Chinese Designers Try Predictive Trend Analysis On For Size

    Predictive trend-spotting is already a fast-growing area of investment for many major brands and e-commerce retailers. But can they really see the future?
    Predictive trend-spotting is already a fast-growing area of investment for many major brands and e-commerce retailers. Photo: Shutterstock
      Published   in Fashion

    For years, brands have looked to localize products for the China market using a mix of data on what has traditionally sold, plus some measure of gut instinct and — more often — a “stick a zodiac animal on it” mentality that really doesn’t work. Now, some designers and brands in China are getting more sophisticated and taking a data-driven approach to trend identification and product development.

    A new collaborative report by Promostyl and Youthology, generated by Alibaba’s Tmall Trend Center, recently looked to prove out whether machine learning-driven research can help predict fashion trends through the middle of next year. According to the researchers, the goal of the report was to help designers “create more on-trend items to reduce end-of-season markdowns and improve their business performance.” For potential customers, the goal is to help them plan purchases ahead of time and “pre-style” for the months to come.

    According to the report, we can expect to see four style trends heat up in China over the course of the next nine months. They are:


    The report predicts that a “fairytale-like and poetic aesthetic” will continue to trend among urban women, a counterpoint to the “monotony of busy urban life.” The look is exemplified by “romantic pinks and earthy neutrals” and includes ruffled tops, chiffon dresses, tasseled mini totes, and men’s linen jackets.

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    Very similar to the “techwear” trend that has peaked in the West over the past couple of years, this trend incorporates high-tech materials over natural fibers, and is heavy on “metallic purple-blue colors matched with reflective gray and neon greens.”


    Not quite taking it as far as “Hanfu” enthusiasm, this trend incorporates somewhat traditional Chinese elements ranging from the old standby, dragon embroidery, and mines nostalgia for popular Chinese candy from the 1980s and ‘90s, ancient poems and Chinese philosophy.


    The final trend identified in the report is one of greater interest in sustainability and a muted “back to nature” vibe. Customers interested in this trend are eco-friendlier and more interested in a “throwback rural aesthetic” that’s more California bohemian.

    According to Alibaba, some designers have already put these trends out onto the runway to see how they fare. Olivia Zhu, creative director of the brand Elfini — which was taken to Paris by Alibaba to take part in Tmall’s China Cool event — said she incorporated some of the trends in her choice of colors and silhouettes. Said Zhu, the brand mixed yellow candy-colored suits with green puffy skirts in a nod to the “poetry” trend and reported a good response by brand ambassadors.

    Predictive trend-spotting is already a fast-growing area of investment for many major brands and e-commerce retailers, but the fact remains that it is a young area and one that should still be taken with a grain of salt. Simply put, we’d need to see more about the methodology of the report before it’s clear that this report actually identified new trends rather than simply filtering out the four that are being most talked about online anyway.

    The question is, does this report really predict a trend, or by ranking these four newish trends and putting the weight of Alibaba’s massive and ubiquitous marketing infrastructure behind them, make them gradually more popular by the middle of next year, thus fulfilling their prediction?

    This very well may be the case. But whether it’s the tail wagging the dog or the other way around, it’s clear that predictive analytics are becoming a hot commodity in China’s fashion market, and regardless of the methodology, we’ll definitely be hearing more about it from Tmall and others.

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