The world of fashion e-commerce is a dynamic and rapidly evolving industry, with consumers increasingly turning to online platforms to satisfy their style cravings. However, with growth comes challenges and the fashion e-commerce landscape is no exception. From inventory management to personalised recommendations, the industry faces numerous hurdles that can impact customer satisfaction and operational efficiency.
Team Apparel Resources delves into the transformative power of artificial intelligence (AI) and generative AI in addressing these pressing issues. The article highlights four key uses of AI that are reshaping the fashion e-commerce landscape, promising to enhance user experience and streamline operations.
AI helps reduce online return rate
In an era where online shopping continues to thrive, addressing the issue of high return rates due to poor fit is crucial for the success of apparel brands. AI technologies have emerged as powerful tools to mitigate this challenge. Through personalised sizing recommendations, virtual try-ons, fit predictions and enhanced inventory management, AI empowers retailers to offer a seamless and satisfying shopping experience while significantly reducing return rates.
The cost of poor fitting
While online shopping offers convenience and a vast array of options, one persistent challenge faced by consumers and retailers alike is the high return rate of apparel. For customers, it results in frustration and inconvenience, while for retailers, it leads to financial losses and logistical complexities. The cost of handling returns can be substantial, often ranging from 38 per cent to 40 per cent of the original product price. A significant portion of these returns stems from fit-related issues, where customers are dissatisfied with how the garment fits them.
Brands are leveraging Artificial Intelligence (AI) to mitigate the return rate by addressing sizing issues, enhancing the overall shopping experience and optimising inventory management.
How AI addresses sizing and fit issues
AI algorithms use a combination of customer data, historical purchasing behaviour and body measurements to provide personalised sizing recommendations. With their implementation, brands can offer size suggestions that align with individual preferences and fit requirements, thus reducing the chances of receiving ill-fitting items. This level of personalisation enhances the customer’s shopping experience.
Alongside, many fashion brands now employ AI-powered virtual try-on tools. These tools allow customers to visualise how a garment would look and fit on them using augmented reality technology. AI can predict the fit of a garment based on the product’s specifications and customer data. This helps customers make more informed purchasing decisions such as selecting the right size and style, reducing the likelihood of a poor fit, ultimately reducing the number of returns.
Let’s examine how some prominent apparel brands have leveraged AI to tackle the issue of high return rates due to sizing problems:
ASOS, a leading online fashion retailer, utilises an AI-driven fit assistant that provides personalised size recommendations to customers. By analysing previous purchase history and user-provided measurements, ASOS has managed to reduce return rates significantly. It reported a 9 per cent reduction in returns among users who interacted with the Fit Assistant.
Zalando, Europe’s largest online fashion platform, implemented an AI-driven feature called ‘Fit Finder’. Fit Finder combines size charts and machine learning algorithms to suggest the right size for customers. This innovation resulted in a 2 per cent – 3 per cent reduction in returns, enhancing the overall shopping experience.
Stitch Fix, a subscription-based styling service, employs AI to match clothing items with customer preferences and sizes. Through continuous feedback and data analysis, Stitch Fix achieves a low return rate compared to traditional e-commerce models.
| With growth comes challenges and the fashion e-commerce landscape is no exception. From inventory management to personalized recommendations, the industry faces numerous hurdles that can impact customer satisfaction and operational efficiency. |
AI (and Generative AI) makes consumers’ purchase decisions easier
The world of e-commerce has witnessed exponential growth over the past decade. However, with the convenience of shopping online comes a unique challenge for consumers – how to determine if a particular product, especially clothing and cosmetics, will suit their body type, skin tone and personal style preferences. This dilemma often leaves shoppers in a quandary, resulting in abandoned shopping carts and lost sales opportunities for e-commerce brands. To address this issue, AI-powered virtual styling tools and image consulting have emerged as a revolutionary solution, helping customers make informed decisions and boost e-commerce sales significantly.
One of the most remarkable applications of AI in e-commerce is the development of virtual styling tools and image consulting services. These tools leverage advanced algorithms, computer vision and machine learning to help customers visualise how a product will look on them, taking into account their unique characteristics.
AI is exceptionally adept at understanding customer preferences and behaviour. Virtual styling tools can analyse a shopper’s past purchases, browsing history and even social media activity to recommend products that align with their tastes. This not only increases the likelihood of sales but also ensures that customers are exposed to a wider range of products they may not have discovered otherwise.
The leading Indian fashion e-commerce platform Myntra has seamlessly integrated a ChatGPT-powered functionality within its shopping application. This innovative feature adeptly assists users in articulating their shopping preferences through natural and uncomplicated conversations. By doing so, this generative AI tool significantly streamlines the process of product discovery, sparing users the need to perform numerous individual searches. Consider a scenario where users intend to make purchases for an upcoming wedding event. In such cases, consumers of Myntra can utilise Myntra’s advanced MyFashionGPT with prompts detailing their specific requisites. Subsequently, the tool rolls out a curated selection of outfit choices that align with the provided information, helping consumers make their mind for purchase decisions.
| One of the most remarkable applications of AI in e-commerce is the development of virtual styling tools and image consulting services. These tools leverage advanced algorithms, computer vision and machine learning to help customers visualise how a product will look on them. |
AI (and Generative AI) algorithms can predict fashion trends and do demand forecasting
One of the main challenges faced by the entire fashion supply chain is managing inventory. These issues can be largely curbed with AI-based trend forecasting solutions, which serve to improve fashion brands’ collection planning process, and in turn, avoid understock and overstock.
To do this, AI-led tools offer data-driven trend forecasts to e-commerce brands and retailers. How does this AI technology work? The image recognition technology analyses millions of shared images and videos on social media to pinpoint thousands of attributes, from silhouettes to colours to textures. This data is then collected to provide an inside look on a number of useful insights for our clients, such as trend growth between particular seasons, optimal launch times, consumer segmentation, apparel category and geography.
With these insights, brands can integrate trend forecasting within the entire collection planning process, limiting under-performing products (or those predicted to do so, thus reducing overstock), accelerating markdowns, transferring inventory, improving cross-team communication, reducing time-to-market speed and more.
Since Generative AI is expected to take over AI, the fashion trend predictions are also being done by this new-age technology that uses a variety of statistical models. In layman’s terms, generative AI algorithms look at all the past historical trends and ‘near-to-accurately guess’ what is next. As the model gets proficient, it starts generating new fashion designs, descriptions and styles that align with the trends it has learned from training data. For example, it might generate descriptions of clothing items with unique combinations of colours, materials and embellishments that are emerging as popular trends.
Instead of relying on trend reports and market analysis alone to inform designs for next season’s collection, both mass market fashion retailers and luxury brands’ creative directors can use generative AI to analyse in real-time various types of unstructured data. Generative AI can, for example, quickly aggregate and perform sentiment analysis from videos on social media or model trends from multiple sources of consumer data.
So, how does this whole process work? Creative directors and their teams could input sketches and desired details – such as fabrics, colour palettes, and patterns – into a platform powered by generative AI that automatically creates an array of designs, thus allowing designers to play with an enormous variety of styles and looks. A team might then design new items based on these outputs, putting a fashion house’s signature touch on each of the looks. This opens the door to creating innovative, limited-edition product drops that may also be collaborations between two brands.
| Too many products, coupled with large number fashion shoppers looking for something very specific becomes a hard to manage event and, for that to be effective, brands need a good and error-free catalogue management system. |
Automatic Product Tagging reforming fashion business – thanks to AI
Inconsistent and unstructured tagging of fashion product descriptions and facets lead to broken textual searches on the website or app and impede product discoverability for both retailers and shoppers. Too many products, coupled with large number fashion shoppers looking for something very specific becomes a hard to manage event and, for that to be effective, brands need a good and error-free catalogue management system.
For an online shopper, the simplest way to locate and identify a product on an e-commerce portal or marketplace is a simple text search. More often than not it happens with fashion consumers searching for a product, let’s say joggers, who can on the online portal of a brand see more products other than those only related to joggers in their search queries. The major reason behind this is inconsistent and unstructured tagging of product descriptions and facets that leads to broken textual searches on the site or app and impedes product discoverability for both retailers and shoppers. A lot of useful search results also get excluded during this process. However, the same can be rectified with the use of automatic product tagging technology that uses AI-based image recognition system to identify exact products that a shopper searches for!
What is ‘Automatic Product Tagging’?
Automatic product tagging is a process that eliminates manual fashion product tagging, organises and tags photos in the product catalogue based on their characteristics, leveraging advanced Artificial Intelligence (AI) algorithms. These algorithms speed up the fashion tagging process, making it automated and eliminating the need for human intervention. Simply put, this automatic product tagging is a process that generates rich metadata for catalogue assets.
Diesel, a globally renowned lifestyle brand, is part of one of the biggest European luxury fashion groups – OTB. Diesel started using AI-powered automated product tagging solution in 2020.
Challenges faced by Diesel: New products coming in every season needed to be sorted, styled, photographed and then sent to the team for attributes to be tagged manually. The process brought up three critical issues: slow onboarding time; high time and effort for manual tagging; inconsistency in catalogue.
To solve these issues, Diesel needed a tool that could automate the tagging process, while being capable of integrating with their existing PIM tool; recognise and specifically call out whether certain iconic logos are present in the products; standardise the values and attributes for use across the organisation; streamline the entire process end-to-end and save time. With automated tagging, Diesel could tag products at a fraction of the time it took to manually tag products. AI tagging has been shown to improve catalogue processing time by up to 90 per cent. Instant tagging was implemented for Diesel with the aim of being able to get its products to the market faster. A total number of 20,000 images have been tagged on Diesel’s online portal since then with over 24,000 tags being generated and 150 batches being uploaded.
