Identifying and targeting the right audiences across multiple devices while still respecting the privacy of consumers is one of the prevalent challenges for digital advertisers. Privacy regulations changed the way cookies are used in advertising, and the traditional cookie approach is phased out, even more as users migrate to connected TV (CTV) and over-the-top (OTP)platforms.
Using a household graph helps solve this challenge. This innovation enables smarter targeting, cross-device attribution, and more effective campaign measurement. Read on to understand what a household graph is, how it works, when to use it, and its benefits.
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A household graph is a data structure that links all of the devices associated with a single household and assigns them a unified, anonymized identifier. Devices might include smart TVs, mobile phones, tablets, laptops, OTT devices, and other connected screens.
The household graph uses the identifier to find, target, and measure an audience, both at the individual and household level. Thus, they can address audience fragmentation and retarget all the users who live under one roof.
For example, rather than treating devices as separate endpoints, household graphing assumes that living room CTV, a partner’s tablet, and a parent’s smartphone all likely belong to the same family unit.
Everybody these days uses multiple devices, from phones to desktops and laptops, to streaming TVs, making it hard to connect interactions to the right people or households.
When you don’t link users at the household level, you are exposed to several risks:
Companies that want to advertise on OTT and CTV channels, and OTT and CTV companies, are the primary users of this technology. Any advertiser that wants to do cross-device ad retargeting for a group of users who live together is a good fit for the household graph.
The base of the household graph is the data stitching technology. The process begins with data aggregation, where information is collected from multiple sources, including first-party CRM data, third-party datasets, and real-time device signals. These inputs are then used for device mapping, which connects multiple, seemingly unrelated identifiers, such as connected TV (CTV) ID, a mobile advertising ID (MAID), or desktop cookies, back to a single physical home.
To ensure the stitching is accurate, certain devices play an “anchor role”, for example, smart TVs and streaming devices such as Roku or Apple TV.
Once the household is mapped, the graph is applied to targeting and frequency control. Advertisers can deliver consistent, sequential messaging across screens, ensuring that a household experiences a cohesive narrative. For example, someone may see a car ad on their living room TV, and later receive a complementary insurance message on their phone.
Additionally, the household graph helps reduce ad fatigue by controlling frequency across all devices. This approach is valuable in industries where typically decisions are taken by a couple or family, like automotive, finance, and travel.
The final outcome is a stronger attribution and measurement. One of the biggest challenges in CTV advertising is that ads can’t be clicked. A household graph addresses this gap by linking ad exposure on a television to subsequent actions on other devices, such as a website visit or purchase made on a mobile phone or laptop.
Finally, household graphs support online conversion measurement by connecting digital ad exposure to real-world behavior. For instance, a mobile device associated with a household can be detected entering a dealership or retail location through geo-fencing, allowing advertisers to understand how streaming and digital ads influence the audience.
Household graphs become particularly important when you need cross-device targeting. If your campaign must reach the same household on multiple screens, using a household graph allows for cohesive messaging. If you want stronger attribution, you use household graphs to better attribute campaign impact. For example, a user might see an initial ad on their smart TV but convert on their phone.
You use a household graph if you require omnichannel measurement, as they help unify measurement across channels, making it easier to evaluate ROI on campaign spend, understand audience reach, and compare the performance of CTV vs mobile or desktop.
Attribution becomes more accurate when you understand how households interact with ads across devices. For example, a CTV ad seeds awareness. When the same household visits your website via a mobile device, the household graph ties these interactions together and attributes the exposure accordingly.
Advertisers have a clear competitive advantage by using a household graph, especially in retargeting campaigns.
It delivers better audience resolution. You have less guesswork and more confidence in who’s being reached. It also aligns ad exposure and synchronizes your messaging for the household. As we mentioned above, a household graph helps close attribution gaps and reduce spend.
