E-commerce enterprises have been becoming increasing popular in the market place. To launch smoothly and effectively each online merchant will undoubtedly need the help from supporting tools such as product recommendation engines. Basically, product recommendation engines functions as advisors to provide appropriate suggestions for web browsers. However, there are different ways that are used to figure out the most suitable advices for consumers. Following are 10 most effective techniques used to increase revenues.
This method is mostly used to display suggested products on cart pages when a customer already chose a product. For example, if a consumer decides to buy an Iphone 8, some suggestions such as earphones, cases, irings, and so on will be recommended for him. Applying this technique to increase the number of items and the amount of value per order is highly effective since these items suggested are often of customer’s demands. However, there is still a drawback in this strategy that shop admins should take into their consideration is checkout process. As the more buyers click on products, the more suggested options will be displayed; checkout process as a consequence, will take longer and more complicated. Therefore, store owners should optimize their pages to make sure that checkout process is at ease to all consumers no matter how many items they add after receiving recommendations.
A product recommendation engine to display best-seller products is applied for almost all online merchants as a strategic sale method. Especially, it is highly suggested in case the stores have limited customer data. In other words, best-selling product recommendation is a backup plan when other recommendation engines do not have enough information to perform its functions.
Latest product recommendation is quite similar to best-seller recommendation as it is specially suitable for a campaign that does not need to be personalized. This method is very useful and handy for web browsers as they can easily identify the most up-to-date products in the marketplace without wasting time looking for them. Additionally, this engine enables regular customers find out what is new in the store just in a second.
This method needs a certain amount of customer data about their tastes and preferences to figure out the most suitable suggestions for shopping doers. Data can be collected via customer shopping behavior such as the products they are looking for, products already bought in the past, keywords, and so on. If an online store system can determine that they thoroughly understand what customer’ needs and they care about customer’s interest, they probably create a sense of satisfaction and retention among consumers.
Imagine when a customer explores a product by chance and later they want to find more information about this product, recently viewed recommendation works perfectly in this field. Every item that a buyer used to search in your online store will be recorded and personalized to give them suitable recommendation whenever they need. Only by one simple click, can customer track all their search history; therefore, they can make shopping decision more easily.
Popular product recommendation is applied to provide suggestions to customers who do not have any idea about detailed description of the product they want to purchase. The recommendation engine which offer popular items will be such a helpful advisor in this situation. A series of items which are favorable for many people will determine that these items are fashionable or have good quality. For example, when a female want to search for a dress and she only enters “dress” in the searching box. By giving her the most popular and favorable dresses chosen by other people, she can take them into her account. As a result, decision-making process will take lesser time and be easier as well.
This recommendation engine is a tool that offers suggestions after a shopping doer decides to buy a particular item. From other people’s experience and shopping behaviors, additional options are generated. Therefore, customers can treat these suggestions as useful advices from many consultants. Moreover, since these suggestions are based on other consumer’s purchases, it will become more reliable and trustworthy.
Personalized advisor provides recommendation to consumers based on their previous shopping behaviors. That is the reason why to function this method, online merchants need to accumulate a certain amount of customer data via their past purchase and browsing history. Each consumer, as a result, will receive a unique list of recommendations that highly match their preferences and tastes. A personalized recommendation engine is considered to the best one to give specific and useful suggestions for web browsers.
Off-site recommendation engine is applied after a consumer already purchased a product and finished their online session in the stores. It is a follow up activity which is expected to increase customer’s demands about other products. Depending on what consumers bought in their previous session, algorithms will analyze several criteria to figure out the most appropriate results to offer to consumers. These results will later be sent to buyers through their emails. Some online merchants even take advantages of this chance to attach an appealing voucher such as discount to encourage customers to shop more.
In conclusion, product recommendation engines play integral parts in making the success for any online merchant. There are also various choices for e-commerce store to consider for their online system. If your store still does not have any product recommendation engine, it is time for you to seriously think about setting up one to maximize your store’s benefit. If you are confused about what recommender is the most suitable for you, Magento 2 automatic related products extension is here to wait for you to experience. Our great extension will sure not to let you down with its significant contribution to your store’s success. For more details and information about our extension, please visit our website: https://www.mageplaza.com/magento-2-automatic-related-products/
Posted by ma in Blog . January 04, 2018