Websites that use product recommendation engines usually have these at the homepage of the website. This allows users and possible customers to look at items that they would recommend based on the date of its upload, availability and price. It can also provide users with a quick look at items that may be on special offer or those that can be matched to other items at a lower total price. This allows the user to get a glimpse of the various offerings of the website prior to doing their search and this also gives them a chance to see items related to the items that they were initially looking for.
Product recommendation engines provide users the chance look at items they didn’t initially intend to look at and from there, provide them with a greater variety of items that are available for purchasing. For instance, if a user visits an online store that sells clothing items as well as apparels, the recommendation engines can provide that user suggested items that can be paired with the items that he or she intended to buy. If a person searches for a shirt, the engine can suggest pants that will compliment the cut of the shirt; or in well – calculated and instructed engines, it can actually suggest entire outfits that will match the style preference of the user. These engines can account for up to 30% of increase in sales because it can turn single item purchases into multiple item purchases, multiplying the sale by up to 5 times depending on the item suggestion and its accuracy in providing the user with the items that will appeal to his or her preferences. This is the importance of providing personalized shopping experiences for the user online by asking for the user data and permission to use this data to calculate the desired search output for that particular user. This minimizes the time that the user will have to spend on just searching for items and instead, use the time instead on comparing and evaluating suggested products that may appeal to the user’s fancy.
An increase in sales can also be brought about by return customers who have enjoyed the convenience and efficiency that they experienced in a particular online store. These return customers can share their experience to other people, and through word of mouth or through the bandwagon effect, i.e. when a particular behavior or preference spreads among a group of people, other users may visit the website to personally experience the ease and enjoyment that the original customer enjoyed. In this sense, sales increase can be attributed to the pleasure that the engine has given the original customer who in turn shared the experience to others thereby increasing the website’s popularity as well. The increase in internet traffic in the website will increase the possibility of it turning up more often in search engines such as Google or Yahoo and will thus bring in more potential customers. The domino effect in this sense works well for the website because a single transaction from a satisfied customer can lead to greater sales in the future through that customer’s personal network.
A product recommendation engine allows the website moderator to efficiently showcase the website’s products by zeroing in on the right customers for the right items. This gives the customer the satisfaction of getting the items that he or she wants while at the same time discovering additional items that can complement the original items intended by the customer to be purchased. The usefulness of this engine in increasing sales is undeniable as it provides an efficient way of enticing customers to enjoy the website and its products and services every time he or she goes online.