For the past several years ad-tech defined the value of identity at the individual level; made possible by the evolution of data, technology and machine-learning. But, earlier this year COVID-19 set in motion many shifts in consumer digital behavior. The more we’ve been working and learning from home, using devices that are shared amongst an entire household, the more apparent it is that marketers need to shift their strategies to align with these changes.
Did you know the average household owns eleven or more connected devices? And the longer we’ve been at home, the more these devices are shared by multiple individuals. If you’re looking for a few simple ways to evolve from an individual focused strategy to a household strategy, here’s a good place to start:
Audience segmentation
Traditionally, audiences are built with a narrow focus on a single user, and what known attributes about that individual or their brand engagement can be leveraged for a targeting strategy. Now that screens are being shared between multiple users in a home, how can you be sure you’re identifying them correctly, and thus, segmenting them in the right buckets for targeting? The key lies in the ability to connect those points through identity resolution. Using ad exposure from household level devices, followed by a second engagement from an individual within that household can indicate a user is a better candidate for purchase or conversion than others. So before you build audiences for targeting, you can qualify them at the household level for segmentation with more confidence.
Example: An auto advertiser uses audience segments from a third party provider such as ‘auto intenders’ to target individuals with new pricing offers. They would continue retargeting these users, unaware that some are connected in the same household, and thus are probably not all in the market to actually get a new car. By bucketing users that share a common household device within this third party segment, they can hone in on which individuals are actually in-market for a car and evolve their strategy to be more effective.
Targeting
Retargeting, frequency capping and sequential messaging have always been meant for an individual user — the more they’re exposed to your brand in a personalized way, the more likely they are to take the desired action. But, have you considered that multiple users could have a shared initial exposure to your brand? Today, you can target a household of potential consumers on a shared device like a CTV, and employ those retargeting strategies based on that common initial exposure. Starting at the household level, means you can compare movement through the funnel between different individuals in that household, and tailor your targeting accordingly. Perhaps you realize only one person in that household will convert and you tailor messaging to them more frequently, while confidently suppressing the other individuals.
Example: a CPG brand uses OTT advertising, but doesn’t incorporate it within their sequential strategy, because they consider it just a ‘brand awareness’ opportunity. By using OTT more strategically as a household level engagement, it can reveal which individuals within a household are more favorable towards a brand further down the funnel. So, you can spend impressions targeting those users, rather than wasting impressions on multiple individuals within the household.
Measurement
Measurement and attribution are imperative to understanding the path to purchase and making strategies more efficient over time. Often that efficiency involves adding or removing devices and channels from a targeting strategy based on their contribution to an action or conversion by an individual. This year we’re seeing addressable TV devices explode in use, which are shared at the household level. Even desktop computers are being used by more people in the home due to COVID-19. So, assuming a linear path of attribution by an individual is missing the full picture. Identity resolution can help you understand where messaging was more effective for some users in the household than others, and leverage that insight to continue more effective strategies in the future.
Example: Without a household view, a direct-to-consumer brand would assume all interactions from one device would be coming from a single individual, and that could create a higher cost-per analysis. By incorporating the household level devices into attribution models, they can find efficiencies between touch points of multiple users, and learn how those split off into individual paths to conversion. Not only can this DTC create a more effective model, but they can use that model to create cost efficiencies in the future.
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