Dressipi

Dressipi offers personalization services tailored to the needs of apparel retailers, designed to show customers the products they're most likely to purchase and want to keep. The technology helps retailers increase product discovery and revenue by using detailed product tagging, intent-driven categories and personalized recommendations.

Dressipi solves problems common to apparel retailers, including inconsistent product data and attribution problems, with a taxonomy of attributes built by fashion stylists and then scaled by AI. That means retailers can offer data that's accurate and relevant, and that leads to higher revenue and efficiency across the business.

Among Dressipi's features are:

• Product Tagging: Ensures high-quality, accurate data that can be used across the business, leading to higher conversion rates and better product discovery.
• Personalized Recommendations: Offers intent-driven categories, filters and recommendations for first-time and repeat customers, leading to higher revenue and profit.
• Outfit Recommendations: Offers customers outfit ideas, showing the value of every product and how to wear it, and leading to higher customer loyalty and retention.
• Product Forecasting: Helps retailers optimize future product assortment and size ratios, reducing overstock and discounting and improving ESG credentials.

Dressipi can be easily integrated with a wide range of tools and software, so it's easy to get up and running and show a quick return on investment. The platform offers unlimited integrations and can be used across the customer journey, including online, in-store, in-app, email and future VR & AR experiences.

Several retailers have seen strong results from Dressipi, including John Lewis, which saw revenue increase with outfit recommendations, and Belstaff, which saw a 69% increase in email revenue through personalized recommendations.

Dressipi's focus on fashion-specific AI and expert stylists differentiates it from more general-purpose personalization technology, and means it can deliver highly accurate and relevant results for apparel retailers.

Published on July 29, 2024