16-05-2025
Why the cart is always full: The rise of the microtrend in fashion
Fashion trend forecasting is difficult enough when the customer is eager to toe the line. Conformity, for centuries, was the industry's ally.
The Swinging Sixties birthed a new generation of forecasters who knew they couldn't rely on lookbooks and swatches from Paris alone, because vast swathes of young people, whether in London or New York, seemed determined not to follow trends but defy them.
How does one make any predictions in a world like this? It turns out, rebellion can take predictable shapes too.
Pioneering forecasters Leigh Rudd (an entrepreneur) and David Wolfe (a fashion illustrator) scoured not just shops and fashion events in London but streets, clubs and resorts as well, gathering data on early indicators of change.
The result: Rudd's consultancy, IM International, famously predicted the 'Hard Times' trend of the 1970s, made up of rebellious looks put together using workman boots, oversized T-shirts, overalls, aprons, and the revolutionary idea of tattered blue jeans.
The early tatters were introduced by young wearers, with the trend travelling backwards, to factory floors, where brands reluctantly acceded to it, and even today continue to rip their own new jeans to make the cut.
Where else have experts trained their eyes, in efforts to see what young people will try next?
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The internet, of course, was key. Between the ease of managing inventory, reaching out to the customer directly, and ferrying goods more quickly and more seamlessly around the world, brands such as Zara, a pioneer since the 1970s, would force timelines down to as little as 15 days from designer's sketchbook to retail shelf.
Shelf lives would shrink. Prices would fall. Buyers would become less concerned with durability, and more taken in by 'how cheap everything is'.
As sales volumes grew, so did carbon footprint. Fashion as an industry now accounts for an estimated 347 million to 2.1 billion tonnes of CO2-eq. For perspective, 347 million tonnes is as much as all of India emits in 40 days.
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Another thing fast-fashion spawned? The rise of the microtrend.
There have always been fads, particularly in spaces such as accessories and headgear. Think of Britain in the Regency era, or flapper age New York.
Thanks to social media, today's microtrends tend to be more widespread and shorter-lived.
Think of cottagecore, traceable to the aesthetic of Taylor Swift's 2020 album Folklore; a trend that seemed to last a few weekends. Think of Barbiecore, and the hot-pink everything inspired by Greta Gerwig's 2023 film.
There have also been 'mob wife' (French manicures, big coats, heavy gold jewellery; traceable to certain TV shows); and the Brat aesthetic (a neon, Y2K-inspired aesthetic traceable to the 2024 Charli XCX album).
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As consultancies scramble to predict these, a new tool they are deploying is, of course, AI.
Companies such as the Paris-based Heuritech, set up in 2013, use a proprietary artificial-intelligence program to analyse images posted on social media. 'We combine statistical approaches and our proprietary deep-learning model to be able to say that… in 24 months, there will be a 13% increase in market demand for leopard print for womenswear in India,' the company said, in a statement to Wknd.
A similar process is followed by forecasting labs in India.
VisioNxt, the fashion-forecasting initiative of the National Institute of Fashion Technology (NIFT), launched by the union textile ministry last year, uses an indigenous deep learning model called DeepVision, paired with observations from 850 trendspotters across the country (most of them NIFT students and alumni-designers) to identify possible cultural shifts.
'Using these datasets, we can tell how a trend of short-sleeved shirts, for instance, is gaining traction among a particular age group, gender and geographic location,' says Kaustav Sengupta, director of insights at VisioNxt and an associate professor at NIFT.
Developing the Indian AI algorithm wasn't easy, he adds.
To begin with, there was a need for standardisation of terms. What exactly is a kurti? What does one call a long skirt that's not a lehenga? (It's called a lancha, incidentally, and is traditionally worn with a long blouse and dupatta.)
The VisioNxt team began, Sengupta says, by creating a standardised taxonomy for Indian fashion: a glossary of over 180 words, defining in precise terms ideas such as the aanchal, jhumka, pathani suit, sharara, zari and jutti. They now release annual trend reports, quarterly reports and trend snippets, customised for clients that include e-commerce platforms, apparel brands, weavers and manufacturers.
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How much of all this is just an educated guess (as so many things are – risk assessment, financial consultancy, pop-up menus)?
Consumers need to understand that trend forecasting can be very self-fulfilling, says Talia Hussain, a London-based researcher of sustainable production and consumption through retail and market-making, at the Institute for Creative Futures, Loughborough University.
If forecasters say mauve will be a trend, then fashion brands make clothes in mauve, magazines and advertisements feature mauve items, and the colour will appear in stores for people to buy.
'Often, the purpose of the forecast is to align brands, media and customers around the idea of mauve, so that it becomes a trend. They are a way for the industry to manage customers and direct us to buy the things they are making.'
Even the fact that trends are increasingly short-lived could be traceable to an industry that wants customers to buy more clothes, and discard them more quickly in order to buy more.
'This is, of course, part of what makes fast fashion so damaging as an industry,' Hussain adds. 'Whether customers respond or rebel is finally up to us.'
