There's more to clean rooms than just anonymizing data

When clean rooms were first introduced, they served as secure, protected environments where personally identifiable information was anonymized, processed and stored for measurement or data transformations in a privacy-compliant way.

But now clean rooms have evolved to perform a number of tasks that go beyond their original capabilities. Many major brands are using these new capacities to drive innovation, illustrating how others can use clean rooms to unlock bigger gains for themselves.

Here are three innovative ways marketers are currently using clean rooms via Epsilon PeopleCloud clean room partnerships.

Go beyond traditional retail media

Retail media networks are the talk of the marketing town, and one major pharmacy retailer is using clean rooms to make its media network more effective. With new clean-room solutions, the retailer’s brand advertisers are able to run campaigns more openly on their own terms. With Epsilon PeopleCloud clean room solutions, and through a data sharing collaboration agreement, brands can now have access to curated sets of data owned by the retailers themselves. This enables them identify overlap, create custom audiences and learn more about prospects and customers thanks to precise behavioral data points previously unknown from such brands. It also creates a way for retailers to directly monetize parts of their owned transactional data, with one or several brands at a time.

With the new services, brand advertisers can maximize the reach and scale of campaigns across all digital channels. Advertisers can activate across the retailer’s media network DSP powered by Epsilon DMS, as well as receive audiences within their external DSPs for activation, with more connections being added in the future. Based on CORE ID, closed-loop reporting is possible where advertisers tie campaign performance to the retailer’s sales data and optimize to drive efficiency, results and learnings for future campaigns.

Create a single customer view across channels

A multi-brand jewelry retailer had a problem: Its customer data was disjointed across brands, negatively impacting its ability to develop propensity and attribution models. The retailer had a limited ability to test the agency of record’s (AOR) performance due to existing media commitments and click-based metrics limited its understanding of multichannel engagements and personalization.

To create a single customer view, the retailer combined its online and offline customer data leveraging Epsilon’s clean room solution. It built a test to evaluate its clean room audiences against the AOR’s audiences, and had acquisition via Google’s DV360, Facebook and email. The brand also delivered personalized product category recommendations in emails.

With Epsilon’s help, the jewelry retailer created a dashboard for its CMO that highlighted inefficiencies across vendors and demonstrated optimal channel combinations.

After all this, the retailer saw a 27% year-over-year increase in new customer sales while reaching 54% fewer prospects, significantly diminishing media waste; they also witnessed two times higher revenue for existing customers on Facebook and DV360. The brand opted to reach fewer prospects in favor of reaching more existing customers who had stronger lifetime value and therefore repurchasing opportunity for the brand. The retailer had 1.5 times higher conversion rates for PeopleCloud audiences vs. the AOR, and 2.8 times lift in revenue per person when received email and display vs. display alone.

Uncover growth audiences and increase ROI

A popular national coffee chain wanted to move from a view of just its loyalty customers to a persistent and connected view of all its guests to drive incremental store visits and increase revenue. It also wanted to include people who frequented competitor brands and potential customers in this view.

Using a clean room solution enabling privacy, psedudonymization of data and holistic customer views, the brand was able to merge multiple data assets, keeping identity at the center for ultra-targeted marketing. The brand used third-party location data to segment the population based on level of loyalty to the brand, time-of-day preference and category shopping frequency. And then, the coffee chain used both owned and competitive store visitation data to size and percent the most relevant population.

The coffee chain assessed every individual’s average in-store spend and averaged at the segment level, then predicted the ability to increase users’ frequency of store visits by analyzing the likelihood of individuals in given segments to move up in visitation trends. Using this strategy, the brand saw a 35% in-store lift visitation for targeted audiences and had four growth audiences.

Data clean rooms are a new technology in the advertising space, and as brands and tech partners dig into their capabilities, it’s clear that the use cases go far beyond the original intent of the technology.

This article was originally published on Adweek, December 2022.