Why Household Level Purchase Behavior is Important for TV Campaigns

Guest Author: Matt Frattaroli, VP of Digital Platform and Agency Partnerships, Alliant

With spend projected to surpass $84B across traditional linear, Addressable linear TV, and Connected TV (CTV) in 2022 (Winterberry Group, annual outlook report), advertisers are beginning to align budgets with the accelerated shifts in viewer behaviors we’ve seen in recent years. In fact, Addressable TV and CTV are expected to see the largest increases, at 27% and 32% respectively, compared to only a 3.5% increase for linear tv. This is largely due to the “video everywhere” trend which also includes double-digit growth projections in digital video spend.

Beyond the draw of increased viewership, dollars are flowing into TV because of newfound abilities to go beyond demographic segmentation and leverage the power of audience targeting that has been available for digital campaigns. For marketers accustomed to building data-driven digital campaigns, there are some important differences (read: benefits) that are introduced when moving to the biggest screen in people’s homes. Understanding the power of purchase data specifically will help capitalize on these differences, but first, let’s start with a quick discussion on household-level targeting.

Digital channels are praised in media because of the ability to personalize messaging on a one-to-one level. Marketers are attracted to this precision, and it can be natural to question the efficacy of household-level TV audiences when compared to individual targeting using identifiers like Mobile Ad IDs (MAIDs).

The assumption many marketers make is that all desktop and mobile targeting is at the individual level and that precision gets lost when applying targeting to TV – that is not the case. While most digital targeting is based on an identifier like a cookie or MAID, the data used to match that device is often not at the individual level. And even in cases where data is at the individual level, fuzzy matching logic may have been used to get there, introducing the potential for inaccurate targeting.

TV audience data on the other hand, such as Alliant’s matched audience segments available through Cadent Aperture Viewer Graph, represents household data that has been deterministically synced to a household. So, while not at an individual level, identity has been deterministically managed during both the audience development and onboarding steps, providing more transparency and accuracy than attempting to match consumers at a one-to-one level with cookies or MAIDs.  

With many other obscured data practices in the supply chain, we encourage data strategists and buyers to ask about data collection levels, followed by an assessment on how it relates to the activation level and their use cases. You want to ensure that the granularity of the source data always matches the activation level.

Now, let’s revisit the tendency to question why it can be beneficial to target at the household level, even cookies or MAIDs are available. In a digital environment, many brands or campaigns would benefit from messaging at the household rather than individual level. Verticals such as travel, CPG, entertainment, retail, and auto can all drive business outcomes from the collective household. This is especially true when you consider that TV is often a shared viewing experience.

Demographic data has long served as the backbone of TV campaigns, but platforms like Aperture are opening new opportunities for advertisers to be smarter with their approach to targeting. And while there are many data sets that can drive successful outcomes for TV advertisers, we believe purchase data is one of the most powerful audience solutions for any vertical or category. For example, other types of data like social or location data can provide behavioral insights but may signal more aspirational traits – someone who engages with a variety of tech brands online is not necessarily primed to buy from one of those brands. Purchase data on the other hand shows that someone has demonstrated they have bought from a tech brand and may be willing to buy again.

Ultimately, there are countless ways that purchase data can be used to build more effective TV advertising campaigns. To help you get started, here are 3 quick ideas on how purchase data can reshape your TV advertising strategies:

Change the way you think about TV campaigns:

The biggest screen remains a powerful brand awareness tool, but with the addition of purchase-based audience data, advertisers can create more cohesive performance-driven campaigns. Using optimized data at the start of a campaign will minimize wasted impressions by finding those most likely to buy, and then allow for the measurement of those impressions against both upper-funnel and lower-funnel KPIs.

Influence creative strategies:

With precision in mind, you can start to think about how different creative messaging can be used throughout your TV advertising campaign. A better understanding of brand preference across the household can lead to different copy or product recommendations, driving better consumer response.

Introduce predictive models:

Deterministic purchase data is an impressive tool on its own but combined with predictive modeling advertisers can extend reach and identify other highly qualified prospective customers. An added benefit is that these models will factor in a multitude of purchase behaviors and other characteristics, removing presumptions about customers’ interests or purchase intent.

Interested in learning more? The Alliant and Cadent teams are available to provide recommendations to help you implement successful tests and learn data strategies.