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Blog / Could First-Party Customer Data Transform The Way We Target Ads?

Could First-Party Customer Data Transform The Way We Target Ads?

While in the past, using first-party customer data seemed far too complicated to be a viable way of acquiring customers compared to cookie-based targeting, stringent privacy laws have seen the most analytically mature marketing forces shift their focus to investigate first-party targeting strategies more seriously. 

In our last blog, I took a look at why first-party data is currently in the spotlight. But now it’s time to ask an even more critical question: how could this focus on first-party data impact customer acquisition?

As cookies dry up, CMOs are looking at a small pool of first-party data 

Retargeting isn’t as simple as it once was. Before recent privacy laws introduced opt-in cookies, it was easy enough to identify someone to retarget later, whether that was a known customer or a prospect that has seen an ad and gone to your website. 

The problem is, while walled gardens created by publishers like meta once brought highly effective and measurable data insights to marketers, ID and cookie deprecation means we’ll soon have to wave goodbye to those third-party cookies. More platforms are automating opt-out as the default for customers, and that means retargeting is going to be much more difficult to do — especially on the prospect side. 

However, there is an opportunity to use first-party data in advertising in the identifiable realm, and while this kind of strategy where you have identifiers on both ends will inevitably whittle down your pool to a few key customers, there is still a lot of untapped potential in this kind of data. 

Forward-looking enterprises are experimenting with using first-party data as a seed to build audiences. By uploading first-party data into a cleanroom, marketers can connect with a publisher’s first-party data to join these datasets via a hashed, privacy-safe way, connecting known customers with known customers. 

Inevitably there’s going to be a fair bit of data loss, but as long as there’s still a good representation, marketers can start spotting patterns in how their customers interact with their brand whilst on the publishers' site. The marketer has contextual data of that small pool of customers, and they can start building a lookalike model to give back to the publisher. They can ask the publisher to target customers that look like the ones in that small pool. 

How could first-party data be used for acquisition?

While marketers have been working to collect first-party behavioral customer data on preferences to serve more relevant content, many have been looking beyond using this data for building retention-based strategies. More and more brands are experimenting with acquisition initiatives based on first-party data, and there are a couple of ways they’ve been doing it.


  1. Cohort-based targeting - this involves using anonymized first-party data to build lookalike models, which can be grouped by certain traits to personalize messaging.

    The pool of targeting based on behavior is smaller for first-party data, but there’s an opportunity to grow your customer base at a relatively low cost through your own channels. 

  2. Contextual-based targeting - displays ads to a reader based on what they're actively looking at, rather than their behavior.

    First-party data could play a role here by helping brands identify what content resonates with their customer base.

Activation of first-party insights for acquisition is relatively nascent. The third-party cookie hasn’t gone away yet. With the emergence of these targeting approaches, advertisers are trying to figure out if they could still reap those benefits from third-party cookies at lower CPMs where they can target anybody, and weighing up if it’s worth risking their long-term strategy for more short-term wins.

Who’s using their first-party data well?

Retail, fintech, and telecom are some of the more experienced industries when it comes to using first-party data for post-sale engagement, as they have the best access to first-party data. Currently, first-party data is much more common within loyalty strategies than on the acquisition side.

For example, using data-driven journey maps to anticipate what your best customers will need next. A large beauty company has done this, and by uploading first-party data within a cleanroom to join specific publishers and build data-driven journey maps of best customers, build messaging and interaction strategies for their existing customers.  

The opportunity now is to use it in an acquisition situation. That’s where there’s investment and growth. An example of this would be the same company that went on to build lookalike models to target prospects directly with that publisher. 

Nestle has also been using first-party data, which they spoke about at RampUp in San Francisco. It used a cleanroom to upload first-party data and join it with retailers’ first-party data. From there, the team was able to build customer lifetime value models for Nestle customers and measure their CRM programs. 

Clean rooms could give CPG brands the visibility they’ve been looking for

CPG and manufacturing companies don’t have a strong base of first-party data, and the data they do have often isn’t tied to conversions. This is why we’ve seen a huge increase in D2C consumer brands like dollar shave club in the last few years - and why big holding companies have been so keen to buy up these companies. 

If CPG brands are working with a distributor, even if some of their sales are direct, they’re still missing a large chunk of that conversion data from the retailer. CPG titans like Nestle and Unilever could increase efficiency if they enter into an agreement to join first-party data and collaborate on a targeting strategy with the retailers. For now, this is only really workable for companies working at scale - but with these marketing forces leading the way, it’s worth keeping an eye on for the SMBs out there. 

Here’s what CMOs should be doing to gear up for the first-party data revolution:

  1. Evaluating what type of first second and third-party data you’re using, and how it currently fits into your data strategy. Understanding where that data is located, and the quality of it. 

  2. Putting effort beyond that data governance aspect, because a lot of the success of using first-party data will be dependent on consent, quality, and how that data is connected - especially as you start to scale the strategy up.

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