AI-Driven Best Practices for Digital Merchandising Success

“Digital merchandising isn’t new — people have been doing it as long as there’s been ecommerce,” says Anthony Delage, Product Manager at Coveo. 

But the game has changed. Technology, especially AI, is redefining what merchandisers can do — and how quickly they can do it.

AI is transforming how merchandisers think and act. “AI helps with macro tasks like ranking, personalization, and semantics,” Delage explains. But the real power lies in how merchandisers can layer their own strategies on top. It’s about balancing automation with control — knowing when to let AI drive and when to take the wheel. In this guide, we’ll explore best practices for using AI to optimize digital merchandising. From data-driven insights to dynamic product ranking and personalized experiences, these strategies will help you use AI without sacrificing the human touch that makes your brand unique.

Leverage Your Data

Merchandising has always been about understanding customers — but without real-time data, it’s like trying to read a map in the dark. AI flips the lights on. It cuts through the noise, surfacing insights digital merchandisers wouldn’t spot otherwise. As Delage explains,

What AI does really well is the macro elements — ranking, personalization, semantics. But merchandisers aren’t just looking for automation. They want tools that give them control, so they can layer their own strategies on top of the data.

AI isn’t just assisting merchandisers — it’s handling the heavy lifting entirely, ensuring every product, placement, and promotion adapts dynamically in real time. No waiting. No manual intervention. Just continuous optimization. Here’s how:

• Identifying trends: AI constantly analyzes customer behavior to detect what’s catching on. Notice a spike in searches for eco-friendly sneakers? AI dynamically increases their visibility — ensuring customers see them at the right moment, without any manual intervention.
• Spotting underperforming products: Instead of waiting for end-of-quarter reports, AI flags products that aren’t pulling their weight in real time. Whether it’s time to discount, reposition, or replace, merchandisers can act before slow movers become dead stock.
• Highlighting cross-sell and upsell opportunities: AI reads browsing patterns to recommend complementary products. Think of a customer shopping for hiking boots — AI might suggest trekking poles, moisture-wicking socks, or a waterproof jacket based on the buying habits of similar shoppers.

Take Bass Pro for example. When a customer adds a specific “adventure” tent to their cart, AI also recommends related essentials like sleeping pads or water filtration accessories. “AI isn’t just there to serve up recommendations,” Delage explains. “It learns from every interaction, getting better over time at predicting what customers actually want.”

If AI sees that customers keep adding sleeping pads but skip the water purifier, it’ll adjust future recommendations, for something more relevant.

Optimize Search and Navigation

Search is the backbone of the customer journey. If shoppers can’t find what they’re looking for fast, they’ll bounce. And in ecommerce, a bounce often means gone for good. AI changes that by turning search from a basic keyword match into a sophisticated intent-detection engine. It’s not about showing more results — it’s about showing the right ones.

Search is the most important piece, it’s where AI really shines because you’re not just ranking products — you’re figuring out what people actually mean when they type in that search bar.

Here’s how AI transforms search from frustrating to frictionless:

• Predictive search: AI anticipates what shoppers are looking for as they type, offering real-time suggestions that speed up the journey. In essence, it’s predicting intent based on past behavior, popular queries, and context.
• Semantic search: Shoppers don’t always use the exact terms in your product catalog. AI bridges that gap, placing similar words by meaning closer together.  It understands that “running shoes,” “sneakers,” and “trainers” are the same thing — so customers don’t hit dead ends because of simple wording differences.
• Relevance tuning: AI prioritizes relevant search results. By analyzing customer behavior, click-through rates, and product popularity, it fine-tunes search rankings to surface the most relevant products first.

Take a hardware retailer like Bunnings. A customer searching for “screws” could be looking for drywall screws, decking screws, or framing screws. AI uses browsing history to make that distinction. “If you’ve been looking at decking lumber, we know you’re probably after decking screws,” Delage explains. That’s the difference between a customer finding exactly what they need — or abandoning their cart in frustration.

AI also boosts site navigation. Traditional product listings are static, showing the same categories to every visitor. AI makes these pages dynamic, adjusting product placements based on user behavior and preferences.

For example, if a customer frequently browses outdoor furniture, AI will prioritize related categories like garden tools or patio accessories. The site evolves with the shopper, creating a personalized experience that feels intuitive without them even realizing it.

As a result customers find what they’re looking for faster, spend more time on-site, and are more likely to convert — all because AI is quietly guiding them in the right direction.

Implement Dynamic Product Ranking

Static product lists are a thing of the past. Shoppers expect ecommerce sites to feel intuitive, like the products they’re most interested in are right there, waiting for them. That’s where dynamic product ranking comes in. AI adapts rankings in real time, based on what matters most to each customer.

We’re always dynamically ranking — whether it’s search, listings, or recommendations. But the magic happens when AI can pick up on subtle signals and adjust the rankings to match what the customer actually wants.

Here’s how AI fine-tunes product rankings to improve discovery and conversions:

• Inventory management: AI-powered merchandising rules can adjust product visibility based on stock levels and strategy. Size-broken products (where XS and XL are available but S-M-L are not, for example) can be “buried” on category pages but remain prominent in clearance sales to maximize sell-through. 
• Customer intent: AI analyzes browsing behavior, past purchases, and engagement to surface products that align with a shopper’s unique preferences. 
• Click-through rates and engagement: AI continuously learns from what shoppers interact with most, pushing trending or high-engagement products to the top. This isn’t a one-time adjustment — it’s a constant evolution based on real-time behavior.

Consider a fashion retailer like River Island. They don’t just display new arrivals — they highlight what’s trending, what’s in high demand, and what fits seasonal preferences. AI makes this possible by dynamically ranking products in a way that feels personalized to each shopper. “The bigger your catalog, the more important this becomes,” says Delage. “With thousands of products, you can’t rely on static sorting. Dynamic ranking helps customers cut through the noise and find exactly what they need —without even realizing the system is working behind the scenes.”

Embrace Personalization at Scale

Customers don’t just want options — they expect the right options, served up at the right time, within their budget. They want to feel like the store knows them, without having to dig through endless pages. But when you’re managing thousands of SKUs, delivering that kind of tailored experience manually? Impossible. That’s where AI steps in.

AI can transform customer data into experiences that feel effortless and intuitive for every shopper.

Personalization is really useful when you have these broad catalogs. If we can rank the right products a little higher based on what a customer’s been looking at, it makes a huge difference.

Here’s how AI scales personalization without overwhelming your team:

• Personalized home page: AI curates landing pages based on browsing history, past purchases, and even location data. A returning customer might see a homepage filled with products they’ve previously viewed or similar items, while a new visitor gets bestsellers or trending products. “It’s about making sure the first thing customers see is actually relevant to them,” Delage explains.
• Dynamic content adaptation: retailers are moving toward AI that personalizes every touchpoint, from homepage banners to tailored email campaigns. AI can highlight relevant products, suggest complementary items, or trigger targeted discounts based on browsing behavior — keeping the shopping journey fluid and engaging.

For example, imagine a customer browsing winter coats last week. When they return, along with more coats — AI might surface matching scarves, gloves, or even suggest seasonal promotions on outerwear. “It’s not about bombarding customers with products,” Delage notes. “It’s about using their behavior to guide them to what they actually need, before they even realize it.”

Test, Learn, and Iterate

In digital merchandising, what works today might flop tomorrow. Merchandising trends shift, customer behaviors evolve, and keeping up with these changes manually can feel like a never-ending game of catch-up. That’s where AI steps in — it helps you anticipate changes before there’s any noise. 

A lot of what we’re doing now is about making merchandisers comfortable working with AI. We are giving them the tools to see what’s working and adjust in real time.

A/B testing remains a human-driven process — merchandisers set up experiments, whether it’s testing intuition against AI or pitting two merchandising strategies head-to-head. But the goal isn’t just to optimize — it’s to build trust in AI so teams can shift their focus from tactical tweaks to strategic growth. Here’s how AI keeps your merchandising strategies agile and effective:

• Automated A/B testing: AI runs multiple tests simultaneously, comparing different strategies to figure out what drives the best results. No more waiting weeks for data — AI delivers insights fast.
• Helpful recommendations: AI reviews test results and offers clear, actionable suggestions. “Using AI to interpret A/B tests is extremely efficient,” Delage explains. “A properly tuned model probably knows more about A/B testing than the average user.” 

Maximizing performance, within limits: A/B testing doesn’t improve endlessly, there’s a point where optimizations level off. But as external factors shift (like changing shopper preferences or supply chain disruptions), AI ensures you’re always adjusting against the right baseline. For example, say you’re running an online clothing store. AI can test different homepage layouts — one featuring new arrivals, another highlighting sales. It quickly identifies which version gets more clicks and suggests the better option. “It’s about raising the floor,” Delage notes. “Even if you’re not an expert in optimization, AI makes sure you’re always improving.”

Overcome Common Challenges

One of the biggest concerns merchandisers have with AI is losing control. Many worry that adopting AI means handing decision-making power to IT teams or developers. But AI doesn’t have to be a black box.

At Coveo, we’ve built AI tools designed for merchandisers — not just engineers. With intuitive dashboards, merchandisers can adjust rankings, personalize recommendations, and fine-tune search results — no coding required. Instead of waiting on developers, they stay in control of their strategy.

Scaling without losing relevance
As product catalogs grow, maintaining a personalized shopping experience can feel overwhelming. AI solves this by continuously learning from customer behavior, ensuring that even as your inventory expands, the experience stays relevant and engaging.

Take Freedom Furniture, for example. Their merchandisers use AI-driven interfaces to adjust product placements based on real-time data. This reduces manual workload and frees them up to focus on strategy, creativity, and innovation — while AI handles the execution.

By addressing these challenges, AI gives merchandisers the tools to drive smarter, more impactful decisions.

Measuring Success: AI is About Outcomes

If you can’t measure it, you can’t optimize it. AI in digital merchandising helps you make sure every decision translates into impact. The real question isn’t whether AI is working; it’s whether it’s driving better results.

Here’s what actually matters:

• Conversion rates: Are visitors turning into buyers, or are they bouncing?
• Average order value (AOV): Is AI helping nudge shoppers toward higher-value purchases?
• Search success rates: Are customers finding what they need without frustration?
• Revenue impact: Is AI-driven merchandising increasing overall sales and profitability?

Use AI as an interpreter, not a collector. Maybe a high-intent search query isn’t delivering the right products, or a top category is seeing unexpected drop-offs. With AI, you don’t have to sift through raw numbers to figure that out — it flags the issue, so you can fix it.

The Real Power Move? Using AI to Play Offense, not Defense

Too many brands treat AI like a safety net — something that stops bad things from happening, like out-of-stock items ranking high or irrelevant products clogging up search results. That’s fine, but it’s the bare minimum. The smartest merchandisers use AI to play offense: predicting trends, personalizing experiences at scale, and turning a massive catalog into a curated journey that feels effortless for customers.

If your AI strategy is just about cleaning up messes, you’re doing it wrong. The real win is using AI to create experiences so seamless and intuitive that customers stay, convert, and come back.