It is no coincidence that data clean rooms are an emerging trend for marketers just as third-party cookies are on their way out. We all want to serve our customers hyper-personalised ads and content, which is why companies have relied on the tracking cookie for so long.
As data clean rooms are based on first-party data, there is a palpable excitement that they are likely to become a far superior path for presenting customers with relevant content in the context of a customer journey, not just online behaviour on one device.
The data clean room is gaining traction as a privacy-compliant way for two or more companies to merge and match first-party data sets without exposing any personally identifiable information (PII). Using a special type of data encryption, data clean rooms allow companies to analyse, match and build audiences using anonymised data without ever accessing or decrypting PII.
A retail media network is a common type of data clean room, with a use case similar to advertising through third-party cookies – except it relies on first-party data instead.
In retail media networks, the publisher (a retailer, media network or large company with direct channel access to customers) sells digital advertising on its branded sites. An advertiser onboards an audience into a data clean room with defined aggregates and cohorts for the customers it wants to target. The clean room lets the advertiser know how many records matched with the publisher’s database and gives an indication of potential campaign reach. The advertiser can then choose to send targeted advertisements to defined segments within the matched records through the publisher’s network.
ITVX and All4 are examples of retail media networks. There are also independent data clean rooms, where two or more companies that may lack the data of a company like Google or Amazon will pool their data to approximate a larger data set and better understand how to interact with and advertise to a larger audience.
Disney Select and AWS are notable independent data clean rooms, performing data matching across different data sets not as publishers but as conduits between the parties that are combining data sets.
High quality data and a personalised experience
In both a retail media network and an independent clean room, the goal of the advertiser or the company pooling its data is the same: to provide an audience with a more personalised advertisement (or series of advertisements) through the publisher’s channel.
Ideally, an advert will be relevant for a customer in the context of an individual customer journey, leading to a superior customer experience. Consider, for example, a cycling company that wants to target a segment of customers it knows through online browsing sessions is considering purchasing saddlebags and other touring gear. It provides this first-party data set to a retail media network, which matches it against its own data set. The shared audience then receives advertising on the network’s channels that is hyper-relevant to the customer journey.
Peeling back the layers of this example, what should be obvious is that effective use of a data clean room is dependent on the quality of the customer data provided by the advertiser. The quality of the data directly drives the potential match rate with the publisher’s data and the advertiser’s ability to reach the matched audience.
The role of a CDP in using a data clean room
For both the advertiser and the publisher, the effectiveness of a data clean room is dependent on high quality data, which is where a customer data platform (CDP) comes into play. Because the output of a data clean room is only as good as the data that is shared before it is encrypted, data quality processes and advanced identity resolution steps must be completed as part of the entry criteria.
Advertisers must not harbour the misconception that a retail media network partner will handle data quality for data sets outside of what it puts into the data clean room. In fact, before entering a retail media network, an advertiser should verify a potential partner’s data quality processes. Is identity resolution outsourced to a third party? Does it create a Customer360 from its own first-party data?
With a robust, enterprise CDP, advertisers should ensure that any data provided to a retail media network is accurate and reflects the latest, most relevant information about a customer to maximise match rate. To drive the best results, advertisers need to provide the data clean room with the highest quality first party data. Best practice shows the necessary data quality steps such as data cleansing, normalisation, enrichment when appropriate, match/merge tasks and attaching a persistent ID to produce pristine single customer views within finely tuned, granular audiences.
Conversely, if you provide a data clean room with data that doesn’t accurately reflect your audience, it will not only return fewer matches, but those it does return may not reflect what is meaningful to a customer in the timeline of their journey. You don’t want to serve up an advert for touring saddlebags to a customer who may have purchased them last week.
As a still-evolving technology, data clean room use cases are still being refined. In terms of enhancing customer experience, however, it is becoming clear that the quality of a data clean room output benefits greatly from using a CDP to ensure data quality prior to merging encrypted data sets.
