Discover how e-commerce fashion brands are leveraging AI to deliver greater e-commerce personalization, boost online sales, and drive operational efficiency. From personalized outfit recommendations to advanced site search and merchandising, this article outlines six powerful AI strategies that are reshaping the fashion industry and delivering long-term value to both companies and their customers.

1. Personalized Outfit Recommendations

What is it?

Personalized outfit recommendations are a type of visual merchandising software used by apparel brands and retailers to create and deliver outfit suggestions such as what pants, sweaters, and handbags pair well with a specific shirt, across their website, email, and social media channels.

How does it work?

Uses computer vision and deep learning techniques to learn what clothing and accessory items from a brand’s product catalog can be combined to form an outfit that meets the brand’s style guide, and to determine which outfits to show to different website visitors based on the specific item they are viewing, their individual preferences, and what products are in-stock.

Customer benefits:

• Engagement: Outfit recommendations are presented in a visually appealing manner, which can make the shopping experience more engaging and enjoyable.

• Product discovery: Seeing how different items can be worn together inspires shoppers to discover new styles and products, improving their creativity and personal expression through fashion.

• Personalized experience: Recommendations are personalized based on user data, such as previous purchases and browsing behavior, enhancing the shopping experience.

Brand benefits:

• Increased sales: Personalized outfit recommendations drive higher conversions and sales by effectively matching outfit suggestions with shoppers’ tastes and preferences.

• Higher order values: By displaying clothing and accessories that look well together, customers tend to buy more items per transaction, increasing the average order value.

• Efficiency: AI can help create product laydown images, generate stylist-quality outfit ideas, and automate manual merchandising tasks, boosting stylists’ and merchandisers’ efficiency and productivity.

Example

Eloquii is an example of an apparel brand that is using our outfit recommendation technology. In the example below, a series of outfit recommendations are shown below The Morgan Super Stretch Skinny Jeans under the banner “How to Style.”

2. Product Merchandising

What is it?

Product merchandising software is used by apparel brands and retailers to show the most relevant products to online shoppers when viewing a product collection or product listing page.

How does it work?

AI and machine learning models analyze both customer and product data to learn and predict which products are most likely to be popular with an individual or customer segment. Products on the product listing page are then dynamically sorted upon loading according to these rankings (or custom merchandising rules set up by the retailer) to maximize the likelihood and value of a purchase in that session.

Customer benefits:

• Product discovery: Personalizing the order products are shown in helps customers uncover products they may otherwise not have using basic default sorting techniques like “newest” or “featured.”

• Increased relevancy: Displaying products according to shoppers’ tastes and preferences leads to more relevant product views and makes browsing more efficient and satisfying.

Brand benefits:

• Increased sales: By showcasing the most relevant products in each category first, businesses see an increase in the likelihood of purchases from customers who find exactly what they need leading to more revenue per visitor.

• Streamline merchandising tasks: AI helps automatically update product listings based on current trends, stock availability, and demand, while also allowing for manual adjustments through custom business rules to promote or suppress select products.

Example

June + Vie is an example of an apparel brand that is using our product merchandising technology. In the example below, products on the collection page for tops are arranged by AI to better align with the shoppers’ preferences and maximize the likelihood and value of a purchase in that session.  

3. Site Search

What is it?

AI-driven site search helps e-commerce apparel brands and retailers improve their websites’ search engines by better understanding queries, retrieving relevant products, and ranking search results based on individual shopper preferences and their relevance to the search query.

How does it work?

Site search uses a combination of AI and machine learning techniques to function. Natural language processing (NLP) and semantic analysis are utilized to comprehend user queries and their context, enabling effective and accurate product retrieval. Machine learning and deep learning algorithms then help analyze and rank the results based on their relevance to the query and arrange the order of products in search results to maximize the likelihood and value of a purchase in that session.

Customer benefits:

• Product discovery: AI tailors search results to align closely with the customer’s preferences, helping them discover products that truly interest them.

• Increased relevancy: Customers find more relevant products quickly, reducing the time spent searching and improving the overall digital shopping experience.

• Easier browsing: Site search features like autocomplete, autosuggest, and dynamic filter and facet menus result in a smoother more intuitive browsing experience.

Brand benefits:

• Increased sales: More accurate and relevant search results lead to a higher likelihood of a shopper finding what they are looking for, fewer null search results, and increased sales.

• Higher customer satisfaction: Efficient website navigation and personalized search experiences improve customer satisfaction, lowering bounce rates, encouraging repeat visitors, and boosting loyalty.

Example

Catherine’s is an example of an apparel brand that is using our AI-driven site search technology. In the example below, a search for “black dress” surfaces several auto-suggested search terms and top product results.

4. Enhanced Product Attribution

What is it?

Enhanced product attribution, also known as auto-tagging, is an image recognition technology used by apparel brands and retailers to automatically generate product taxonomy, titles, descriptions, and features for products in their product catalog using just a product image. The same technology is also used to power the recommendation engine embedded in AI-driven services like outfit recommendations, merchandising, and site search.

How does it work?

Utilizing a combination of computer vision, NLP, and machine learning, enhanced product attribution involves scanning, analyzing, and interpreting product photos and information available from the manufacturer or brand to generate rich product data without human intervention. Companies can use the enriched product data generated by AI to feed their product information management systems, e-commerce platforms, and marketing channels.

Customer benefits:

• Product discovery: Incorporating detailed, accurate, and customer-centric attributes into product information makes products more discoverable to shoppers online.

• Enhanced shopping experience: With improved product titles, tags, and descriptions, customers can better understand products and their benefits to make informed purchasing decisions.

• Personalized experience: Powerful product attribution allows shoppers to enjoy a personalized digital shopping experience with outfit recommendations, merchandising, and site search.

Brand benefits:

• Streamlined catalog management: Automatic attribute creation helps brands create product information more efficiently, boosting employee efficiency and productivity.

• Accurate product data: AI-generated product attributes and descriptions help identify and eliminate data entry errors, making product data more accurate, clear, and consistent.

• Robust product data: AI can help generate a rich list of product features for each item, using structured and customer-centric language, as well as fill in missing data, allowing for improved filtering, sorting, and search capabilities.

• Marketing: Incorporating rich product data and words and phrases people use can improve a brand’s search engine marketing (SEM) and search engine optimization (SEO) performance.

Example

Here is an example of a floral summer dress that has been attributed using AI to identify the product category, style, length, and color, among other product details.

5. Fit and Sizing Recommendations

What is it?

AI-powered fit and sizing recommendation tools are used by e-commerce apparel brands and retailers to provide personalized sizing advice to shoppers, ensuring that they select the right size and fit for their body type.

How does it work?

Fit and sizing tools utilize machine learning algorithms, customer data, and product data to generate accurate size recommendations. The technology analyzes a shopper’s body measurements, purchase history, and feedback, as well as product dimensions and fit characteristics, to suggest the best size for each individual.

Customer benefits:

• Confidence in fit: Shoppers receive personalized size recommendations, which increases their confidence in purchasing an item and reduces the likelihood of returns due to fit issues.

• Enhanced shopping experience: By providing accurate sizing information, the shopping experience becomes more satisfying as customers need to return and exchange fewer items.

Brand benefits:

• Increased sales: By providing personalized fit recommendations, shoppers are more likely to complete their purchases, leading to higher conversion rates

• Reduced returns: Accurate size predictions lead to less size bracketing and fewer returns due to poor fit, improving profitability and reducing inventory on hand.

• Customer loyalty: Shoppers who consistently receive accurate fit recommendations are more likely to become repeat customers, boosting customer loyalty and lifetime value.

Example

True Fit is an example of a company that offers AI-powered fit and sizing recommendations. In the example below, a shopper is guided through a series of questions to determine their body measurements and preferences. The tool then provides a personalized size recommendation for a selected shirt.

6. Pricing Optimization

What is it?

AI-driven pricing optimization involves using advanced algorithms and machine learning to dynamically adjust product prices based on various factors such as demand, competition, customer behavior, and market conditions. This helps e-commerce apparel brands and retailers maximize their revenue and profitability.

How does it work?

Pricing optimization tools use AI and machine learning models to analyze vast amounts of data, including historical sales data, competitor pricing, market trends, and customer behavior. These models then predict the optimal price point for each product in real-time, considering factors like seasonality, inventory levels, and consumer demand elasticity. The goal is to set prices that maximize sales and profit margins while remaining competitive in the market.

Customer benefits:

• Fair pricing: Customers benefit from competitive pricing that reflects current market conditions and demand.

• Better deals: Shoppers are more likely to find products at their ideal price points, leading to higher satisfaction and perceived value.

Brand benefits:

• Increased sales: prices based on real-time data and predictive analytics can lead to higher sales and profitability.

• Improved competitiveness: Dynamic pricing strategies help brands stay competitive in a fluctuating market by adjusting prices in response to competitors' changes.

• Better inventory management: By setting optimal prices, brands can better manage their inventory levels, reducing excess stock and stockouts.

Example

Nike is an example of an apparel brand that uses AI-driven pricing optimization. Nike’s online store often displays different prices for the same shoe model based on factors like color and size, which suggests the use of AI to dynamically adjust pricing. This approach helps Nike balance supply and demand while maximizing revenue from their diverse product offerings.

About

SabinoDB is a leading e-commerce and marketing technology company committed to helping companies grow their business by making data and technology their competitive advantage. Our solutions include Digital Advertising, E-Commerce Personalization, ‘Shop The Look’ Outfit Recommendations and Product Recommendations, Direct Mail and Catalog Response Models, and Web Development. If you are interested in learning about how SabinoDB can help your company, please contact Ryan Hammon at ryan.hammon@sabinodb.com.