Fashion brands operate in a highly competitive environment, and need to stay on top of the latest trends and technologies (in addition to their cutting-edge designs) to maintain their customers’ interest. A recent article in Business of Fashion highlighted the impact that AI, and specifically ChatGPT, highlighted the industry’s expectations for AI’s potential to improve chatbots, hyper-personalized marketing copy and new ways for product discovery.
We had already begun internally discussing the ways we could integrate this AI engine into our platform to find ways that fashion brands can save time, improve their operations, iterate more quickly and enhance the customer experience with artificial intelligence (AI), and specifically with the integration of OpenAI’s API for ChatGPT.
Full disclosure — we used ChatGPT to write portions of this blog post, giving it a prompt containing all of our ideas and then editing its response to add more detail. Like we said…it’s a great tool to help you save time and iterate more quickly!
Companies of all types have been looking for ways to incorporate ChatGPT’s ability to generate responses to queries into their operations. Fashion brands are no exception. In the fashion world, the API can automate repetitive tasks, such as writing product descriptions, data-mining product reviews, delivering personalized recommendations and creating microtargeted ads. This can save fashion brands time and reduce costs while also delivering high-quality customer service and a large amount of relevant product content that attracts more customers.
Advertisement
One of the ways that fashion brands can use ChatGPT is to automate product descriptions based on product metadata. Product descriptions can be repetitive yet time-consuming to write and require a significant amount of effort from the brand’s team to make them creative and informative, yet also accurate and consistent. By using ChatGPT, product descriptions can be generated quickly and efficiently, based on product data such as images, metadata specifications, and even on customer reviews.
For example, ChatGPT might generate a customer-facing product description for your website based on the product metadata like item type, color, material and sourcing region, then an additional product description available to your in-store associates that includes the website description plus additional notes like “many reviewers are wearing this as resort wear.” This not only saves time and effort but also ensures that product descriptions are consistent, on-brand and up-to-date, providing customers and your in-store sales associates with accurate and relevant information.
Another creative way brands can incorporate ChatGPT is to data-mine product reviews to uncover common themes and particularly to make early identification of products with issues such as size and fit. This information can be used to make improvements to the brand’s future offerings and customer experience. For example, if a large number of customers are complaining about the size and fit of a particular product, the brand can use this information to adjust its sizing chart or provide better sizing advice to customers for that specific item.
Additionally, mining these reviews can identify issues with the material or cut of a product that might suggest the item should be marked down earlier, reducing the risk of holding a poorly-fitting item in the store’s inventory for too long. This review feedback can also be correlated with returns data for specific products to confirm issues and make decisions like not re-ordering a specific fabric that customers found uncomfortable or to not wear well.
Personalized recommendations are a powerful way to improve customer engagement and build customer loyalty, and brands also can use ChatGPT to deliver more powerful personalized recommendations based on a customer’s order or browsing history. By analyzing customer data, it can recommend products that are most likely to interest the customer, based on their preferences and/or past purchases. This not only helps the brand to increase customer engagement but also demonstrates that the brand understands the customer’s needs and preferences, building trust and loyalty over time.
Marketers and advertisers at these brands can also leverage ChatGPT. Microtargeted ads can easily be created with ChatGPT, which can analyze customer data to identify the most effective messaging, images and targeting strategies for different customer segments. Using these microtargeted ads can result in higher conversion rates and more effective marketing campaigns, reaching the right customers with the right message.
The API could also be used to power a customer service chatbot, answering customers’ frequently asked questions and product questions or handling common processes like orders and returns. This frees up your customer service reps to focus on more complex tasks and puts more information into the customer’s hands faster.
The integration of OpenAI’s API for ChatGPT offers fashion brands a powerful solution to automate repetitive tasks, improve the customer experience and enhance their offerings. By leveraging the power of AI, fashion brands can save time and reduce costs by making data-driven decisions faster while delivering a higher-quality customer experience. Additionally, by using the API to analyze customer data, fashion brands can optimize their offerings, inventory, and marketing and messaging strategies, resulting in improved customer engagement and loyalty.
Alyx Kaczuwka is Director, Customer Experience and Engagement at XY Retail. Her primary focus is helping luxury retailers drive growth by combining data-driven approaches with practical product strategy. She utilizes her knowledge of marketing and sales to develop strategic roadmaps to ensure XY’s platform will drive customers’ sales and enhance their customer experience. Kaczuwka is enthusiastic about trend forecasting, keeping a close eye on emerging market trends to help luxury retailers stay ahead of the curve and maintain a competitive edge. Additionally, Kaczuwka focuses on ethical implementations of AI and ML, as well as CX measurement and optimization.
