Data Clean Rooms: Driving The Data Dating Game

In this guest post, InfoSum’s ANZ GM Richard Knott (lead image), says with cookies’ end imminent, it’s now up to brands and marketers to collect first-party data…

Although Google has postponed the deprecation of the third-party cookie in Chrome to 2024, most marketing organisations are ending their long-term relationship with cookie-reliant marketing strategies.

Consequently, the power of cookies has been waning for a while. Third-party cookies have been blocked by default in Firefox and Safari since July 2020, and are not available on any kind of linear TV or CTV. Indeed, the title of Nico Neumann, Asst. Professor at Melbourne Business School’s presentation to IAB MeasureUp in October said it best “Why no marketer should miss third-party cookies”.

With consumer privacy and data protection legislation on the rise in Australia and globally, there’s no point delaying the inevitable. This means many organisations are beginning to explore new ways to discover and leverage the optimal data to drive their marketing efforts in the medium to long-term.

For an increasing number, the answer to breaking up with the cookie is to embrace blind-data dating with the introduction of Data Clean Rooms.

Let’s dig into what they are, why they are gaining popularity and why they are poised to be a match made in heaven for brands, publishers and consumers.

It’s getting personal

With cookies on the way out, brands and media owners are now focused on collecting and nurturing first-party data. This involves informed consent and is more ethical and more accurate. Marketers today know that customers are much more than the sum total of their transactions, and they’re working hard to develop trust and create a personalised relationship with them.

But it’s not enough for a brand to collect first-party data, however rich and reliable it might be. To be successful, marketers have to contend with two separate matches: a brand-to-consumer match to determine what types of consumers they want to target, and a consumer-to-media match to figure out where to reach them most efficiently. Blind dating is hard enough as it is, who would want to pick the venue at random on top of it?

The only way to make it work is through privacy-safe data collaboration. The challenge here is sharing datasets while striking the right balance between data protection and data usefulness. And this is where data clean rooms come in.

How data clean rooms work

Trading partners—such as advertisers and media owners, or retailers and distributors—have long recognised the benefits of sharing data with one another. But previously, the technology wasn’t there to do it securely and without risking their customers’ privacy.

Now, there are data clean rooms. A data clean room is a secure environment where multiple data sources are matched and analysed, without sharing or compromising the data itself.

This means marketers can plan, measure, and activate while fully protecting their data’s security and customers’ privacy. There’s no data movement and matching takes place in seconds.

Marketers can find out what their customers’ favourite channels are. They can verify who was exposed to their campaigns, where, and when, and compare the effectiveness of various channels for each campaign.

This enables them to size up and fill the gaps in their cross-media campaigns, trying new media channels and experimenting with media spend by channel. It’s possible to measure the true reach and frequency and study what types of profiles.  With this, marketers can enrich profiles with more actionable attributes and develop lookalike models that make sense to their business.

The importance of lookalikes

Developing new lookalikes is important as older segmentation models have lost relevance. Marketers have traditionally split consumers up into segments using systems such as PRIZM and VALS, which were developed in the 1970s and 1980s. Categories such as “township travellers” and “generation web” no longer make sense in 21st century Australia.

By matching first-party data sets through data clean rooms, much more accurate segments can be identified that make sense to a specific brand. This enables personalisation that’s more relevant and less intrusive. McKinsey reports that personalisation programs based on rich and diverse first-party data can boost customer satisfaction rates by 20% and sales conversion rates by 10-15 per cent.

For example, UK bank TSB wanted to create a radio campaign targeting potential applicants for a new type of account. As a financial institution, TSB was subject to exceptionally strict rules governing the handling of sensitive data. Even more importantly, protecting this data is critical to maintaining customer trust.

Using a data clean room, TSB was able to match its five million UK customers against Global radio network’s audience of over fifty million individuals. It could analyse the intersection of data “as one”, without either party having to share any sensitive data. TSB remained in control of its data at all times and ensured it met strict data security and compliance requirements.

We need to be doing data differently: smarter and more ethically. As the last third-party cookies crumble into dust, marketers who have stayed a step ahead will enjoy the most effective, data-driven campaigns, with a successful “marriage” between brand, media and consumers.