11 Ways Artificial Intelligence Is Transforming Fashion E-commerce

Over the last couple of years, we’ve been working with several fashion e-commerce clients to help them digitally transform. One area that we’ve been researching and exploring is how artificial intelligence (AI) can be deployed as a tool to help them with internal automation as well as to transform customer experiences.

There are simple things we’re doing today from a content and search engine optimization (SEO) perspective. We use AI to provide engaging titles, meta descriptions, compelling product descriptions, automated tagging, and to test and optimize different versions of the copy we’re writing. Our marketing platforms also incorporate AI into product recommendations, send-time optimization, and forecasting. However, this use is really just scratching the surface of the potential with regard to AI. 

Design, Production, Manufacturing, and Supply Chain Management

AI is revolutionizing the fashion industry by providing enhanced efficiency and automation in design, production, manufacturing, and supply chain management.

In the design phase, AI uses algorithms to analyze trends and consumer behavior, leading to the creation of designs that are more likely to resonate with customers. Additionally, AI-powered generative design technologies allow designers to set material and performance constraints, and the AI generates product designs meeting those criteria, leading to a more diverse range of designs and reduced waste.

In production and manufacturing, AI is helping to improve quality control through precise grading of materials and reducing errors in final product inspection. It can uphold a higher and more consistent standard for materials than humans alone, leading to improved garment quality. Moreover, autonomous robotics, powered by AI, streamline material handling in factories, reducing the time-to-completion for critical tasks and ensuring damage-free transportation of materials and finished goods.

AI also plays a crucial role in optimizing supply chains. It can predict potential disruptions, optimize delivery routes, and automate repetitive tasks. It also aids in gathering and disseminating relevant data, such as current inventory, demand trends, workforce needs, and raw material availability and prices. This results in better inventory management and forecasting, reducing wastage and costs.

Fashion Industry AI and Customer Experience

On the consumer front, AI is dramatically enhancing the online shopping experience. Personalized recommendation engines provide customers with suggestions based on their shopping behavior, improving engagement and increasing the likelihood of purchases.

Visual search capabilities allow customers to upload images to search for similar items in an online store, enhancing the convenience of shopping. Virtual try-on features, powered by augmented reality and AI, enable customers to visualize how clothes will look on them before purchase, reducing uncertainty and potentially decreasing return rates.

AI is serving as a powerful tool in the fashion industry, automating processes, increasing efficiency, and transforming the customer experience.

Federated AI Versus In-House AI

For small clients, deploying AI necessitates a different approach due to their limited resources and data. The machine learning models these clients can build are often hindered by a paucity of data, resulting in models that are under-trained and perform poorly. To overcome this, they often resort to sharing machine learning data and algorithms across clients, often referred to as federated learning. This allows the smaller clients to pool their resources together, combining their data to create more robust models. They train their models locally with their own data and share only the model updates, maintaining data privacy. This method not only enhances the performance of their AI systems but also provides them with the ability to leverage AI without having to make extensive investments in data collection and infrastructure.

Contrastingly, large e-commerce clients, with the vast data they generate and collect, are in a position to support their own robust data science implementations. These clients possess the resources to build, train, and fine-tune complex models using their own data. The sheer volume of data they have allows for the creation of detailed customer profiles, improved recommendation systems, and more accurate demand forecasting. Their scale also enables them to invest in cutting-edge hardware, software, and data science talent to constantly innovate and improve their AI systems. They have the capability to create bespoke AI solutions that are perfectly suited to their unique needs, an advantage smaller clients typically don’t possess. Furthermore, the large data sets allow for greater privacy control as data does not need to be shared externally.

Ten Examples Of AI Transforming Fashion E-Commerce

Whether it’s a solution that utilizes AI across clients or deployed internally by a brand, AI and its advancements are revolutionizing the fashion e-commerce industry, including fashion, in numerous ways:

1. Personalized Recommendations: AI algorithms use customer data to suggest clothing items that align with their personal style and previous purchases. This makes the shopping experience more personal and increases the likelihood of purchases.
   • Example: Wiser
2. Virtual Try-Ons: Advances in augmented reality (AR) and AI have made it possible for customers to virtually try on clothes. This helps customers visualize how an item will look on them before they buy it, reducing the uncertainty and potentially decreasing the number of returns.
   • Example: Robosize
3. Chatbots and Virtual Assistants: These AI-based tools can answer customer inquiries, help with product selection, provide styling advice, and even facilitate transactions. This can improve the customer service experience and increase sales.
   • Example: Tidio
4. Demand Forecasting: AI can predict future trends and consumer demand by analyzing historical sales data, social media feeds, and current fashion trends. This can help fashion retailers to manage inventory more efficiently and reduce waste.
   • Example: Merchbees
5. Price Optimization: AI can analyze numerous factors, including market demand, competitor pricing, and historical sales data, to set optimal prices for products. This can maximize profit while maintaining customer satisfaction.
   • Example: Intelis
6. Automated Marketing: AI algorithms can analyze customer behavior and use this information to develop targeted marketing campaigns. This can increase engagement and conversion rates.
   • Example: Klaviyo
7. Intelligent Search: AI can enhance user experience and conversion rates by providing an intelligent, predictive search bar and advanced filters for precise and personalized product discovery.
   • Example: Rapid Search
8. Visual Search: With AI, customers can upload images to search for similar items in an online store. This makes the shopping experience more convenient and intuitive.
   • Example: Simile
9. Multi-Language Translation: AI-powered machine translation can automatically translate your e-commerce site to reach potential customers that don’t speak your native language or are in other countries.
   • Example: LangShop
10. Fashion Manufacturing: With AI, fashion manufacturers can improve material grading, automate data-gathering and asset management, reduce errors in final product inspection, and enhance sustainability and customized manufacturing.
    • Example: Coats Digital
11. Fashion Design: AI can also be used to design new fashion items. By analyzing trends and consumer preferences, AI can generate design ideas that are likely to be popular.
    • Example: StarryAI

The application of AI in fashion e-commerce is vast and growing, and these are just some of the ways that AI is helping to improve the industry. As AI technology continues to evolve, we can expect to see even more innovative applications in the future.