Challenges and Strategies in Implementing Householding

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In our previous article discussing the phaseout of third-party cookies, we emphasized the significance of householding during this interim stage, when some browsers and devices have disabled third-party cookies and others have not. In our experience, householding emerges as the most intriguing solution amidst the current cookieless landscape.

We’ve witnessed a shift in the approach to implementing householding over the years. Previously, companies utilized household targeting to refine messaging and impressions, operating on the premise that households share similar interests and that users switch between devices within the same household. With the decline of third-party cookies, companies have extended this concept, leveraging the persistence of cookies on some devices to create a fundamental identifier based on households. This shift necessitates a corresponding adjustment in implementation approaches.

We developed a household targeting pipeline for our clients, comprising the following stages:

• Data collection: During this stage, we gather traditional cookie-based information associated with a single IP address or other available keys. The number of cookies collected varies depending on the presence of enabled cookies on devices within a household.

• Household identification: Utilizing third-party services like Tapad, we ascertain whether an IP address or other key is linked to a residential household rather than a corporate or communal setting.

• Segmentation: The segmentation stage of the householding pipeline mirrors the familiar process of user segmentation in cookie-based targeting. The distinction lies in defining segments based on households rather than individual users. Notably, attributes such as age or gender may be expressed as percentage ranges within households, reflecting collective traits rather than individual demographics.

• Targeting: This stage operates identically to traditional cookie-based targeting, where specific ads are targeted to specific segments.

• Measurement and optimization: Following targeting, we employ standard practices of measuring ad performance and optimizing campaigns, akin to cookie-based advertising strategies.

Overall, household targeting in ad tech enables advertisers to reach specific groups of households with relevant ads, potentially enhancing ad performance and ROI. However, it also raises privacy concerns, as it involves collecting and using data from multiple individuals within a household. It’s imperative to prioritize these privacy concerns when implementing and deploying household ad targeting.

Householding implementation challenges

During implementation for one of our clients, we encountered several potential challenges that could hinder the identification of users with household IP addresses. For instance, Google’s DV360 platform can identify and flag requests to the DSP, indicating the use of mapped cookies to target cookieless user traffic. To evade this kind of detection, we adjust the overall percentage of requests enriched with cookie-mapped household data.

Another challenge arises from Apple’s iCPR feature, which conceals user requests behind an Apple proxy IP address, rendering such traffic unsuitable for householding. In such cases, we resort to a fully cookieless targeting approach selected by our client, such as device fingerprinting or targeting based on the context of the visited site.

Additionally, collaboration with publishers presents a challenge, as explicit confirmation is required from each publisher to process household traffic. Each publisher must send a flag indicating readiness to process household traffic. Depending on a given publisher’s business model and contractual obligations, they may be unable to handle traffic enriched with cookie information under any circumstances. Therefore, it is important to determine what traffic each publisher is able to handle ahead of time and to strictly respect the flag sent by each publisher at runtime.

Conclusion

The transitional phase between fully cookie-based and cookieless ad targeting presents a challenging landscape. Nevertheless, it also offers an opportunity to mitigate revenue loss by leveraging novel approaches to utilize the remaining cookie data while allowing for further testing and refinement of fully cookieless targeting approaches. In our work with clients, we have found that householding is an excellent means to utilize the available cookie data effectively. In our next piece, we will provide a detailed account of recent householding implementation and deployment experiences with one of our long-term ad tech customers.

Learn more about Lineate’s custom solutions for AdTech