So what really is product recommendation? Well simply put it is the counterpart of a salesman on an e-commerce platform. It gently urges shoppers towards the right products by making personalized recommendations after analyzing and studying the shopper and the product he is interested in buying.
A product recommendation engine can also be defined as a filtering tool which runs on technologies such as machine learning, data analytics and deep learning to provide purchase suggestions that match the prospective clients' tastes and interests as accurately as possible. The success of the engine or the recommender systems is highly dependent on the accuracy of predicting suggestions.
Amazon and Netflix are in fact textbook examples of strong personalized product recommendations based engine. Most of us have experienced how Netflix suggests percentage interest match of a particular movie to our choice of movie genres. More often than not we end up watching the recommended movies or shows.
There can in fact be several other recommendations that we can make to our shoppers depending on what we are trying to sell. So what algorithms are these recommendations based upon? What goes on behind the scenes to create these recommendations?
These filtering systems or Recommendation engines almost always work on any one of the following three recommendation engine algorithms:
An algorithm that considers users interactions with products, with the assumption that other users will behave in similar ways. This records user-product interaction and tries to segment different types of users or products together. For instance, it will group similar customers by focussing on what they bought. Then they will suggest other products they purchased to all users who show interest in a particular product. It is based on the assumption that some customers are similar to each other.It can also group similar products into a particular group and show identical product recommendations to a user.
An algorithm that considers similarities between products and categories of products. It focusses on accurately categorizing products. The idea behind content-based filtering is that if you buy an item, you will also need other things that are of the same category. For instance, if you buy a mobile phone, you will also need to buy a screen guard and a mobile cover.
It is a combination of both Content and Collaborative based filtering. In other words, if product recommendation filtering systems were to be represented on a straight line Content-based filtering would make up one extreme while collaborative filtering would make up the other end. Every other form of filtering in between would be a Hybrid filtering system. Hybrid filtering systems are highly successful as they combine collaborative and content-based filtering to varying degrees as is the business need. It helps to customize results that are more accurate to a particular type of audience.
A recommendation Engine allows you to send personalized emails and promotional offers to individual customers due to collected data
Since time immemorial customers like nothing more than shop staff knowing their specific taste. Personalized recommendations gives them a sense of privilege. Recommendation engines help replicate the same sentiment on e-commerce sites by showing personalized product recommendations through data collected in real time.
Other than a sense of privilege customized content also engages the viewer more efficiently than random suggestions. When suggestions are in tune with his/her tastes chances are high that the customer will be interested.
An ecommerce recommendation engine is undoubtedly a body double for a salesman in an online store. It subtly urges the shopper to have a look at products he or she could be interested in. By showing a variety of products according to the viewer's interest, it helps them make a purchase.
Product Recommendation Engines help businesses get complete visibility into their sales, customers, revenue and more through detailed descriptions. These reports provide deep insights that are helpful when designing sales strategies.
It is not possible to manually collect such data about shoppers and then act on them. Product Recommendation Engines help reduce the workload on employees to find such data and them act on them.
Amazon reported a sales increase of 29% after implementing its recommendation engine. Product recommendation engines help increase sales strategically due to its personalized recommendations.
Also Bought – Recommend products that are similar and are usually bought together.
Also Viewed – suggest similar products to the ones viewed by the customer.
Best Sellers – Suggest highest selling product by combining sales data and recommendation logic.
Featured Items – Recommend products which can be relevant to a given search by brand, category, etc.
Hot Now! – Recommend real time highest selling products similar to customers interest.
Personalized – Extremely customized personalized recommendations based on viewers real time actions using machine learning.
Recently Viewed – This recommends to the viewer products they have viewed in the past as well as current session.
Also Added To Cart- Recommend products of a particular category that are logically bought together.
Thus, It would be safe to say that Product Recommendation Engines are now an intrinsic part of e-commerce platforms and functioning without them is like trying to drive blindfolded on a traffic-heavy highway. Qwentic has successfully developed Product Recommendation Engines for several Ecommerce platforms. To know more about them you could Message us here.
Qwentic is a leading technology consulting company, engaged in offering end to end consulting services. We are technology consulting partners to several leading businesses across a diverse range of industries spanning Logistics, Healthcare, Advertisement, and E-learningRead More