80% of customers say dissatisfaction with online search is a barrier to purchase. 

 

Isn’t it time to look again at search?  

 

The old approach search based on SEO content, manual listings, basic filters and technical language is dead. And shoppers want more: 82% of customers want AI to assist in reducing the time they spend researching for what to buy.  74% of consumers are walking away because they are often paralysed by choice. This volume of choice is leading to lower conversion and abandoned carts. 

 

According to the Business of Fashion, 50% of fashion executives consider product Discovery and Search as the Generative AI with the most potential. 

 

Comprehensive Enriched AI Search

 

We are moving away from non-personalised basic filters (size, colour) to real time recommendations rooted in detailed product attributes and shopper speak ie "I’m looking for a suit based outfit with a casual vibe for an upcoming tech event" will return recommendations that go beyond the colour to pick up style and context.  The enriched product and shopper data facilitated by AI will recognise context, as well as intent to provide a personalised list of recommendations and suggestions.  Leaning into this space are Zalando & Thredup.  Zalandos AI assistant has been used by 500,000 people since 2023. 

 

Social & AI Platforms

 

We are moving away from curated content in social commerce based on previous interactions to curated content prediction and more intent based responses in social and AI platforms. Brand discovery is now as common on social media as through search engines. Tiktok’s “For you” page is a phenomenal example of how well-predicted curated content works. Pinterest’s investments in AI are delivering more curated shoppable content and product offers. Pinterest's new collage feature, which is powered by AI and computer vision technologies, is seeing three times the engagement of its traditional Pins. Then there are of course ChatGPT, Deepseek that offer shoppers an interactive conversational style that pulls in suggestions through reasoning albeit limited by its general application. 

 

The challenge is that there is no AI one size fits all for Discovery and Search in fashion. These are  tough to solve problems - garments are often non-digitised, lacking in detailed attributes that hinder retrieval and content-led discovery. Intent is challenging to unpack - ChatGPT is not a fashion stylist. 

 

GenAI, Enriched Data & Search 

 

Enter the next level GenAI solutions for fashion search & discovery.  

 

At the core of these solutions, is product clothing data on the supply side and shopper data on the demand side.

 

On the clothing side, there are product attributes - think material, colour, style, accent, fabric fit, measurements, true size, fabric fit, accents, models etc -  that link to consumer language and therefore intent. All extracted through simple imagery: for example a garment lay flat photo. These fashion attribute platforms will be the bedrock of next-gen product management systems. 

 

On the shopper side, the enriched data is the style preference, shape, body morphology analysis, size, preferences, purchases, returns and feedback that make up personal fashion profiles. These fashion shopper platforms will be the data provider powering predictive next-gen marketing campaigns and loyalty programmes, drawing on the aforementioned detailed product attributes to curate, and recommend.  

 

Imagine the search, discovery and hyper-personalisation possible….How many better fashion decisions will be made? 

 

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We have solutions available in these spaces, please do reach to chat, test, learn and deploy Fashion specific GenAI today. Our AI Listing - enriched product attribute platform to help your search and discovery - as well as our AI Sizing - our recommendation and shopper data solution.