How to make data work harder in 2025

The Future of Brands 2025

How can brands make their data work harder?

It is a constant question in an industry that has become data-rich as businesses have digitised. But, as Salesforce regional vice-president Felicity Starr noted at a panel last month at The Future of Brands in London, there is a big gap between being data-rich and “insight-rich”.

According to Starr, the “low-hanging fruit” for brands to improve the quality of their data insights includes activating first-party data and driving personalisation via low-lift predictive models.

For Ozone director of strategic solutions Frances Lazenby, getting more out of audience data starts with consent.

“The more we think about the consent and compliance with the data, the more we can start thinking about our audience as humans, not data points,” she explained. “That’s the first step in driving more significant brand growth and outcomes.”

On the other hand, John Lewis & Partners planning performance and media lead Stellina Argyropoulou suggested that brands adapt their wider philosophy about audience beyond understanding the “who” with regard to audience make-up.

Instead, brands should move towards understand the “why” — why and under what circumstances consumers choose to shop with their brand.

Tashan Nicholas, director of data research company Magic Numbers, agreed that the best data insights are born out of changes in philosophy by brand leadership.

“Not all data is created equal,” he said. “It’s really important for us to understand what decisions can be made with what data and what [decisions] can’t.”

He advised the easiest thing for businesses to do to get more from their data is to “get people in the room and interrogate” it by asking the right questions.

“It’s better to have an approximate answer to a good question than a fantastic answer to a bad question,” he advised.

Watch the full conversation

In conversation with session chair and The Media Leader tech editor John Moulding, the group unpacked the increasing importance of mixed media modelling (MMM) and econometrics not just for media but for brands’ business strategies more generally.

According to Argyropoulou, MMM has informed John Lewis’ wider business decisions based around consumer interests. This has led to marketing “absolutely” gaining more respect in its own boardroom, thanks to the insights MMM has delivered not just for marketing purposes.

Nicholas agreed that MMM helps marketers “speak the language of the CFO”.

He explained: “A lot of our measurement techniques look at online touchpoints and so, when we look at attributed sales, we’re looking at a version of the world that ignores all the other offline touchpoints that have happened just before.

“What MMM or econometrics is doing is stripping all of that away and understanding what is the incremental touchpoint that has actually driven that.”

While MMM does take quite a lot of time to generate useful insights — at least three years of weekly data, according to Nicholas — recent advancements in modelling technology have meant that the speed at which insights can be developed has improved over time.

That has, in part, led to a shift from brands pursuing an understanding of their audience based on demographic personas to caring more about behavioural insights and signals, according to Starr.

She noted that brands can infer behaviour and intent based on how users engage with different online platforms, allowing them to create a “self-optimising journey” based on predictive understanding of consumers’ moods in different scenarios.

But, Starr warned, innovation will be needed this decade as more consumer journeys end in automatic purchases.

“In five years’ time, 20% of all customers worldwide are going to be machines,” she said. “So how do your machines sell to their machines?”