Fluency

AI

The Drum: Can we quantify desire? Under the hood of M&C Saatchi’s brand desire engine

Tim Spencer, CEO of Fluency and Rhonda Hiatt, CTO of Clear are interviewed by The Drum on the power of the new ‘Brand Desire Engine’.  

Data-backed marketers have made serious strides in the last couple of decades in figuring out what people desire (and why, and how much). We sat down with two people who claim to be further along on that quest than most: the team behind M&C Saatchi’s ‘brand desire engine.’

No prizes for this level of insight, but marketing is about getting people to buy (or in some cases do) stuff. Between the messaging and the action is the mysterious realm of wanting: desire.

If you could know what desire is, what makes it tick and how to measure it, that’d be a useful tool. That’s why brands and agencies are willing to pump so many resources into human understanding labs, psychometric research and a raft of qualitative and quantitative techniques to peel back the musty curtain that obscures the reality of want.

Earlier this year, M&C Saatchi unveiled its new tentpole desire play: the ‘brand desire engine’. Working on the confluence of eight Artificial Intelligence (AI) platforms, using psycholinguistics and machine learning (ML) and a billion (and growing) rows of data, the pitch is that it will allow chief marketers to pinpoint how desire for its brand shifts, and in response to what.

All of this data is out there telling us what the brand is like, what it’s about, what’s good about it, what’s not so good about it. The problem is that it’s not being tapped; it’s not being derived properly. 

Tim Spencer, Fluency CEO and Co-founder  – The Drum

But what does that really mean? How, really, can anyone demystify desire? We spent some time with two leaders from the M&C agencies behind the engine: Rhonda Hiatt, Chief Strategy Officer at strategy arm Clear; and Tim Spencer, Chief Executive Officer at data specialists Fluency.  

The greatest survey 

 
Clear has published brand desire rankings for years; in the past those rankings were decided by a consumer survey. While that methodology was, according to Hiatt, “robust” and “sophisticated”, its replacement tells an interesting story about big data’s power in the modern marketing industry. Clients, she says, were starting to glean insights about the desirability of their brands from large data sources, but those sources weren’t joined up and might even tell conflicting stories. The brand desire engine is built around the goal of metabolising all that data into a clear understanding of desire. 
 
“We thought, there has to be a better way than just surveying a lot of people,” says Hiatt. “We’re not saying surveying isn’t a great tool. It’s absolutely part of our methodology here; it’s still hugely helpful and insightful. But we wanted to broaden that perspective and be more diligent with the data. 
 
The survey is a hundred-year old methodology, but, says Spencer, “the brand in 2022 is in a very different position to where we were in 1920 … We have huge datasets wrapping around the entire world, which we are codifying for the first time. We dont have to ask someone what they think or feel; we can go on to Twitter, we can look on search engines, we can get a strong point of view from review sites. All of this data is out there telling us what that brand is like, what it’s about, what’s good about it, what’s not so good about it. The problem is that it’s not being tapped; it’s not being derived properly.” 
 
Those data streams are many: M&C, they tell us, buys in almost £1m of data off-the-shelf from the likes of YouGov, Kantar and Google; it also feeds in reams of publicly-available data, and clients can add first-party data of their own. Set up at launch to spit out results for 200 top brands, and aiming to grow to map out “all brands that we can get good sizeable data on” (perhaps 5,000), they can feed in those data sources for other clients as they come along.  
 
See original article here