AI in Fashion: From Trend Prediction to Enhanced Customer Engagement

AI's potential in retail is vast, impacting everything from customer experience to manufacturing. Its success hinges on harnessing the right data—often unstructured and vast in volume and variety. This requires meticulous management to ensure data quality and velocity. By effectively utilizing this data, AI can unlock new capabilities, offering predictive analytics for customer behavior, optimizing supply chains, and personalizing shopping experiences. The ongoing conversation about AI in retail boardrooms reflects its integral role in driving innovation and competitive advantage in the industry.

In our consultations, we identified these applications as highly promising and essential for any forward-thinking retailer.

Personalized Recommendations and Virtual Fittings

Personalized recommendations and virtual fittings enhance the shopping experience by tailoring product suggestions and fit to individual customers. AI is crucial here as it analyzes vast amounts of unstructured data like customer behavior patterns, body measurements, and style preferences to offer customized solutions and unlock access to various segments.

Whitney Cathcart, co-founder and CCO of 3DLook, discussed the importance of their virtual fitting room technology in an interview with Just Style, highlighting its appeal to the digitally-savvy, camera-friendly younger generation. This technology is notably used by Inditex's Bershka brand.

Brands can leverage this technology by integrating AI-powered tools into their websites and apps, which can analyze customer data in real-time to suggest products that align with individual tastes and sizes. This not only improves customer satisfaction but also reduces returns due to poor fit.

Fashion Forecasting and Trend Analysis

Fashion forecasting and trend analysis is about using AI to predict upcoming trends and consumer preferences. AI processes unstructured data from various sources, including social media, online searches, and purchase history, to detect patterns and trends before they hit the mainstream. This proactive approach enables brands to meet consumer demand more accurately and maintain a dynamic inventory.

In an interview with Vogue, Madé Lapuerta, the content creator behind Data But Make It Fashion and a Harvard trained data analyst talked about adding objectivity into a category that is inherently subjective can draw interest amongst fashion consumers who are “really interested in the way fashion and larger societal sentiment are intertwined.”

“When people say Chanel bags are a bad investment but u know prices have increased over 660 percent since 1990.” - Madé Lapuerta, the content creator behind Data But Make It Fashion

AI-enabled LLM Chatbots

Not all chatbots are AI-enabled; some operate on simpler, rule-based systems. These rule-based chatbots follow predefined paths and responses based on specific keywords or commands they are programmed to recognize. While effective for straightforward, predictable inquiries, they lack the flexibility and depth to handle complex or nuanced conversations that deviate from their programming.

In contrast, AI-enabled LLM (Large Language Model) chatbots, like those powered by GPT technology, use advanced natural language processing techniques to understand and generate human-like text. This allows them to engage in more dynamic, context-rich conversations, adapting their responses based on the interaction's flow. The capability to process unstructured data enables these AI chatbots to provide more personalized and effective customer service, making them invaluable for brands aiming to enhance user experience and gather detailed insights into customer behavior and preferences. This is particularly important in environments where customer engagement and satisfaction are directly linked to service quality and personalization.

"A language model is something that tries to predict what language looks like that humans produce," said Mark Riedl in an interview with Cnet, professor in the Georgia Tech School of Interactive Computing and associate director of the Georgia Tech Machine Learning Center. "What makes something a language model is whether it can predict future words given previous words."

Crowdsourced Mechanism and Campaign Recommendations

Crowdsourced mechanisms combined with AI enable brands to gather and analyze community feedback on a large scale to shape marketing campaigns and product developments. AI analyzes unstructured data like customer reviews, survey responses, and social media interactions to extract valuable insights that can then be pushed back to in-store marketing campaigns that adds value to the customers.

“How personalisation is executed has changed a lot, but the desire and the intent for personalisation from marketers has been pretty consistent. That is because it shows a customer that you are listening. Customers are also smart — and understand the concept of a value exchange.” SAP Emarsys CEO, Joanna Milliken said in an interview on Business of Fashion. “Through our research, we are seeing that loyalty is on the decline, because price and product are not the only factors that businesses need to compete on. They must also compete on that overall experience — and that is where differentiation can be found.”

Sentiment Marketing

Sentiment marketing involves analyzing customer opinions and emotions from unstructured data such as social media comments, reviews, and blog posts to tailor marketing messages. AI tools are critical for effectively parsing this data to gauge public sentiment towards products or brands. Brands can leverage this by using AI-driven insights to craft marketing strategies that resonate emotionally with consumers, potentially increasing engagement and fostering a stronger connection with the brand. This targeted approach helps brands to effectively address consumer needs and preferences, enhancing overall marketing effectiveness.

Sustainability as a Service

AI today can help brands assess and make better design and production decisions that can reduce their environment footprint. AI processes unstructured data from supply chain activities, sales logs,  product usage, and consumer feedback to offer insights on how to enhance sustainability practices.

“To constantly improve our trend forecasting and only produce what we can sell is an important piece of the puzzle to reach our goal of becoming net zero by 2040,” says Lise-lotte Löveborg in an interview with Guardian. Lise leads the fashion intelligence team at H&M’s headquarters in Stockholm. “To that end, we are starting to explore how AI can play a part in our forecasting process.”

The transformation of fashion through AI is business critical

The evolving role of AI in fashion is pivotal in reducing the errors associated with mass-fashion, such as overproduction and lack of personalization. By harnessing AI to tailor experiences and predict trends more accurately, brands can create greater value for customers while also advancing environmentally responsible practices. This strategic use of technology not only meets consumer demands more effectively but also minimizes waste, marking a significant step towards sustainable industry standards.


TICTAG is a leading provider of data collection, annotation, and AI enhancement services, dedicated to revolutionizing industries through the power of artificial intelligence. Specializing in maximizing AI-enabled ROI, TICTAG empowers organizations to unlock new levels of efficiency and innovation. With a focus on enabling organizations to harness the full potential of AI, TICTAG offers cutting-edge solutions tailored to meet the unique needs of clients across various sectors. By combining advanced technologies with unparalleled expertise, TICTAG is committed to driving innovation, enhancing efficiency, and unlocking new opportunities for businesses worldwide.

With offices strategically located in Singapore, South Korea, India, Hong Kong, Indonesia, and Malaysia, TICTAG operates globally to cater to the diverse needs of its clients across different regions.

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