Programmatic creative is about understanding both the ecosystem in which you are bidding in, as well as the optimal creative message to deliver once you’ve won the bid. And the data you have available needs to be leveraged at every step along the way.
Programmatic Targeting vs. Creative Targeting
First, let’s start with programmatic targeting. If you are bidding on media in a real-time auction, you’re using various inputs to help determine whether to buy the temporary rights to a piece of real estate on a particular Web page. This is the higher order decision, where you tell the software which ad impressions you want and which you don’t want.
Next comes creative targeting. Once an advertiser is awarded rights to that piece of real estate, a call is made to their ad server asking, “What should I put here?” And at this stage, the ad server also uses various inputs to determine what kind of assets it should deliver and show to the user.
Use Your Data At Both Checkpoints
So now we know there are two decision points: Whether to buy an impression, and which ad to put there once you do. The key here is figuring out how to use the right inputs, in both places, to make the best decision.
There are two types of data in particular that are worth calling attention to, in order to illustrate how they can be used across both programmatic and creative strategies: ad-engagement data and contextual data.
Ad-engagement data gives marketers
the ability to segment users based on past clicks, viewability, video completion, etc. For example, if your programmatic strategy involves frequency capping, you could sync a segment between
your ad server to your demand-side platform based on past viewability, enabling you to frequency cap based on viewable opportunities rather than just delivered impressions. On the creative side,
you can use information about viewability to inform sequential messaging, so that the user progresses through your story as intended, rather than having the effect of skipping certain pieces because
they were not viewable.
Similar logic can be deployed for other engagement data: Marketers can buy a low-funnel audience-based past interaction with their ads, or they can engage in a branding and prospecting campaign and then simply show different messages to people based on whether they had already engaged with an ad in the past.
Contextual data offers another way for programmatic and creative targeting to work hand in hand. Consider a fitness apparel retailer, for example. They might want to bid on all impressions in a fitness context, because it means those users are already in the right mindset to receive a related message. But once the bid is won and the ad server must choose which creative to serve, it might be appropriate to show one product to people reading about yoga, and another to those reading about running.
Of course, there are plenty of other data sets that can and should be used at both decision points in order to drive personalization and maximize relevance. And the more ways you slice and segment your data, the more targeted you can get with each creative message.
It’s time for marketers to bury any confusion they may have about the differences between programmatic targeting and creative targeting, because conflating the two means missed opportunities to maximize relevance and performance. Programmatic and creative must work together, and marketers need to start pushing partners for the data that can unite their bidding ecosystem with an equally impactful and targeted creative strategy.