Product recommendation systems are among the most widespread applications of machine learning that enable businesses to enrich customer engagement and shopping experience.
A product recommendation system is a subclass of information filtering that uses machine learning to provide personalized recommendations to consumers based on their data. For instance, they can provide appropriate recommendations based on customers’ preferences, demographics, and past purchases. Segmenting customers based on their data is one of the many ways big data is impacting e-commerce and other digital businesses. And this ability of product recommendation systems has gained them a lot of traction on e-commerce and online media sites in the past few years. According to a study, up to 31% of total e-commerce revenue is generated because of recommended products. E-commerce is the most common example when it comes to the industries using product recommendation systems. But, various other industries are being disrupted by their use-cases.
How Product Recommendation Systems are Benefitting Several Businesses
The world is moving from one-size-fits-all solutions to personalized tailor-made solutions. And product recommendation systems are helping business owners across several industries to adapt to this shift towards providing personalized solutions.
One of the core benefits of product recommendation systems is the ability to frequently collaborate with customers. If a company is providing relevant recommendations to customers, then there are fewer chances of losing potential clients. For instance, if an online media company is giving relevant suggestions, then a viewer might not want to go to a competitor site and search for movies or series. Customers also appreciate personalized recommendations. According to a survey conducted, 63% of customers want product recommendations, which was up from 57% two years ago. They were also willing to share their data in exchange for personalized recommendations. And this shows how the importance of personalized recommendations is increasing and will continue to increase in the future. Hence business administrators should start using product recommendation systems for providing personalized services to their customers.
An online company with thousands and thousands of products cannot provide recommendations for all their products to customers. And providing inaccurate recommendations often might become frustrating for customers. An accurate product recommendation system always recommends a product that is most relevant and likely to be purchased by customers. Thus, because of appropriate recommendations at the right time, customers might order several products at a single time instead of buying one product. That’s how product recommendation systems help to increase the sales of businesses.
Product recommendation systems also provide recommendations for products that customers frequently buy together. For instance, shirts or t-shirts are often bought along with jeans, and recommendation systems can recommend shirts that go well with purchased jeans.
Several techniques are used by product recommendation systems to categorize customers into different groups and provide personalized recommendations to them. For instance, collaborative filtering, content-based filtering, and demographic-based filtering are different approaches that can be taken for providing recommendations. The next milestone in product recommendation systems would be creating a single algorithm that will be best for categorizing customers with all the filtering approaches. And if researchers can achieve this milestone, then instead of using different techniques for categorization based on various domains, they can use a single algorithm that will classify customers in all domains.
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