eCommerce recommendation engines are product suggestions approaches that help the customers access various suitable items in your store. There are a lot of engines to suggest products, that may make you concerned which ones are the most effective approaches to your stores. Our article today will show you 5 leading eCommerce recommendation engines to boost your sales and customers satisfaction.
5 Awesome eCommerce recommendation engine
Based On Products’ Rating
Nowadays, customers rarely completely trust the advertisement from brands. They may have some suspicions and want to check the trustworthiness by products’ rating. Because of this tendency, product recommendations based on rating become one of the most effective and persuasive engines. The explicit feedback such as rating, reviews, comments of customers about your products and services play as personal recommendations. The systems can rely on the results of ratings to suggest the best products for the customers during their searching process on the homepage.
The sellers can use tracking tools and selling reports to summarize and analyze various types of customer data. They are purchase history, order value, preference, behaviors,… to form customers’ favorite types of products and generate well-matched suggestions. The personalized product recommendation requires a large amount of customer data. It also needs complicated analytical processes with the utilization of algorithms, artificial intelligence and machine learning. Moreover, we can not have customer data of new visitors. So it may be impossible to generate personalized suggestions for those customers.
We would like to inform you of a new and effective method to recommend suitable for returning and first-meet customers. It’s called a “fallback scenario”. This method will experiment with whether there is enough data to form personalized suggestions. If they do not have sufficient data, they will use metadata to generate recommendations for customers.
Another way to recommend products is based on similar items. Traditionally, your system can detect which products with same characteristics to group them and pop up suggestions based on the product category. However, there are some features that prevent the simple process from detecting similar items such as similar colors, patterns, shapes and so on. Here, we need advanced engines and two of them are filtering meta-database similarity and “item to item collaborative filtering”.
The productive product recommendation types which are usually displayed on cart pages are complementary products. It is as known as “Frequently bought together”. This engine required a huge amount of selling data, product information and customers data to generate helpful suggestions without increasing cart abandonment. When taking advantage of this engine, you may need to make the layout design into consideration. To have an eye-catching layout, you can use A/B testing to try different styles.
The final recommendation engine in this list is the simple one that suggests the recently viewed products. The only data source for this engine is the searching history which you can withdraw from your website easily. This engine plays a crucial role in retargeting campaigns. You may take advantage of it to send email reminders for customers. Who have been distracted when shopping or abandoned their shopping carts.
Each eCommerce recommendation engine has a different purpose and requires different sources of data. Therefore, to apply exact engines to your selling, you may need to understand clearly your selling status and goals and an awesome tool to help you. If you use BigCommerce platform, product recommendation app of GritGlobal can be a wonderful option for your to suggest products effortlessly.