With the late November announcement by Amazon Web Services that it was launching AWS Clean Rooms, an analytics service for organizations to analyze and collaborate on combined datasets without sharing or revealing underlying data, it is a good time to revisit an earlier Redpoint blog on data clean rooms.
Around the same time as the AWS announcement, we ran a blog that focused on why data quality is a vital component in a data clean room. With the proliferation of both independent (Disney Select) and walled garden (Roku, Walmart Connect) data clean rooms, and Amazon Web Services now joining the fray with a data clean room designed specifically for analytics, the question of data clean room use cases appears to be top of mind.
This blog will look at one specific use case: how an organization can monetize its own first-party data using a data clean room.
A Data Clean Room and High-Quality Data
First, a quick review of data clean room basics. The working definition of a data clean room – both independent and walled garden versions – is a place for companies to match and merge two or more first-party data sets without exposing any personally identifiable information (PII). All use cases entail some form of analyzing, matching and building models using anonymized data, utilizing a type of data encryption that never exposes PII. Ideally, customer data will only enter a data clean room if a customer explicitly authorizes its use for marketing or advertising purposes. When compliance and consent are followed, companies are positioned to become digital advertisers for their customers – using fully anonymized data that is compliant with all regulations.
As an example of how a company can monetize its own first-party customer data as a digital advertiser, let’s use a (fictional) global specialty retailer. With millions of customers accessing its mobile app and website the company – just like a Target, a Walmart or another enterprise retailer – sits on a wealth of first-party data. Its reach makes it an attractive advertising partner for smaller companies who want to cast a wide net over a large pool of prospective customers. (This type of advertising partnership, called a Retail Media Network, gained popularity during the pandemic.)
Imagine that one of those smaller companies is a regional sports outfitter. Interested in targeting some of the retailers’ customers, it provides some data about the type of customers it intends to reach, and matches it with the retailer’s data.
Perhaps the outfitter, which specializes in ski equipment, wants to advertise to customers who live in either New England or the Pacific Northwest who have booked travel in February for trips to Vail, Colo. or Jackson, Wyo. It buys a list of customers who have booked travel to those ski destinations from a third-party, and combines it with some first-party data – maybe browsing sessions from customers in the target demographic who have searched for deep powder gear. With those constraints, the outfitter – without sharing PII – sends data it has on potential customers to the specialty retailer.
The retailer then applies a full data cleansing, normalization, data matching and enrichment to a pool of first-party data to create a Golden Record for any identity that matches those constraints. Those records are then anonymized and put into a data clean room along with any cohort information. The outfitter can then access a list of persistent IDs in the data clean room and query against the list using its own data to see what kind of match rates it produces. Providing there are enough matches (contracts will stipulate a minimum threshold to protect anonymity), the outfitter will have its target audience.
Beyond the Cookie: Trust and a Data Clean Room
At first glance, this type of arrangement seems like little more than a simple data share. The outfitter, one might argue, is simply buying a wider audience. Skeptics might point to the efficacy of a third-party cookie for reaching a prospect on an affiliate channel and ask what’s the difference.
The two main points to be made here are, first, data quality: the outfitter isn’t just tracking a device across the internet, nor is it just buying a lookalike audience, it is buying the retail company’s perfected first-party data, and using it to target actual anonymized customers. Second, unlike a cookie, a data clean room will ideally only contain data for customers who have consented to share anonymized data for the purposes of receiving a more personalized customer experience, to include relevant advertising.
Reinforcing both points is where a company like Redpoint comes in, with the rg1 customer data platform (CDP) that is instrumental in setting up the clean room with unassailable first-party data – high quality cleansing, matching and normalization in a performant, secure perimeter.
Advanced identity resolution and the creation of a persistently updated Golden Record are completed at the point data is ingested from every source that contributes to a unified customer profile. As for compliance, a Golden Record will also include a customer’s opt-ins/outs, right to be forgotten/erasure requests and all other conditions for how their data is allowed to be used.
The level of trust partners ultimately have in the shared data environment of a data clean room begins with the trust in the CDP that delivers the anonymized data. When the use case for a data clean room is to monetize one’s own first-party data, a CDP that perfects an organization’s data at the point of data ingestion and accurately resolves the identity of an individual customer, household or other entity will ensure maximum value. All parties will reap financial rewards, either through the data clean room service or, for the buying partner, via customers who will be on the receiving end of a hyper-personalized advertising campaign.
Related Redpoint Orchard Blogs
Ease Distrust in Advertising with Data Clean Rooms & PII Vaults
First-Party Customer Data: The Healthy Alternative to Cookies
There are No Third-Party Shortcuts to Understanding Customers
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