AI generated styling strategies that are transforming fashion e-commerce

In the constantly growing world of fashion e-commerce, styling has become much more than a core visual element and has rather become a strategic tool for businesses to, boost both their conversion and engagement rates alike. As we are starting to see, fashion go beyond, different product categories, and are now a means of self-expression and individual identity. When it comes to the way that products are styled as well as presented, this has a direct impact on the extent to which purchasing decisions are influenced.

Follow along as this article explores, the key AI styling strategies, that e-commerce fashion brands use to take their digitally generated merchandising efforts to the next level and boost growth.

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The strategic role of styling in fashion e-commerce

When you compare the fashion industries, to any other industries, you understand that fashion tends to thrive on the foundations of inspiration as well as a customer's ability to visualise themselves in the products that they are interested in buying.

When one shops in a physical store, there are emotional connections that are a result of touching certain fabrics, pairing and trying on different styles, as well as the actual feel of an outfit. Online this becomes challenging, and has been attempted to be recreated in a visual manner, creating the whole narrative of styling crucial to a digital shopping journey.

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Why Styling Matters

• Emotional Connection: Styling helps customers project themselves into the products, fostering an emotional connection that drives purchasing decisions.
• Conversion Booster: Effective use of styling can result in a significant increase in conversion rates by helping shoppers visualise complete looks.
• Engagement Driver: Interactive and personalized styling experiences enhance shopper engagement, leading to longer site visits and higher average order values.

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The Challenge of Scaling Styling

It goes without saying that styling is scaling significantly across those larger and faster moving catalogues, though comes with significant challenges. When you look at traditional methods be it manual curation or photoshoots, often a time these are quite cost & resource intensive as well as can be difficult to maintain consistency. This is where AI-driven styling solutions come into play, offering scalability, personalization, and operational efficiency.

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Five AI styling strategies for fashion brands

1. Try complete the look banners to attract customers

Overview: "Complete the Look" banners suggest additional items to create a full outfit, typically displayed on product detail pages (PDPs) under sections like "Wear it With" or "How to Style It."

Implementation: Mostly manual, relying on merchandising teams to curate outfit pairings based on brand guidelines and seasonal trends. Some retailers use rule-based automation for basic pairings.

Pros:

• Increases basket size through cross-selling.
• Easy to implement for quick wins.

Cons:

• Limited personalization needs and visual engagement.
• Difficult to scale across large catalogues.

Real-Life Example: Anine Bing features a scrollable "Complete the Look" carousel on their PDPs, offering curated outfit suggestions using flat packshots.

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2. Enable packshot-based outfit suggestions

Overview: This strategy showcases fully styled outfits built from curated packshots & tools, going beyond simple product pairings to demonstrate the versatility of items.

Implementation: Requires e-merchandising teams to configure styling rules aligned with the brand's aesthetic and commercial strategy. Automation can assist in surfacing pairings, but human oversight is essential.

Pros:

• Enhances product discovery by showcasing versatility.
• More structured and visually engaging than basic pairings.

Cons:

• Resource-intensive to maintain.
• Limited personalization compared to AI-powered solutions.

Real-Life Example: Boden displays a carousel of complete outfit suggestions on their PDPs, presenting multiple styled variations for a single hero product.

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3. Packshot outfit builders for your business

Overview: Packshot outfit builders allow shoppers to create their own looks using drag-and-drop interfaces or modular tools built on product packshots.

Implementation: Relatively easy to deploy, requiring thoughtful UX, reliable product data, and careful curation to avoid overwhelming users.

Pros:

• Engaging and interactive for shoppers.
• Lightweight and scalable without the need for photoshoots.

Cons:

• Limited guidance and emotional impact compared to model-based visuals.

Real-Life Example: Uniqlo's Look Generator invites users to select pieces from a collection and create their own outfits visually, featuring flat-lay packshots and options to download or share looks.

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4. On-model outfit inspiration carousels on real models

Overview: This approach shows products worn in full, styled looks, providing shoppers with visual inspiration and helping them picture how items fit into an overall outfit.

Implementation: Requires defining relevant looks and producing or generating styled images, which can be costly and time-consuming. AI-generated imagery can help scale this process.

Pros:

• Enhances product discovery and purchase confidence.
• Supports cross-selling by surfacing complementary products.

Cons:

• High execution costs without AI assistance.

Real-Life Example: Noonspain features on-model outfit carousels on select PDPs, displaying items worn in several styled looks, ranging from professional photoshoots to AI-generated imagery.

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5. AI mix & match styling tools

Overview: AI Mix & Match styling allows shoppers to explore real-time, on-model outfits generated from the product catalog, with the ability to swap tops, bottoms, and layers to create personalized looks.

Implementation: Powered by proprietary AI, this solution generates millions of outfit combinations instantly, ensuring precise fit and proportions.

Pros:

• Highly scalable and personalized.
• Enhances engagement, product discovery, and purchase confidence.

Cons:

• Requires investment in AI technology and integration.

Real-Life Example: Eileen Fisher offers a fully interactive Mix & Match experience, allowing shoppers to explore millions of outfit combinations on models reflecting their body type.

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Final thoughts on the fashion industry

This allows for us to understand that professional outfit styling is not just a nice to have when it comes to fashion and e-commerce, instead it is a process & driver of performance. Whether it is simple banners or even AI powered styling, every strategy provides a unique trade-off when it comes to personalization as well as lasting impact.

If you are able to understand, these approaches, e-commerce brands have the ability to leverage informed decisions and transform their digital growth in a meaningful manner. Are you ready to get started with choosing your next strategy?

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