Why AI-Driven Category Page Personalization Is a Must for eCommerce

McKinsey reports that effective personalization drives 40% more revenue, showing the tangible impact it can have on engagement and conversions. Shoppers often get frustrated scrolling through irrelevant items, and static pages fail to guide them efficiently. By adapting product grids, filters, and collections to each visitor’s behavior, brands can make browsing smoother and more rewarding.

From fashion to home goods, smart category personalization helps customers find what they want faster and encourages them to complete purchases. In this blog, we’ll explore how AI can enhance category pages, the challenges you might face, and practical solutions for your business.

» Find out how eCommerce personalization helps your business

Why Static Category Pages Fall Short

Static or generic category pages treat every shopper the same, showing identical product grids regardless of browsing behavior or preferences. This lack of relevance leads to frustration, wasted time, and higher bounce rates.

Remember: Category pages are the shop window of eCommerce. They help shoppers move from a general interest to specific products, bridging the gap between browsing and buying. When structured well, they make navigation effortless and support smoother product discovery.

Core Functions of an Optimized Category Page

• Organize products clearly: Create logical groupings that help customers quickly find what they’re looking for.

• Provide filtering options: Allow filtering by attributes like size, price, or color to refine searches.

• Guide product discovery: Highlight relevant items, bestsellers, or seasonal picks to capture interest.

• Encourage conversions: Reduce friction in navigation, keeping users engaged and moving toward checkout.

» Learn how AI-powered personalization can drive higher conversions and AOV

Why Category Page Personalization Gets Overlooked

Many brands focus their personalization efforts on homepages or product recommendations, assuming category pages are just functional product lists. But this is a missed opportunity.

Category pages play a key role in the decision-making stage, where shoppers actively compare options and narrow choices. Ignoring personalization here means losing relevance right when intent is highest.

Common Reasons Behind the Oversight

• High-intent, low recognition: Category pages often receive significant traffic from search, but brands don’t always treat them as a conversion-focused touchpoint.

• Misaligned priorities: Personalization is usually applied to drive discovery (homepages) rather than influence decisions (category pages).

• Static approach to design: Many businesses still view category pages as fixed grids instead of adaptable layouts that respond to user data.

» Confused? Here are the differences between personalization and customization

The AI Solution: Dynamic, Intent-Aware Category Pages

AI-driven personalization transforms static category pages into intent-aware shopping experiences. Instead of showing the same grid to every visitor, AI uses real-time data to predict what each shopper is most likely to engage with, shortening the path from discovery to purchase.

What AI Can Personalize on Category Pages

AI can dynamically adapt nearly every element of a category page to reflect user intent:

• Product rankings: Reorders items based on behavior, preferences, or trending demand.

• Banners and callouts: Adjust by region, weather, or season (e.g., surfacing “Winter Essentials” during colder months).

• Filters and sort options: Automatically surface the most-used attributes like “vegan leather” or “under $50.”

• Dynamic collections: Create temporary groupings like “Back-to-school picks” or “Sustainable bestsellers” in response to live demand signals.

» Here's how to improve the customer experience with AI

Behavioral Data: The Key to Smarter Category Personalization

Behavioral data offers valuable insights into how shoppers browse, filter, and engage—helping transform static catalogs into responsive, intent-driven pages.

Key behavioral signals to track

• Filters applied: Reveal product preferences or price sensitivity (e.g., “under $50” or “size 8”).

• Dwell time: Indicates which products or sections hold attention, even without purchases.

• Search patterns: Expose niche interests like “vegan leather” or “eco-friendly materials.”

» Here are the best ways to optimize eCommerce search filters

Key Technologies Behind AI Category Personalization

AI personalization combines several machine learning models to understand shopper intent and optimize product presentation:

• Collaborative filtering: Analyzes similarities between users and their past behavior to recommend products preferred by people with similar interests.

• Deep learning: Uses neural networks to interpret large datasets (e.g., product images, text, click behavior), predicting what each shopper wants to see.

• Reinforcement learning: Continuously tests different product arrangements and learns which combinations lead to higher engagement and conversions.

» Learn how to create relevant product recommendations for your eCommerce store

Brands That Benefit Most

AI-based category personalization is most valuable when shoppers face complex or large product selections or when intent varies widely:

• Large catalogs: Fashion, electronics, and furniture stores use AI to surface relevant items from thousands of SKUs.

• Multi-brand retailers: AI identifies shopper preferences across multiple labels, showing the most appealing products first.

• B2B platforms: AI simplifies procurement by prioritizing frequently ordered or contract-specific items.

» Here are the benefits of using AI in eCommerce personalization

4 Real-World Examples of Category Page Personalization

1. Hillberg & Berk (Jewelry Retailer)

Hillberg & Berk uses Fast Simon’s AI-powered merchandising and audience segmentation to adjust which products appear first within its category grids. Through A/B testing, the brand fine-tunes which items are promoted to specific audience segments — for example, showing more birthstone pieces to returning gift shoppers. Why they did it: Category pages generated most of the discovery traffic, but shoppers often abandoned them when presented with too many similar options. Personalization helped simplify decision-making and highlight items that felt immediately relevant.

Result: Noticeable gains in search conversion and overall revenue, with higher engagement from returning visitors who now see curated product assortments within categories.



» Here are 6 modern strategies to increase conversion rates beyond A/B testing

2. Ally Fashion (Apparel)

Ally Fashion uses Fast Simon’s AI collection merchandising and advanced filters to dynamically reorder products within each category based on browsing behavior and seasonality. The integration with Tapcart ensures these personalized layouts carry over seamlessly to the mobile app experience. Why they did it: Shoppers browsing from mobile devices were scrolling through long product lists with low add-to-cart rates. Personalized collections prioritize trending and style-relevant items for each user.

Result: Higher click-through rates on category pages and stronger mobile engagement across key apparel categories.



» Learn more about personalization in online shopping and why it matters

3. Francesca’s (Fashion & Accessories)

Francesca’s applies Fast Simon’s multimodal search and customizable filters to fine-tune its category pages, allowing shoppers to see more relevant items based on color, occasion, and past interactions. Why they did it: The brand’s broad catalog made it difficult for visitors to find pieces that matched their personal aesthetic. Dynamic filters simplified product discovery within each collection.

Result: Increased time on category pages and a higher likelihood of progressing to product pages.



» Not sure what to customize? Here are the elements you should personalize in your eCommerce store

4. Revolve (Fashion Retailer)

Revolve leverages AI to personalize category grids based on each shopper’s browsing and purchase history. Instead of showing the same “Best Sellers” or “New Arrivals” to everyone, the platform highlights styles aligned with individual preferences, such as favored fits, designers, or color palettes. Why they did it: Many shoppers arrive with broad, high-aspiration goals — for example, finding “that one-in-a-million dress.” Static category pages made discovery time-consuming and overwhelming. Personalization helps shoppers quickly surface relevant items and reduces decision fatigue.

Result: 18% increase in add-to-cart rate, higher engagement on category pages, and faster discovery of desired products.



» Read more: How to create a personalized brand experience to drive CLV

Inspired by These Brands?

Fast Simon’s AI personalization tool helps you create dynamic, high-performing category pages that keep shoppers engaged.

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Limitations and Solutions of AI-Driven Category Personalization

» Growing your store? Steer clear of eCommerce errors that damage brand loyalty

Personalize Your Category Pages

Fast Simon’s AI-powered personalization can transform your category pages into smart, tailored shopping experiences. Its advanced machine learning shows each shopper relevant products, highlights trending items, and optimizes filters in real time — even before the customer starts searching. This helps your visitors find what they want seamlessly, keeps them engaged, and increases add-to-cart actions.

By understanding your customers’ behavior and motivations, Fast Simon ensures each shopper feels valued, boosting loyalty and satisfaction. If you want your business to convert more visitors into buyers and make category pages work harder, Fast Simon’s AI personalization can make it happen.

» Ready to get started? Get a demo of Fast Simon or consider these other AI solutions for eCommerce