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From the Era of Web1 to the Rise of Trend Oracles: AI Unveils Fashion’s Web3 Era of Hyper-Personalisation

For those readers who may not recall the static fashion trends of the Web1 era, in the mid-90s, trends were primarily dictated by magazines, luxury brand creative directors, and catwalks. It was very much a top-down, centralised approach, where style and messaging were concentrated in the hands of a few people, who had the luxury of dictating it to the rest.

The arrival of Web2 ushered in a new level of democratisation, breaking the traditional trend processes and spreading out power by allowing communities to connect, grow, and communicate through global platforms. This era also gave rise to disruptive trend-forecasting entities such as WGSN, Fashion Snoops, Mintel, and others e. WGSN was founded in 1997 and managed by my friends Marc and Julian Worth, before the business was sold in 2005 (it continues to operate today, aiming to predict trend and culture on a near-decade-long horizon), and those four letters quickly became synonymous with a new idea of what trend should be in the always-on, social media age. Companies like WGSN became prominent trend oracles for brand, retailers’, and designers across the world.

However, as time passed, the rise of this responsive trend model – where mass market brands chased consumers rather than setting the benchmark – translated into a feeling that high-street brands and retailers were homogenising their design DNA, all chasing the same transient fashion fads, and all dipping into the same forecasting buckets of silhouettes, materials, assets, colour themes, and styling detail.

This sentiment was not helped by the rapid expansion of social media channels such as Facebook, Instagram, Twitter, and YouTube, which prompted these trend forecasters to expand their capabilities, by harnessing big data, and artificial Intelligence (AI), to evolve trend forecasting into a quasi-science backed process of forecasting that has become today’s best-practice model, despite its limitations. For most brands, capturing trends from social media has become a purely reactive exercise – and one that’s conducted at an unmanageable scale, with organisations deploying all those data-driven insights just to try and stay one step ahead of what a constantly-changing global market wants.

The future, then, is going to have to land somewhere in between these two extremes. It should allow brands and designers the ability to set new creative directions at the same time as empowering micro-communities to create and own new trends.

Which is why, looking ahead to the future of Web3 in fashion, I believe that a transition from Web2 is not only inevitable, in fact, it’s already started.And my estimate places this shift to within a 3–5-year timeframe. This is not to say that the present model will totally shift during this period, but rather that new disrupters will continue to create new businesses, and platforms that will allow consumers to understand the value of their own data and how they share it with brands, and retailers that will start delivering hyper-personalised product options.

During this transformation period, we can expect greater emergence of businesses both new and existing – businesses that will leverage Web3 architecture, operating on decentralised internet platforms using blockchain technology, together with AI. Software developers will continue to harness this architecture to create decentralised applications (dApps), and these same businesses will create open Application Program Interfaces (APIs), leveraging other related ‘smart’ software and hardware that can contribute to a product’s smart lifecycle.

The Web3 era will focus on facilitating hyper-personalisation, utilising Generative Artificial Intelligence (Gen-AI) and real-time data to cater to individuals, and small fashion tribes, instantly responding to emerging trends rather than leading to brands being overwhelmed and left behind the curve when trying to cater to individuals, or a small tribes fashion specific needs.

How? I can imagine a near-term future where, instead of adhering to macro trends of today’s Web2 model, brands will leverage new Web3 models linked to micro-real-time, hyper fashion trends. This Web3 approach will allow brands, and retailers to analyse precise consumer data feeds, including consumers’ own avatars, fit measurements, styling preferences, and other data elements. It’s this vision and approach that will enable the transformation of trend analysis – moving from a single-brand mass-market approach to a detailed real-time micro-trend approach, culminating in not only mass-customised fitting products, but also serving the individual customer!

Mass customisation (MC) for those that are not familiar with the term, is a model that has been used for several decades, offering bespoke elements (waist, thigh, leg length) while maintaining production at scale, addresses the challenge of fitting products and understanding customer preferences. Mass customisation should not be confused with Made-To-Measure (MTM), and Bespoke Manufacture of a single garment, which is typically a wedding dress or a suit made for a special occasion. That individual tailoring remains a separate and viable sector.

The main difference with Mass Customisation is exactly that it caters to the masses (large groups of people sharing the same measurements), providing the consumer with a Made-To-Measure feeling, but obviously without a product being made in accordance with an individual’s specific body measurements.

Advancements in technology have overcome previous technological limitations of delivering on mass customisation, cost effectively, and are now able to propel the fashion industry into a new era where brands can now, using AI as a starting point, economically deliver on mass customisation, (Single Ply Cutting, Direct-to-Garment, or Direct to Roll dyeing, whole Knitted garments, 3D printing, etc.), resulting in further disrupting the current status quo by offering personalised designed and fitting products that are “tailored” for their loyal customers.

As that infrastructure is being built out, Web3 will also be changing the game on the consumer-facing and trend side of the equation, creating advanced AI models that filter down to the consumer’s real-time “trend oracle.”, and will even guide the product through localised sustainable manufacturing processes. This two-way interaction will also allow consumers to shape the design process based on their individual data types, owning and designing on their own avatars, and constantly developing their own fit preferences.

Web3 will predict future trends more accurately than previous models at the same time empowering customers to understand the value of owning their data, including personal avatars, purchasing history, style choices, lifestyles, their individual likes, and dislikes. Web3 customers will be able to choose which brands access their data, completely transforming their shopping experience. Furthermore, decentralised marketplaces, tokenized garments, and enhanced transparency will mark the future of Web3, opening doors to not only unparalleled personalisation, but it will allow all brands to deliver on their environmental sustainability goals!

As Web3 unfolds, questions will of course arise on how brands will adapt to a decentralised marketplace, the emergence of new e-commerce platforms connecting designers directly with customers, maintaining brand identity in a decentralised ecosystem, leveraging blockchain technology for end-to-end transparency and ethical sourcing. Brands will need to adapt their marketing and communication strategies to resonate with smaller communities and down to the individual’s level, where power dynamics will shift towards the consumer.

In conclusion, just as Webs 1 and 2 gave way to progress, Web3 is emerging fast, and I anticipate the fashion and footwear industry following suit by adopting a decentralised approach supported by Web3. This shift will not only support environmental sustainability in the Retail, Footwear, and Apparel (RFA) sectors but will also bring financial benefits through reduced product returns, derived from truly understanding your customer’s total needs.

Mark Harrop,
With nearly five decades as a technology advisor to the fashion industry, Mark has dedicated his career to helping the world’s renowned brands, retailers, and manufacturers achieve efficiency savings throughout their entire supply chain via informed technology investments.

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