In 1997, Netflix started as a video rental company. Back in the times when to watch a movie at home you should have actually gone out to the shop, select a DVD, and bring it home, the founders of Netflix X and Y saw opportunities for a new business model. Instead, users would choose the movies online and get DVDs delivered right at their doors. But Netflix wouldn’t have become an international prodigy if they didn’t come up with an innovative solution – recommendation system.
In this post, we are going to talk about recommender systems and how any business from large-scale to small can use them to repeat Netflix’s success.
What is a recommender system?
Machine learning and artificial intelligence are the areas of computer science that teach computers to analyze data and learn from it in order to make its own decisions. Those businesses who want to streamline their processes, provide better customer service, and improve decision-making, implement machine learning algorithms for their projects.
A recommendation system is one of ML practical applications. This is an algorithm that informs the user about the products on the website or application that may be the most interesting to them at a given time. The client receives information, and the service earns on the provision of quality services. This does not necessarily mean sales. The service can also earn commissions or simply increase user loyalty, which then translates into higher income and brand recognition.
Personalization of online experience is an obvious trend of the last decade. McKinsey estimates that 35% of Amazon’s revenue or 75% of Netflix’s revenue comes from featured products, and this percentage is likely to grow. Recommender systems are all about making the client happy.
Types of recommendation systems
- Content-based filtering
The user is recommended objects similar to those that the user has already accessed or purchased. Similarities are evaluated based on the type of objects. When you use this type of recommendation system, it is possible to recommend even those items that have not been rated by other users. However, the main disadvantage of this approach is a strong dependence on the subject area, reduced accuracy, and limited usefulness of recommendations.
- Knowledge-based filtering
Knowledge-based filtering bases its recommendations on the knowledge of the subject area (but not about each product). This type of recommendation is highly accurate, offering the user what he needs. This system considers user preferences and demographic characteristics. The main disadvantage is that knowledge-based filtering algorithms are harder to build and collecting data is more difficult and time-consuming.
- Collaborative filtering
The rating history of both the concrete user and other users is used. This is a more versatile approach than content-based and knowledge-based filtering that often gives better results. However, in the beginning, the algorithm might not work properly (the so-called problem of a cold start).
- Hybrid recommendation systems
Hybrid recommender systems are based on a combination of collaborative and content approaches, which helps to get rid of most of the disadvantages inherent in each system.
The benefits of recommender systems
Businesses all over the world implement recommender systems to their websites and applications. Personalization is now a standard of brand communication with the customers. Your competitors are already using recommender systems on their website and you are losing the game if you don’t have it. A specialized algorithm enables companies to offer users more interesting pieces of content that will motivate them to explore the resource further.
- Customer satisfaction
Providing a more personalized experience, you increase customer satisfaction. Recommender systems facilitate user navigation inside your digital product be it an e-commerce website or a mobile application. At the same time, you save the users time to find what they are looking for.
- User engagement
Studies show that recommendation systems help to boost user engagement. Netflix, Amazon, and many other companies prove that users tend to interact with the product more when there is a recommender system. They also tend to keep going back and maintain a higher loyalty to the brand.
- Stimulated sales
Recommender systems allow to augment the average check providing the users with valuable recommendations about what else they can order.
- Data-driven approach
The machine learning algorithm bases its recommendations on data and not on assumptions like marketers do. Therefore, the recommendations that the program provides are more accurate and sustainable.
Do you need a recommendation system?
Online shops, media portals, educational platforms – all of these can win from integrating a recommendation engine. When your competitors are already using AI-powered solutions, it is unreasonable to not follow the lead. Recommendation systems help to provide the best customer service and, consequently, increase sales and improve customer retention.
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