw their ad and what, if anything, they did as a result. With that knowledge, marketers can refine their targets and optimize future campaigns to be more effective.
Take the example of one of Simulmedia’s e-commerce clients. They had developed several CRM-based audience segments and thought they had a pretty good idea of who their best customers were, but they wanted to test their theory through an actual campaign. Over the course of a few months and multiple campaign flights, Simulmedia tracked the conversion rates of 12 different CRM-based TV audience segments. The client was surprised to learn that the most significant—and unexpected—shared trait among the most responsive segments was higher household incomes relative to the other segments. With this crucial learning, the e-commerce company decided to use household income as one of the key targeting attributes in future targeting efforts.
The key to better targeting is better measurement. That’s not necessarily intuitive, considering that these two campaign steps come at the very beginning and very end, but it’s true. By measuring which audience segments were most responsive to a specific ad, marketers can continually refine their target audiences and optimize ahead of the next campaign.
To see how Simulmedia has helped drive growth for businesses like yours, check out these case studies. If you like what you see, get in touch with us to see how we can help you improve the performance of your TV advertising, too.