Meabh Quoirin landscape

Direct-to-consumer is dead. Or is it just beginning? NFTs are the next big thing. Actually, no one wants NFTs. Quiet luxury is en vogue. Scratch that – it’s all about maximalism. You heard it here first: these are just some of the 10,000 new consumer trends driving the future of business, writes Meabh Quoirin, CEO and Co-Owner, Foresight Factory.

If you work in any consumer-facing company, you’ve likely come across reports and data sources that feel a lot like the above. You’ve probably thought to yourself: where do I go from here? How on Earth am I supposed to keep up with this endless stream of trends? And how do I know which ones will offer a genuine transformative opportunity for my brand?  

The truth is most of the phenomena that get labelled “trends” these days are just passing fads that will fade quickly into history. Moreover, companies tend to borrow trends from one another and repackage them with new names, creating an echo chamber across the industry. The amount of “trends journalism” out there means that businesses are drowning in information and so-called insights, making it ever more difficult to know what’s relevant – and, most importantly, what needs to be acted upon now to create a better future. So how can you filter out all the white noise to unlock real white space opportunities?  

Diversity of perspective, diversity of data 

This is a question that we at Foresight Factory make it our job to answer, and key to our process are two points of diversity: diversity of perspective, and diversity of data.   

Foresight Factory is female-co-owned by me. Over half of our leadership team is female, including our product director, head of sales, marketing director and head of data. We have offices in London, New York, Chicago and France. Our consultants and analysts represent 12 nationalities and speak 17 languages between them. We also work with a network of 60 trendspotters across 24 markets who provide on-the-ground cultural context. But even with all those diverse perspectives built into the business, we recognise our limitations as humans with biases.   

Intelligent deployment of generative AI

That’s where diverse data and the intelligent deployment of AI and machine learning technology come in. We carry out annual surveys of 46,000 consumers across 27 markets, and the hundreds of consumer segments we track range from electric car owners to single parents, from business travellers to cat owners. The data from these surveys form the backbone of our trends, which quantify genuine consumer demand. We then map those trends to commercial activity, so that brands can instantly identify opportunity spaces according to their target audiences and markets. By layering these different data together in a blended approach, we give brands the confidence that the insights they unearth are anchored in real consumer need, and that they will create scalable demand at a global and local level. Because, as we all know, too much time and money are wasted on chasing the wrong thing.  

Technology is crucial to extracting these insights – but it’s not the be all and end all. Leaders should keep this in mind when it comes to shiny new tools like generative AI. There’s no doubt that ChatGPT can be useful for organisations everywhere, but generative AI is only as smart as the question you ask it. Automated services like these can only be one slice of the strategy especially since they have obvious limitations when it comes to thinking about the future. Still, very soon, we’re going to see marcoms teams turning to AI to write copy, deliver briefs and come up with creative ideas, and it will continually get more advanced and efficient in these areas. But how will we know that these ideas will really resonate? How can we be certain that they are responding to a core consumer need? And how can we be sure of the truth behind their ideas? Another major pitfall of generative AI is that it has difficulty with nuance. While the analytical mind can prioritise, telling you what to ignore as well as what to pay attention to, generative AI cannot.  

 Getting the timing right 

Timing is another important factor to consider. Generative AI may be able to give you an insight or idea, but it won’t be able to tell you when to act on it. Already, countless brands fall into the trap of misjudging the pace of change and activating at the wrong time. They overestimate demand for a signal or innovation, not realising that consumers often take a while to catch up with technology. If they execute too soon and the execution fails, they blame the signal itself, not realising that they were simply too quick off the mark. Only by harnessing robust consumer data, mapping it to commercial activity, and identifying those first-mover and fast follower opportunity areas can brands be sure that they are seizing the moment at the right moment.   

The business world is awash with data and “trends”, and generative AI is only going to create an even bigger wave. To stay afloat, organisations need to recognise that success isn’t about merely having AI; it’s about what you do with it. To really thrive, companies must zero in on relevance by layering diverse perspectives with diverse and robust data – or risk drowning in distraction.