Listen Up: Can Machine-Generated Insights Help Us Hear Audiences Like Never Before?

For years, data has been both a blessing and a curse for marketers. On the one hand, digital platforms and services enable access to data on a scale never before seen which, in theory, is allowing marketers to be increasingly personal and relevant in their output. But on the other hand, taking the right insights from within a mountain of data can be like searching for a needle in a haystack. With so much data out there, it can be hard to discern a coherent strategy from it all. And that’s rarely more true than when trying to understand just what your target audience truly thinks about a given brand. 

For Creativ Company, that’s where ‘ellain’ - a new machine intelligence solution that both summarises and quantifies human perception - comes in. It’s an AI-powered tool that measures how millions of consumers think and feel about a brand, campaign, or IP, by combining two previously distinct disciplines: Brand research, and social listening. ellain collects data, identifies the relevant patterns, and then creates bespoke reports to help marketers make sense of it all in actionable ways. To find out more about the tool and the thinking behind it, LBB caught up with Vibhu Bhan, the co-founder of Creativ Company. 

“Our new invention uniquely combines the capabilities with machine learning and LLMs to transform raw data into actionable insights rapidly,” Vibhu tells LBB. “It's designed to cater to businesses and analysts across industries who are seeking to drive decision-making with data-backed insights derived from vast online communities. We believe this tool will particularly resonate with organisations looking to harness predictive and generative analytics to stay competitive.”

But, crucially, this isn’t about automating creativity in the advertising world, nor is it about removing the human element that’s required for great and memorable work. Instead, ellain is aiming to enable marketing professionals to make the most informed possible decisions in their work in order to drive effectiveness. 

“We see creativity and data are symbiotic in generating insights. While data provides the factual foundation, creativity guides the interpretation and application of those insights into innovative strategies and solutions,” continues Vibhu. “Together, they empower businesses to tackle complex challenges with informed creativity through a balance between efficiency afforded by machine intelligence and human interpretation and application.”

It’s an approach which is underpinned, in many ways, by Vibhu’s own experience in the world of finance before entering adland. As a result of that journey, he believes there are a few things marketing can stand to learn from the data-and-detail oriented finance landscape. “Marketing can greatly benefit from the analytical rigor and precision inherent in finance,” he suggests. “Lessons include data-driven decision-making, risk assessment, and leveraging quantitative analysis to optimise marketing strategies and ROI.”

What’s pivotal to ellain and Creativ Company’s offering is the sense that the data being fed into the machine is reliable. On that point, Vibhu explains how, with the help of AI-powered Large Language Models (LLMs), we’re arriving at a point where data is going to be more precise and more trustworthy than ever before. 

“LLMs, when it comes to generating text, have come a long way in ensuring that predictions are both computationally and contextually efficient,” he notes. “Further, confidence in such algorithms is being reinforced through continuous validation and testing against diverse datasets, transparency in model development, and incorporating human oversight to mitigate biases and constraints present in any data-driven approach. Yet again, I see humans playing a critical part in keeping machine intelligence on the straight and narrow as the use case demands.”

And, excitingly for Vibhu, it’s only getting better. “What really excites me is the effort that the research and academic community is putting towards realising the next level of machine general intelligence,” he says. “The challenge that is commonly cited is that it's easy to teach machines what to predict next given a set of inputs, but it's very hard to train machines to use associative learning which leads to perceptions and ideas in humans. Associative learning is the link between emotions, physical feelings, environmental sensory inputs and the emotions, which gives us a full picture and understanding of a given situation.”

It’s all part of what Vibhu sees as an optimistic future where creativity and technology work in tandem. “I expect human creativity and storytelling to remain crucial, but assisted by machine intelligence in scaling and personalising content to meet consumer demands,” he concludes. “I also see human ingenuity and creativity exploding to new heights, now that machine intelligence will easily replicate well known creative concepts.”