.jpg&w=3840&q=75)
You installed a recommendations app. Maybe you even paid for one. The widgets are live, products are showing up, and technically everything is "working."
But your click-through rate is sitting under 2%. Revenue attributed to recommendations is a rounding error. And you're starting to wonder if product recommendations are one of those ecommerce tactics that sounds good in blog posts but doesn't actually move the needle.
They do. Recommendation engines drive 10-30% of revenue for stores that implement them correctly. The gap between that and what you're seeing isn't a concept problem. It's an execution problem.
Here are the six most common reasons Shopify product recommendations underperform, and what to do about each one.
1. Your recommendations are buried below the fold
This is the most common and most fixable mistake. Merchants install recommendation widgets on the product page, scroll down to confirm they're showing up, and move on. The problem: those widgets are sitting below three paragraphs of product description, a size chart, a shipping FAQ accordion, and two sections of customer reviews.
Most visitors never scroll that far. They look at the product, decide yes or no, and either add to cart or bounce. If your "You may also like" section requires 4-5 scrolls to reach, it's invisible to 70-80% of your visitors.
Where recommendations should live instead:
Place them directly below the product image and add-to-cart button, before the extended description and review sections. On mobile especially, that first screenful is where buying decisions happen, and most recommendation widgets get pushed to the bottom of a very long page.
The homepage is another missed spot. Your homepage gets more traffic than any other page, but most stores use it as a static billboard. A "Trending now" or "Picked for you" section above the fold on your homepage creates immediate engagement and gives visitors a reason to click deeper into your catalog.
Test this for yourself: open your store on your phone. Count how many thumb-scrolls it takes to see your first recommendation widget. If it's more than two, you have a placement problem.
2. You're showing "popular products" instead of personalized ones
The default recommendation logic for most Shopify apps is some version of "show your bestsellers everywhere." It's the easiest algorithm to build, and it looks reasonable at first glance because the products it surfaces are, by definition, things people buy.
But it fails in a specific and measurable way. A returning customer who bought running shoes last week comes back and sees the same bestseller list as a first-time visitor browsing winter jackets. A customer who's bought three times from your premium collection gets recommended your entry-level products because those have the highest total sales volume.
Generic recommendations feel irrelevant because they are. And irrelevant recommendations train your customers to ignore the recommendation sections entirely. Once visitors learn that those "You might also like" widgets don't show them anything useful, they stop looking. You lose the placement permanently, not just the click.
What to look for in your current setup: does your recommendation engine factor in individual browsing behavior, past purchases, and real-time session activity? Or is it pulling from a static list of top sellers?
AI-powered recommendation models trained on your specific store data can match individual visitors to products based on what they've actually browsed, what similar customers bought, and what makes sense given the item they're currently viewing. The difference between a static bestseller list and a personalized recommendation isn't incremental. Stores that switch typically see click-through rates jump 3-5x because the products being shown are ones the visitor is actually likely to want.
3. Your widgets load too slowly
Speed kills ecommerce performance across the board, and recommendation widgets are a common culprit. Many recommendation apps inject heavy JavaScript bundles that block page rendering or take 2-3 seconds to populate after the page loads.
You've seen this yourself: you land on a product page and there's a visible "jump" as the recommendation section pops in a couple seconds later. Or worse, a blank space sits there with a loading spinner while the rest of the page is already interactive.
By the time those recommendations load, the visitor has already made their decision. They're either adding to cart or hitting the back button. A recommendation that loads 2 seconds after the buy button is a recommendation that doesn't exist for 60%+ of your visitors.
Check your page speed with and without your recommendation app enabled. If your widget is adding more than 200ms to your page load, it's costing you more in lost conversions than it's generating in recommendation clicks. The best implementations use async loading that doesn't block your main page content while still rendering fast enough to be visible during the decision window.
4. You only have recommendations on product pages
Most merchants stop at product page recommendations, and it makes sense as a starting point. That's where the buying decision happens.
But product pages aren't the only place where recommendations change behavior. Limiting recommendations to a single page type means you're missing 3-4 high-value touchpoints in every session.
Search results: visitors who use site search convert at 2-3x the rate of browsers. When a search returns zero results or a thin result set, showing AI-powered product suggestions rescues what would otherwise be a dead end. Instead of "No results found," you show "We didn't find an exact match, but customers looking for [query] often like these." That single change can recover 15-20% of zero-result searches.
Cart page: this is your last chance to increase order value before checkout. "Frequently bought together" or "Complete your order" recommendations on the cart page have some of the highest conversion rates of any widget placement because the visitor is already in buying mode. They've committed to a purchase and are more receptive to adding a complementary item.
404 pages: every store has them. Broken links from old email campaigns, expired product URLs, mistyped addresses. A 404 page with no recommendations is a dead end that bounces the visitor. A 404 page with "Here's what's popular right now" or personalized suggestions turns a broken experience into a product discovery moment.
Post-purchase page: the order confirmation page is one of the highest-engagement pages on your entire site. The customer just bought something and is still on a buying high. Showing relevant recommendations here, especially with a one-click add-to-order option, captures incremental revenue at near-zero acquisition cost.
Map out every page type in your store and ask: could a recommendation widget add value here? If the answer is yes and there's no widget, that's revenue you're not capturing.
5. Every visitor gets the same recommendation experience
Even merchants who have personalized product selections often treat the recommendation layout itself as one-size-fits-all. The widget size, number of products, placement, and logic are identical for every customer segment.
Your first-time visitor and your fifth-time buyer have completely different needs. A new visitor doesn't trust your brand yet. They need social proof and your bestselling, most-reviewed products to build confidence. A returning customer already trusts you. They want what's new, what's related to what they bought before, and what other customers with similar taste are buying.
A customer browsing a $300 item has different cross-sell expectations than someone browsing a $30 item. The premium buyer might respond to "Complete the look" styling suggestions. The value buyer responds to bundle discounts and "Customers also bought" social proof.
The fix isn't more widgets. It's smarter segmentation in how recommendations are served. Look at whether your current tool lets you customize recommendation logic by customer segment, traffic source, or purchase history. If every visitor sees identical widget behavior regardless of who they are, your recommendations are doing half the job they could.
6. You're not measuring what matters
Plenty of merchants can't answer a basic question: how much revenue did my recommendations generate last month?
If your recommendation app doesn't provide clear attribution data, you're flying blind. You don't know which widget placements are working, which ones visitors ignore, or whether the app is paying for itself.
The metrics that matter for recommendations go beyond impressions. Track click-through rate by widget placement (not just overall). Track revenue attributed to recommendation clicks with click-only attribution, not view-based attribution that inflates numbers by counting people who saw a widget but never interacted with it. Track AOV for orders that included a recommended product versus orders that didn't.
If you can't get this data from your current tool, you can't optimize. And if you can't optimize, you're stuck guessing which of the five problems above to fix first.
What all of this adds up to
Every problem on this list traces back to treating recommendations as a feature to check off rather than a revenue channel that needs ongoing work. Installing a widget and walking away isn't optimization. It's just installation.
The merchants who see 15-30% of revenue influenced by recommendations are the ones who treat it the way they treat paid acquisition: measure everything, test placements, personalize by segment, and iterate monthly.
If your current setup can't do that, the problem isn't recommendations as a concept. It's the tool. AI-powered product discovery platforms like PersonalizerAI build custom recommendation models trained on each store's catalog, order history, and customer behavior, so every widget placement serves genuinely personalized results with clear, verifiable attribution. That's the difference between a widget and a revenue channel.
Start with the first problem on this list. Open your store on your phone, find your recommendation widgets, and count how far down the page they are. That single audit will tell you a lot about where the gap is.