Once you join Netflix to enjoy a movie, it provides you with a large bulk of choices. Among the choices most of them are your favorites. The recommendations make you wonder how an artificial system has done such magic? There is a story behind it that talks based on sustainable digital marketing. The right decisions on choice architecture will lead the business to greater destinations. Even if you have a problem of choosing the best to watch, the solution is not that difficult as the system already provided recommendations. Modern computer technology has made many consumer choice problems simple. Netflix, the mail order DVD rental company succeeded immensely due to effective choice architecture. On the website the customers can easily search the movies by the actor, director, genre and more. Additionally they can get recommendations from the other movie lovers with the same taste. That method is calling collaborative filtering. This is an effort to solve the problem of choice architecture. If you know what people like you tend to like, you might well be comfortable in selecting products you don’t know, because people like you tend to like them. Collaborative filtering solves the problem of difficult choices (Thaler & Sunstein, 2008., pp. 105).
People adapt different strategies to adapt different choices. When the problem is small, people tend to examine all the relative alternatives prior to taking a decision. But when the choice set gets large, we must use alternative strategies and that can lead people into trouble (Thaler & Sunstein, 2008., pp. 89). According to Thaler and Sunstein (2008), choice architecture refers to the practice of influencing choice by “organizing the context in which people make decisions”. The examples are the ways food displays in cafeterias, where offering healthy food at the beginning of the line or at eye level can contribute to healthier choices.
As mentioned in the beginning, once you log into Netflix, you can see the exact screen you wanted to have. The magic is done with the help of a recommender system. This system designed to predict the future preference of a person provided limited data. This reduces the unnecessary content which interrupts the viewer in enjoying the favourite movies. The recommender systems in digital marketing use collaborative filtering as a technique. The recommender system is one of the main web analytic applications for sustainable digital marketing (Hwangbo & Kim , 2019).The system can propose products and services in a personalized way satisfying the customers sustainability preferences. This can be applied in different areas in digital marketing. There are two ways a recommender system can be built. First is content based recommendation. This will filter the content based on likes, dislikes and viewing time in netflix. The second one is collaborative filtering. Here automatic predictions occur based on the collection of preferences and tastes within a pool of users (Vaidyanathan, 2020). Recommender systems provide reliable information for the customers to solve the complex decision making problem. The customers don’t need any domain knowledge as they can automatically learn the process in digital marketing. Additionally the consumers will learn new interests. When purchasing individually, the motivation will be low and the decision making will be complex. But with the recommender system along with the collaborative filtering the customer can get to know the idea of the similar tastes of other customers.
The decision on problem solving will be complex in the present scenario. In order to move ahead in sustainability, Netflix focused more on customer satisfaction and comfort. The collaborative filtering leads the complex problems to be solved easily. Netflix is one of the examples of success stories in digital marketing sustainability. The platform allows people to save time and solve the complex problems. In this sense recommender systems are an appropriate technical medium for sustainable digital marketing because that allows the organization to provide accurate information regarding the users preferences. The recommenders have a positive impact on sustainable digital marketing.
Thaler, R. H., & Sunstein, C. R. (n.d.). Nudge, improving decisions about Health, Wealth and Happiness. New York, United States: Penguin Random House.
Hwangbo, H., & Kim , Y. (2019). Session-Based Recommender System for Sustainable Digital Marketing. Retrieved from https://www.researchgate.net/publication/333826519_Session-Based_Recommender_System_for_Sustainable_Digital_Marketing
Rocca, B. (2019). 302 Found. Retrieved from https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada
Vaidyanathan, V. (2020, February 2). How Does Netflix Get You The Right Content All The Time? Retrieved from https://www.scienceabc.com/innovation/netflix-get-right-content-time.html