AI Merchandising: How to Use AI Tools to Increase Sales

AI merchandising uses artificial intelligence tools to guide, execute, and optimize product placement throughout the customer experience in an online store. 

Natalie Westlake, SVP of marketing and operations at fine jewelry brand Bluboho, uses AI to discover what customers are looking for, what shifts are happening in the market, and understand emerging demand. “I’ll use it to check strategies. I’ll use it to invent brand ideas. I’ll use it to discover what’s coming for jewelry in 2026,” she says on Shopify Masters. 

Here’s what AI merchandising is, how it works, and three ways to apply it to your store today.

What is AI merchandising?

AI merchandising guides, executes, and optimizes product placement in an ecommerce store with the goal of improving customer satisfaction and maximizing sales. 

Traditional merchandising takes place in brick-and-mortar stores, through physical product placement and store layout designed to drive sales. In ecommerce merchandising, that work plays out across your product category pages, store search results, and product recommendations.

Some AI merchandising tools can fully automate the merchandising process, including product placement, visual merchandising, and inventory management, all based on customer preferences and patterns learned from AI data analysis. Your merchandising team sets the merchandising strategy—which products to promote, which audience segments to target—and AI analyzes sales data and customer data to personalize which products appear on a given page, the order shoppers see them in, and product recommendations.

How does AI merchandising work?

AI merchandising pulls from many data sources: sales data, customer behavior data (clicks, search queries, and time on page), purchase history, and external signals like seasonality and market trends. 

Machine learning analyzes those data points simultaneously, predicting what will lead a specific customer to make a purchase. The model might recognize that shoppers searching “outerwear for cold weather” usually buy mid-weight jackets rather than the heaviest ones, then populate mid-weight options at the top of search results. 

The same machine learning data analytics can also identify demand shifts—seasonal changes in what shoppers want, or a category gaining traction faster than the rest of your catalog. 

Every search, click, and purchase becomes new training data, and AI merchandising updates what each shopper sees in response. As traffic accumulates, a homepage might show running shoes to one shopper and dress shoes to another, based on what each has clicked and bought.

How to use AI in merchandising

• Get insights to anchor your merchandising strategy
• Improve store search and product recommendations
• Generate product copy and imagery at scale

Beyond AI merchandising in your ecommerce store, AI solutions can help you guide your broader strategy, and even create and improve the visual assets behind eye-catching product pages and recommendations. Here’s how you might use them:

Get insights to anchor your merchandising strategy 

Here are some of the questions that guide a digital merchandising strategy: Which products are trending or declining in sales? Which customer segments are growing? Which page placements convert and which don’t? Answering them once required learning SQL or working with an analyst to pull a complex series of reports. AI-powered merchandising tools answer your questions in plain language.

Sidekick is the AI commerce assistant built into Shopify with direct access to your store’s data—sales, inventory, customer segments, traffic, and products. You can ask questions the way you’d ask a data analyst. A query like “Which products had the biggest drop in conversion rates last month, and what do they have in common?” returns the numbers alongside a conversational summary.

Those answers can then inform decisions like which products to refresh with new imagery or which categories to retire from the homepage. A query like “Which products are frequently purchased together?”—the kind of cross-product pattern that shapes bundles and recommendation rows.

Improve store search and product recommendations

Store search results populate based on how you’ve tagged and titled products, while AI product recommendations reflect the patterns a merchandising tool has identified across your catalog and customer behavior.

Shopify’s Search & Discovery app adds a layer of controls for search, filters, and recommendations to Shopify’s built-in search engine. For search results, it offers semantic search (which interprets shopper intent), synonym groups (so different words return the same products), and product boosts (so specific items rank higher for chosen queries). For collection and search pages, it lets you create custom filters by attributes like price, color, or tag. 

On product detail pages, the Search & Discovery app generates AI-powered recommendations from purchase history, product descriptions, and how you’ve organized collections, with the option to manually set related and complementary products. 

Because the Search & Discovery app uses semantic search rather than exact keyword matches, it can interpret what shoppers mean, not just the words they type. A search like “shoes for a fall wedding” might surface dressy boots and loafers even when “wedding” or “fall” don’t appear in any product title. 

Recommendations work similarly—the AI can pair a sock with sneakers based on patterns from thousands of shopper sessions. Product descriptions that name materials, occasions, and use cases give the model more to draw on when it pairs items or interprets a search query. 

Generate product copy and imagery at scale

Generating product copy and images—including product descriptions, alt text, photography, email subject lines, and category copy across hundreds of SKUs—traditionally has been a manual task. Generative AI tools can take on some of that production work, leaving editing and brand voice decisions to your team.

That automation supports better coverage across your catalog. Every product, not just your bestsellers, can have rich copy and imagery to match—and copy can be tested and refreshed without rewriting it from scratch.

Some brands do this by feeding an AI model their strongest existing copy. Sean Frank, CEO of men’s wallet brand Ridge, built a copywriting bot using the AI assistant Claude. “You put all of your favorite copy you’ve ever written, all of your best performing emails,” he says on theShopify Masters podcast. “You go into Claude, make a custom GPT, make a project, and you can start getting all of your copy written by AI.”

Shopify Magic, the suite of generative AI features available across all Shopify plans, automatically connects to your product data and customer segments—no separate tool or data setup required, and your data isn’t shared with other stores. You can use it to write or rewrite product descriptions, edit product photos to swap or generate backgrounds, and generate alt text that helps your products surface in search.

Start by selecting a handful of your lowest-performing product pages, generate two or three different description tones with Shopify Magic, and compare customer engagement and conversion rates as the data comes in.