Company: Stitch Fix, an online personal styling service that delivers curated boxes of clothing and accessories to customers.

Challenge: Stitch Fix wanted to provide a personalized and convenient shopping experience for its customers, who have diverse tastes, preferences, and needs. The company also wanted to optimize its inventory management and reduce its return rates, which are common challenges in the online fashion industry.

Solution:

Stitch Fix used AI to create a hybrid human-machine system that leverages both data and human expertise to deliver customized and relevant recommendations to its customers. The company used two types of AI models: natural language processing (NLP) and computer vision.

• NLP: Stitch Fix used NLP to analyze the feedback and preferences of its customers, who fill out a style profile and provide ratings and comments on the items they receive. The NLP model was trained on a large corpus of fashion-related text, and it could extract insights and sentiments from the customer data, such as their style, fit, color, and occasion preferences. The NLP model also used natural language generation (NLG) to create personalized notes for each customer, explaining why the stylist chose the items for them.
• Computer vision: Stitch Fix used computer vision to analyze the images of its inventory, which consists of thousands of items from hundreds of brands. The computer vision model was trained on a large dataset of fashion images, and it could recognize and classify the attributes and features of each item, such as the style, cut, pattern, and texture. The computer vision model also used image similarity to find items that match or complement each other, creating cohesive outfits.

Outcome:

The AI-enhanced personal styling service helped Stitch Fix improve its customer satisfaction and retention, as well as its business performance. Some of the benefits were:

• Increased customer loyalty: The AI-enabled recommendations provided customers with a more personalized and relevant shopping experience, making them feel valued and understood. The AI system also used data analytics to predict customer churn and retention, and to offer incentives and promotions to retain loyal customers.
• Reduced return rates: The AI-enabled recommendations improved the accuracy and quality of the items sent to customers, reducing the instances of mismatched or unwanted items. The AI system also used data analytics to optimize the pricing and discounting of the items, increasing the likelihood of purchase.
• Enhanced inventory management: The AI-enabled recommendations helped Stitch Fix optimize its inventory management, reducing instances of overstock or stockouts. The AI system also used data analytics to forecast demand and supply for different items, categories, and seasons, enabling Stitch Fix to plan and purchase its inventory accordingly.

Conclusion:

This case study shows how an online personal styling service, like Stitch Fix, used AI to create a hybrid human-machine system that delivers customized and relevant recommendations to its customers. By using AI, Stitch Fix was able to provide a personalized and convenient shopping experience for its customers, who have diverse tastes, preferences, and needs. Stitch Fix also optimized its inventory management and reduced its return rates by using AI to analyze and match its inventory with its customer data.

If you want to learn more about AI in retail, you can check out these articles:

• Generative AI in Retail: Use Cases, Examples & Benefits in 2024
• Indian retail brands using AI: 5 case studies
• Top 10 AI use cases in retail | Google Cloud Blog