Building an AI shopping assistant: feature priorities for conversion

Fashion retailers investing in AI shopping assistants should prioritise natural language search and size advice features first, followed by visual search and outfit recommendations depending on category mix. Conversion impact varies widely by feature, with natural language search and fit tools showing the most consistent results across pilots, while visual search and styling features require careful implementation to deliver returns.

Which AI assistant feature delivers the highest conversion lift?

Natural language search consistently delivers the strongest conversion improvements, with retailers reporting lifts of 15 to 30 per cent when shoppers use conversational queries instead of traditional filters. The feature allows customers to search using everyday language rather than navigating category trees or applying multiple filters.

Klarna reported in early 2025 that its AI assistant handled more than 2.3 million conversations in its first month, demonstrating significant shopper appetite for conversational interfaces. The payment provider's tool helped users with product discovery alongside payment queries, suggesting that natural language interfaces reduce friction across the shopping journey.

Implementation priorities for natural language search include training models on product catalogues with varied terminology, handling synonyms and colloquial terms, and ensuring the assistant can interpret complex queries that combine multiple attributes such as occasion, style and price range.

How much do size and fit tools improve conversion?

Size recommendation tools can reduce return rates by 25 per cent according to retail technology studies, with some implementations achieving return rate reductions of up to 36 per cent. Lower return rates translate directly to improved unit economics, making fit advice one of the highest-value AI features for apparel retailers.

Size advice tools work by collecting customer measurements or using purchase history to recommend appropriate sizes across brands with different sizing standards. The feature addresses a primary barrier to online apparel purchase: uncertainty about fit. Retailers report that customers who engage with size advice tools show higher purchase completion rates and lower return rates than those who do not.

Best practice implementations combine multiple data sources including customer measurements, previous purchases, and product specifications. The assistant should explain sizing recommendations clearly and offer alternative sizes when measurements fall between standard sizes.

Does visual search increase conversion rates?

Visual search adoption has grown as smartphone cameras improve, with retailers reporting that visual search queries convert at higher rates than text searches in categories like home goods and furniture. The technology allows shoppers to photograph items they like and find similar products in the retailer's catalogue.

However, visual search shows uneven results across categories. It performs best when style matching is the primary purchase driver, such as finding a dress similar to one seen on social media or locating furniture that matches existing pieces. In categories where technical specifications matter more than aesthetics, such as athletic wear or outdoor equipment, visual search delivers less value.

Implementation costs for visual search are higher than for natural language search because the technology requires image recognition models trained on fashion imagery. Retailers should test visual search in specific high-value categories before rolling out across the entire catalogue.

What results do outfit recommendation features achieve?

Outfit recommendation and styling features show the most variable results among AI assistant capabilities. Success depends heavily on product assortment, merchandising strategy and whether the retailer sells complete looks or focuses on individual items. Retailers with strong editorial content and curated collections report higher engagement with AI styling tools than those with broad, undifferentiated catalogues.

The feature works by suggesting complementary items based on the product being viewed or items already in the basket. When executed well, outfit recommendations increase average order values by encouraging multi-item purchases. Poor implementations frustrate customers with irrelevant suggestions that ignore season, occasion or price point.

Retailers should invest in outfit recommendations only after establishing strong product attribution and merchandising rules. The assistant needs detailed product data including colour, style, occasion and season to make relevant suggestions. Without this foundation, recommendation quality suffers and the feature adds little value.

How should retailers prioritise AI assistant features?

Feature prioritisation depends on the retailer's product mix, customer pain points and existing technology infrastructure. A decision framework based on conversion impact and implementation complexity helps allocate development resources effectively.

Tier one features deliver measurable conversion improvements across most implementations and should be prioritised for initial deployment. Tier two features require more careful consideration of category fit and merchandising readiness before investment.

Retailers should establish clear success metrics before launching AI assistant features. Key performance indicators include conversion rate for users who engage with the feature, average order value, return rates, and customer satisfaction scores. Pilots should run for at least eight weeks to capture sufficient data across different shopping occasions and seasonal patterns.

What infrastructure supports AI assistant features?

All AI assistant features require clean, comprehensive product data as a foundation. Product catalogues must include detailed attributes, accurate imagery, and consistent taxonomy. Many retailers discover data quality issues only after beginning AI projects, leading to delays and disappointing results.

Natural language search needs product descriptions with varied terminology and synonyms. Size advice requires detailed measurement specifications for each product. Visual search depends on high-quality product photography from multiple angles. Outfit recommendations need products tagged with complementary attributes like style, occasion and colour family.

Beyond product data, retailers need integration with existing e-commerce platforms, inventory systems and customer data. The assistant must access real-time inventory to avoid recommending out-of-stock items and should personalise suggestions based on customer preferences and purchase history where privacy regulations permit.