Top 10 eCommerce Product Recommendation Insights
When you visit an online store, have you ever noticed how some products seem tailored to your interests? These product recommendations aren’t random. They’re designed to guide you to products you might like, boosting both your shopping experience and the store’s sales. For online businesses, knowing how to use eCommerce product recommendation effectively is key to keeping customers engaged and increasing revenue. In this article, we will introduce you to 10 important insights into eCommerce product suggestions. We’ll cover different recommendation systems, strategies to improve customer engagement, and how automation can help you implement these recommendations more easily. What is eCommerce Product Recommendation? eCommerce product recommendation is personalized suggestions shown to customers while they shop online. These suggestions are based on their past behavior, browsing history, or similar customers’ actions. The goal is to show relevant products to shoppers, encouraging them to make additional purchases. Research shows that 75% of online shoppers prefer a personalized shopping experience, which is precisely what product recommendations aim to deliver. By using personalized suggestions, businesses can not only enhance the shopping experience but also increase conversion rates significantly. Defining Product Recommendations At its core, a product recommendation is a suggestion made to customers while they shop. It could appear as “People also bought this,” “Recommended for you,” or “You might like.” The aim is to make the customer feel like the store understands their needs and preferences. Personalized product recommendations can boost revenue by 10-30% for eCommerce businesses that effectively implement them. These recommendations are powered by algorithms analyzing browsing behavior, purchase history, and similar customers’ actions, making them highly effective at influencing purchasing decisions How They Work Recommendation engines use customer data to create these suggestions. This could be anything from a shopper’s browsing history, previous purchases, or even how similar users behaved. As customers interact more with the site, machine learning product recommendations become smarter and more personalized. The longer a shopper uses the site, the more data is gathered, leading to better and more relevant suggestions. Types of Product Recommendation Systems Different eCommerce businesses use different types of recommendation engines. The type of system you choose depends on the kind of data you have and how personalized you want your recommendations to be. Collaborative Filtering Collaborative filtering looks at what similar customers have bought or viewed and recommends items based on those similarities. If customers A and B have similar tastes, and customer A buys a certain product, customer B might get a recommendation for the same product. One downside of collaborative filtering is that it requires a lot of data to work well. Smaller stores might find that their recommendations improve only after they’ve collected enough customer interactions. Content-Based Filtering Content-based filtering looks at the product attributes themselves. Instead of comparing users, it compares the characteristics of products. If a customer likes a specific brand of shoes, content-based filtering will recommend other shoes from that brand or shoes with similar styles. Content-based filtering is especially useful for stores that want to promote specific products or brands. Since it doesn’t need user data, it’s also good for new or small eCommerce stores. Hybrid Systems Hybrid systems combine collaborative and content-based filtering to provide more accurate recommendations. These systems can look at both user behavior and product details, giving a more personalized experience for each customer. Hybrid systems are typically the most effective, but they can be more complex to set up. However, for larger stores with diverse products and customers, they offer the best results. Top 10 eCommerce Product Recommendation Insights Now that you know how eCommerce product recommendation work, let’s explore 10 insights that can help you implement them effectively and grow your business. Show Products Based on Browsing History One of the easiest ways to offer personalized recommendations is by showing customers products they’ve already viewed. This helps them quickly find what they’re looking for and can lead to faster decisions. Displaying products based on browsing history is a great way to keep your store relevant in the customer’s mind. It’s simple and effective for reminding shoppers about products they were considering but didn’t buy. Bundle Commonly Bought Together Items People love convenience. Offering product bundles that are often bought together is a great way to increase the value of each order. Bundles save customers time and encourage them to purchase more, boosting your average order value. Use Personalized Recommendations in Emails Emails are an excellent way to keep customers engaged, especially when they contain personalized product recommendations. Product recommendation emails feel more tailored to the customer and help drive them back to your store. Introduce Shoppers to New Products Customers are always looking for new and exciting products. Introducing them to fresh items can keep their shopping experience exciting. Keeping your product lineup fresh helps attract return visits from customers who want to see what’s new. Bring Back Lost Sales Sometimes, customers don’t complete their purchases. But with the right recommendations, you can win them back. By reaching out with personalized recommendations, you can recover lost sales and increase your conversion rates. Display Related Products on Product Pages When customers are viewing a product, they’re often open to suggestions for similar or related items. Displaying related products on the page keeps customers engaged and encourages them to explore more options, leading to bigger purchases. Use Social Proof to Drive Purchases People trust the opinions of others. Showing reviews, ratings, and recommendations from other customers builds confidence. Social proof makes customers feel more secure in their purchases, increasing the likelihood that they’ll buy. Highlight Best-Selling Items Best-selling items are often popular for a reason. Highlighting these can encourage customers to pick what others love. By highlighting what’s already successful, you help customers find products they’re more likely to enjoy. Offer Product Bundles with Discounts Offering discounts on product bundles is a smart way to increase order value and make customers feel like they’re getting a deal. Discounted bundles encourage customers to spend more while feeling like they’re saving,
