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How do Recommendation Systems Boost Your Sales and Customer Satisfaction?

The worldwide development and spread of technology have led to an increase in e-commerce sites. With the rise in e-commerce sites day by day, a seriously competitive environment has emerged in the market. In this competitive environment, Recommendation Systems have emerged to increase sales rates by bringing the right product to the right user in line with the right strategies.

Recommendation Systems' primary focus is to provide customers with personalized products and services. The right products and services are offered to customers, considering their preferences, tendencies, tastes, and purchasing history. To give a more concrete example, we can consider recommendation systems on various online channels such as Netflix, Amazon, or YouTube. In these channels, we are offered content and product options according to our interests so that both the time we spend here and the rate of using their products and services increase.

So, how do Recommendation Systems boost your sales and customer satisfaction?

Recommendation Systems aims to enable brands that want to do e-commerce through online channels to communicate more accurately with potential customers, enabling them to complete their shopping and become regular customers. At this point, collecting data about users plays an important role. With the help of various software systems, the data of the users who visit the websites or applications are analyzed, and these users are offered a variety of products and services that they may be interested in. Thus, customers can easily buy products and services that they are looking for and sometimes even not. Users always want to reach the products they are looking for quickly, and during their shopping, they may be inclined to buy products that they do not think to buy but that interest them. Accurate data analysis is critical for this situation to occur.

Every brand that knows its customers, analyzes their needs and wishes correctly, and offers products and services will increase its sales rates when viewed in a comprehensive framework. That's why we can clearly say that if you know your potential customers and give importance to customer experience, you will be offering the right products to the right customers, and this situation causes to increase your total sales.

What are the Recommendation System Types?

Mainly, there are four different Recommendation Systems that are widely used. The purpose of all of these types is to make the purchasing process more efficient by making the user and customer analysis correct.

• Collaborative Filtering Systems: Collaborative Filtering System, one of the most frequently used filtering methods, analyzes the movements of customers and users on online channels and establishes a relationship between all customers with this analysis. The data obtained from here is aimed at providing the right products and services to the users.
• Item-Item Collaborative Filtering Systems: It is a method similar to the filtering method we mentioned before, but the main difference between them is that the Item-Item Collaborative Filtering System focuses on establishing a similar relationship between products. After collecting the data of the users, it is ensured that matching products are offered to the users by establishing a connection between the products they are interested in.
• Content-Based Filtering Systems: Content-Based Filtering is a slightly more complex method. This method focuses on both the keywords in the product description and the products that users are interested in. In order to provide the right products and services to the users, more product variety is achieved by using the keywords in the product description. Thus, both user preferences and keywords become the main focus. When we look at it as a total, we can see that a double-sided method has been developed for personalized recommendations for users.
• Popularity-Based Recommendation Systems: As the name suggests, Popularity-Based Recommendation Systems focus on trending products of that period. If we make it concrete by giving an example, we can say that work should be done at this point in order to deliver a best-selling hand cream to more customers. The difference between this recommendation system from other systems is that customer preferences cannot be personalized. Since the product and service recommendation based on trends is offered, it cannot be evaluated on the personalized recommendation scale. In short, it does not fall into the category of user-based suggestions.

If you want to use a Recommendation System application for your brand, Recommaster transparently analyzes your customers on online channels such as the web or mobile using artificial intelligence-based recommendation systems. Thus, it enables you to make sense of the behavior of your existing customer groups and increase your sales rates, allowing you to reach even more potential customers.