Nielsen has long worked with advertisers to provide smarter third-party insights. Now the company is working directly with marketers to sharpen first-party data and refuel martech investments via privacy-compliant panel data merged with brands’ first-party IDs. The upshot is more granular customer segmentation and understanding via synthetic lookalikes to boost owned channel commercial impact.

Synthetic audience segmentation and augmentation, says the company, is powerfully risk-free. Hence, Nielsen is launching Advanced Audiences and aiming to deal directly with marketers, enabling them to power their first-party marketing initiatives.

“We’ve got an extensive consumer profiling data set that has been in market for a long time,” explains Nielsen's Pacific Head of Advanced Analytics, Glenn Channell.

“Now we are extending into the client-side in terms of how our market insights can interact with their own data stack.”

Beyond transactions

Brands with first-party data, says Channell, have a trove of information about how their customers are interacting with them – i.e. what they are buying and when.

But that can be as far as it goes. Nielsen is aiming to blend brand first-party data with synthetic zero-party data to drive higher customer lifetime value by giving brands deeper insight on what messages and offers their customers are more likely to respond to. That means marketers can easily create many more segments – and trial more tailored offers to them, for example.

“What we want to do is complement brands’ first-party data with our whole of life view of the consumer at the population level and fuse that to their data to extend marketers’ understanding of their customers, enabling them to better activate,” says Channell.

“Brands have transaction and interaction data within their customer data platform (CDP). We can now give them the full scale view of everything outside of that – the things they don't know about the consumer,” says Channell. “Key lifestyle behaviours & interests, attitudinal & opinion trends, sporting passions, purchase intentions and decision making criteria, across a broad range of consumer categories. It's extensive quant data, but it gives you ‘the why’ about the consumer, which helps inform first-party segmentation and targeting, as well as external partnerships and sponsorships.”

Crucially, the modelled data is fully privacy-compliant. That’s because it’s based on synthetic lookalikes built from Nielsen’s fully consented 30,000 Australian panel – which can then be merged and used to create lookalikes within a brand’s CDP.

De-risking targeting

Jonathan Betts, Nielsen’s Executive Director, Commercial Growth and Product Strategy, thinks modelled and synthetic data is coming into its own as regulators plot the new privacy landscape.

“Five years ago, people may have looked down their nose at synthetic and modelled data – they wanted gathered data.” But incoming privacy changes pose existential challenges to that approach, says Betts.

“If you want to take your own dataset and start enriching it, was the consent managed properly? If you don’t know, you’re exposing your business to significant risk,” he adds.

“Our consented panel takes data from real people, but generates synthetic output. So for brands, it’s privacy-secure versus other ways of trying to add to what you know about your customers.”

Betts says Nielsen panel data extraction has been vetted by the company’s privacy team, with any potentially risky elements – like geolocation, political or religious beliefs – vetoed and removed.

The upshot is the privacy-compliant panel data can be layered on top of marketers’ first-party data to create more granular segments. Via machine learning, these additional segments can be automatically optimised and personalised to drive greater uplift and performance.

“You’re giving the machines more signals and more data points, which means automation can create more segments for you,” says Betts.

“So segmentation is one use case. The second one is personalisation: Brands can now better understand what high value customers care about to better inform messaging, partnerships – or any other element of marketing.”

CLV extractor

First-party marketing, says Channell, is all about increasing lifetime value from the investments made to attract those customers in the first place, i.e. advertising.

As acquisition costs soar, marketers are increasingly focusing on existing customer lifetime value (CLV) – as evidenced by Mi3’s FY 25 Marketing & Customer Benchmarks report. Per Mi3’s survey, wringing greater value from owned channels, direct consumer connections and martech sunk costs, was flagged by marketers representing $3bn in collective budget as a key KPI priority.

Conversely, customer acquisition KPIs were flagged by the benchmarks report as declining – perhaps due to soaring performance media prices.

Sustained hikes in biddable customer acquisition costs – i.e. sustained CPM inflation on Google, Meta, Amazon and TikTok – are leading to significant unit economics impacts, particularly for ecom and direct to consumer brands.

Channell believes marketers can counter those ROI pressures by re-oiling first-party martech engines to drive greater lifetime value returns.

“Advanced Audiences data can help power that process,” says Channell. “Previously Nielsen’s data was being used to plan advertising in paid channels. Now brands have the opportunity to leverage the same datasets, overlaid with their CDP data, to deliver more intelligent first-party marketing – and maximise lifetime value from the advertising investment that acquired their customers in the first place.”

Example use cases

1. A QSR business creates customer segments for its customers purchasing via the app and is able to trial and optimise offers across these segments to increase purchase frequency and basket size.
2. A travel booking company is able to understand the life stage and household size of its database members and tailor the destinations, price points and key messages in its email marketing to increase conversions and revenue.
3. A health insurance company can build a model to predict customer churn to support a program to retain these customers as policies come up for renewal.
4. A high street retailer can identify which sports codes and clubs best align with their loyalty members to support their partnership strategy.
5. An ecommerce marketplace business can profile their customers and their purchase intentions to secure promotional support from their partners as part of retail sales events.

Unlock advanced audience data at Advanced Audiences - Australia | Nielsen