Spin cycle: Inside the world of trendspotting and fashion forecasting

Hindustan Times

16-05-2025

'It's tough to make predictions, especially about the future.'
That's a quote attributed to Yogi Berra, the American baseball catcher, manager and coach. Others, including physicist Niels Bohr and filmmaker Samuel Goldwyn, have made similar observations.
Predicting future trends has long been an obsession with humans. One of the earliest future-trend predictors was the priestess Pythia at Delphi, in Ancient Greece. She was revered for her prescience on war and other matters of state.
Pythia actually lives on, in modern boardrooms, as the much-revered Delphi Technique, in which a team is made to predict future outcomes, individually, and through repeated rounds arrive at a 'most probable' future prediction.
Jump forward about 2,000 years and there was the Nostradamus (Michel de Nostredame), the 16th-century French astrologer, perhaps the most famous historical figure associated with future predictions.
In India, meanwhile, one of the seven Vedic sages, Bhrigu, is seen as the father of Indian astrology (or future predictions).
Predicting the future used to be about the security of a kingdom or a throne: What plagues or enemy action might be coming? Was a drought, or some form of pestilence, imminent? These were the things the people in power were most eager to prepare for.
Today, in addition to governments, businesses need to be better prepared to face the future. This is part of what drove sales of futurist Alvin Toffler's 1970 bestseller Future Shock, which has sold over 6 million copies around the world. The book traces the impact of rapid social and technological change on individuals and society. It highlights the psychological distress and disorientation caused by too much change in a short period.
Toffler coined the term 'future shock', and defined it as a state of anxiety and disorientation resulting from an accelerating rate of change. And he was writing more than two decades before the first glimmers of the internet appeared.
The way technology has continued to change our lives would make Toffler a modern-day Nostradamus indeed.
While he dealt with what the future had in store, in broad terms, John Naisbitt, through his 1982 book Megatrends: Ten New Directions Transforming Our Lives, made trendspotting a public obsession. Naisbitt identified 10 major trends that he believed would reshape society, business and culture, as the world transitioned from the industrial era to the information age.
Some of these — a shift from an industrial society to an information society; forced technology to high-tech / high-touch; national economies to a world economy; hierarchies to networking — hold true, more than 40 years on.
We'd gone from predicting the future to predicting trends. We were starting to let technology guide the way. It was game on for trendspotting.
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Part of the credit for this goes to Faith Popcorn, author of the 1992 bestseller Popcorn Report: Revolutionary Trend Predictions for Marketing in the 1990s. She used consumer analysis and cultural-trend analysis to predict long-lasting societal shifts, clearly distinguishing them from fleeting fads. Some of the trends she spotted were cocooning (retreating into home for safety and comfort), cashing out (leaving high-pressure jobs for simpler, more meaningful lives), clanning (seeking out a community of like-minded individuals), and small indulgences (splurging on select premium products). Many of these have remained relevant, through downturns and tech bubbles bursting, through boom times and a pandemic.
Today, most market researchers, consultancies and marketing agencies have dedicated trendspotting teams. Seeing into the future remains the holy grail, except now what most people really want to know is: What could the next bestseller be?
Exercises in this direction are more or less what annual trend reports are about. It's still very much a game of chance and circumstance. So many annual reports are irrelevant even before the proverbial ink has dried.
The better future predictors are able to look beyond the obvious and examine adjacencies.
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I remember analysing the future of the Indian engine-oil market, in the late 1990s.
The large brand we were working with was focussed on the commercial-vehicles segment. This brand was the leading provider of engine oil to truck operators. Business was growing, in the wake of liberalisation.
But there were several storm clouds visible. For one thing, engine and engine-oil technology was changing, increasing the gap between required oil changes from one month to sometimes several. This was a variable visible to the company. In our conversations, they often spoke of how they hoped to counter it.
Then our research team sat down to do a little research of our own, and that's when we realised no one was looking at the data on trains.
The growth of the Indian Railways was about to cut into the transit of goods by road. Even a rise of a few percentage points in the share of goods transported by rail would change the math entirely for trucks, and for our client, who was hoping to sell them tonnes of engine oil.
We presented our data to the client, who had not considered goods trains part of their competition until then. It was an interesting reversal of the case study in Theodore Levitt's seminal Harvard Business Review article, Marketing Myopia.
There, he pointed out how American railroad companies saw businesses decline — not because fewer goods or people needed to be moved from place to place; those numbers grew. They lost business because they remained confined to their pre-existing ideas of where a railroad should go, who it should serve, and how. The roads became the primary mode of transportation because they evolved to go anywhere the consumer needed them to go.
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If a product as fundamental as engine oil, in a segment not exactly bustling with competitors, can be affected by future trends, it's easy to see why prediction is such a core piece of the puzzle, in industries defined by flux: such as fashion (and personal technology, for that matter).
Predicting the future of fashion — and being able to shape what people will choose — is, in many ways, the only way this industry has ever been a sustainable one. Because each of us may, of course, wear whatever we please. We may make our own clothes, barter and swap (as indeed we should, at this point in our carbon-emissions struggles).
Fashion isn't like food or pharmaceuticals, education or indeed most consumer goods (cars, toys, furniture, engine oil). In all these cases, it is relatively easy to herd the consumer. Offer them a range of colours, styles and price bands, and they will feel sufficiently pandered to.
Fashion is different. What we wear must reflect who we are; and each of us prefers to think we are unique. Who is to say what we will piece together, or how long a given preference will hold?
Trend-forecasting, in fashion, is not for the faint-hearted.
Which is why the effort is an aggressively ongoing one. It starts with colours (of the season, the year, the moment). Colour-trend 'forecasting' can be traced to at least the 1800s, and the creation of swatch books for use by French textile mills.
With the development of synthetic dyes in the mid-19th century, the need for such forecasting grew, as the range of available colours boomed.
Now, as you are likely thinking to yourself, who buys anything in a shade of peach fuzz (the 2024 colour) or mocha mousse (the colour for this year)?
I asked this question of a leading menswear brand that makes most of its revenue from formal shirts in plain blue or plain white.
Why bother with the colour of the year, every year, I asked? Clients want to see a colourful new collection; it reassures them that their brand is in step with the times, I was told. In other words, in order to sell blue, or white, sometimes you have to make a little peach fuzz.
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How much of it is really prediction?
Well, it's not just someone throwing darts at a shade card.
Market researchers talk to consumers, by the thousands. They talk to domain experts, opinion leaders and influencers. They use ethnographic studies and observational studies. They hit the roads to see what's popular within different segments of a market, and why.
Modern trend predictors use a plethora of digital tools. They scrape images from social media and analyse them at scale using AI programs. What do pictures shared on certain kinds of social-media handles have in common? What's the demographic? The geography?
Global companies like WGSN, Mintel and Heuritech have built an impressive track record of spotting trends by using a mix of qualitative research (cultural observation, expert interviews, scenario planning) and quantitative analysis (data mining, social listening, consumer analytics) to detect both strong and weak signals of change. Time series analysis is used to spot if a trend is growing, how fast will it grow, when will it peak. AI algorithms help through it all.
Read the story alongside for more on how this works (and the corresponding dangers of it).
How often do they get it right? Let me end with a little story.
When Segway PT, the self-balancing transportation device, was launched in December 2001, it was hailed as a revolution in personal transportation. Steve Jobs, who we know saw the future better than most mortals, wanted to invest in Segway, and offered them $60 million.
Dean Kamen, the inventor, turned him down. Kamen didn't want to relinquish that much control. Then, Segway slowly tottered, it was blamed for being too highly priced, too heavy, for having 'unclear practical utility'. It faced regulatory challenges too.
Learning from the mistakes made, the company's new owner, the Chinese firm Ninebot, pivoted. The original Segway PT sold an estimated 100,000 units between 2002 and 2018. Its successor the Segway-Ninebot eKickScooter, more stable, more solid and easier to maneouvre, shipped one million units in its launch year, 2018, alone.
Often, spotting a trend is just the start. A lot depends on what we do next: how well we listen as new cues pour in; how agilely we pivot to what a customer will actually buy.
It's true of almost any sphere of life. Seeing into the future is just half the spell. The other half is scrambling fast enough to meet it, and being agile enough to correct course midway.
(Ambi Parameswaran is an independent brand coach and author of 11 best-selling books on branding, advertising and consumer behaviour)