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The system which tends to present the most suitable product to the eCommerce website user is called product recommendation. It is a process of continuous optimization to find perfection in the recommendations. It is based on data processing coupled with intelligence and data presentation. Nowadays, modern technologies like Artificial Intelligence (AI) is very helpful in eCommerce product recommendations. Various data gathered is sent to the intelligence engine for the processing which returns an output as recommendations. The intelligence engine considers many factors or variables defined before recommending a product to the website users.

The product recommendation enhances the user experience of the eCommerce website. There can be thousands of products in the catalog and any user might not browse all the products available. They like to find the product of their requirement a the earliest opportunity. Product recommendation solves this pain of the website users. Online buyers always look for comfort in finishing the process. Product recommendation makes the buying process hassle-free. It is pretty handy to beat the competition nowadays. As precisely product is recommended that much sales the website will book.

eCommerce product recommendations work in the following ways:

1. User Behavior-Based Recommendation

A user browses an eCommerce website with a definite pattern. All the user behavior data is used in profiling the user. Based on browsing behavior the intelligence engine makes a decision and recommends products. The browsing behavior includes website navigation behavior, product search data, product page visits, products used earlier, and feedback given and the list goes on. Different users usually have different browsing patterns which differentiate their data profiles. For example, if a user has searched for a t-shirt of a particular brand then that user can be recommended for other similar branded t-shirts and likewise. There can be many behavior patterns that need to be read by the intelligence engine before showing a product recommendation to the user.

2. Social Information Based Recommendations

The intelligence engine creates social information based profile of users. The social information might include but is not limited to, location of a user, the purchasing power of the user, and so on. This data hints at what a user might like to purchase online. For example, if a user belongs to a posh area of a city and looking for online shampoo then the user will be recommended for a shampoo bottle instead of a small shampoo pouch and vice versa. Social information also hints at the psychology of the user. It might hint whether the user will like freebies more than other users. At this juncture, retailers should mind the life-time value of the user instead of one-time or short-term gains from the user.

3. Up-selling and Cross-selling Practices in eCommerce

Up-selling and cross-selling are frequently used in the eCommerce industry. These are the tools to enhance eCommerce sales as well as customer experience. Up-selling recommends a higher-valued product with enhanced features or quality than what the user had chosen to purchase. These tactics work well, especially in eCommerce. On the other hand, cross-selling is recommend and sell similar products with what the user has already purchased. For example, if a user has already purchased a tie and a pair of shoes then it is easier to recommend and sell a belt to that user and likewise.

Conclusion

Many factors decide on product recommendations. Somehow the product recommendation has become a major factor in enhancing sales. Efficient data processing by the engineers is making this possible. Amazon is successfully implementing AI technology for data processing to enhance user experience. Modern eCommerce platforms like Builderfly help retailers to attain their business goals efficiently.

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