The bar for e-commerce user experience has been set by giants like Amazon and Netflix. Customers now expect the platform to know what they want before they know it themselves. When they view a product, they expect to see relevant suggestions that match their taste, budget, and intent. The SaaS Hub notes that static "Related Products" sections—where the merchant manually links items—are no longer sufficient. They are labor-intensive to maintain and often inaccurate. The solution lies in Artificial Intelligence. By deploying AI-driven recommendation engines, you can serve hyper-personalized product suggestions to every single visitor, drastically increasing the likelihood of adding an extra item to the cart.
AI recommendation engines work by analyzing vast amounts of data in real-time. They track every click, scroll, add-to-cart, and purchase on your site. They identify patterns that a human would miss. For example, the algorithm might notice that people who buy the blue yoga mat also tend to look at the green water bottle, even though those products are in different categories. The
best selling products apps for shopify leverage these machine learning models to automatically populate "Recommended for You" widgets. These widgets are dynamic; they change based on the user's behavior during that specific session. If a user starts looking at premium items, the recommendations shift to show higher-tier products.
"Also Bought" logic is one of the most powerful algorithms for cross-selling. It leverages social proof and data correlation. When a customer is on a product page, showing them "Customers who bought this item also bought..." provides a strong signal. It tells the user that this combination is popular and trusted by others. It acts as a peer recommendation. The AI ensures that these suggestions are statistically significant. It won't recommend a random item that was bought together once; it waits until there is a pattern. This relevance is key. If the recommendation makes sense, the customer perceives it as helpful service rather than a sales pitch.
"Recently Viewed" history is a simple but effective retention tool. As customers browse, they often forget what they looked at five minutes ago. A "Recently Viewed" bar at the bottom of the screen or on the cart page acts as a visual memory aid. It allows the customer to easily navigate back to a product they were considering. The AI can optimize this list, perhaps highlighting the item that has the lowest stock or the highest rating to nudge the user toward a decision. This feature significantly reduces bounce rates by keeping the user engaged with products they have already shown interest in.
"Shop the Look" is an advanced recommendation strategy for fashion and lifestyle brands. Instead of just suggesting a shirt, the AI can analyze the product image and suggest the pants and shoes that the model is wearing. This visual search capability allows the customer to buy the entire outfit with a few clicks. Some advanced apps use computer vision to identify similar items in your catalog if the exact match is out of stock. This creates a seamless discovery experience where the customer can explore your inventory visually, leading to higher basket sizes.
Cold start recommendations solve the problem of new visitors. When a user lands on your site for the first time, the AI has no data on them. In this scenario, the system defaults to "Global Best Sellers" or "Trending Now" data. It shows the products that have the highest universal conversion rate. This ensures that even a cold traffic visitor is presented with your strongest offers immediately. As soon as the visitor clicks on one item, the AI wakes up and starts personalizing the subsequent recommendations based on that click, refining the experience in real-time.
In conclusion, AI recommendations move merchandising from a "one-to-many" broadcast to a "one-to-one" conversation. It treats every visitor as an individual with unique intent. By using algorithms to serve the right product at the right moment, you remove friction from the discovery process. You stop selling to strangers and start serving customers.
