How Netflix uses predictive analytics for personalized recommendations – Customers nowadays seem to expect brands to read their minds. Customers demand personalized recommendations now that companies like Netflix are delivering individualized insights on a daily basis. They want brands to always be one step ahead of them.
So, how can companies achieve this level of proactiveness? What methods do you use to predict requirements, trends, and behaviors? The answer is “Predictive Analytics”. But, how does it work? Let’s find out.
What is Predictive Analytics?
Predictive analytics uses numerous statistical approaches, including data mining, pattern recognition, and machine learning, to examine current and past data in order to create predictions. It has helped brands in better understanding their customers, as well as identifying risks and opportunities and guiding decision-making.
Companies may use predictive analytics to explore the future and gain more consistent and accurate information. Predictive analytics gives a macro-level insight into user behavior and purchasing patterns, but brands also employ it on a micro-level.
Also Read: How To Access the Secret Menu On Netflix
How Netflix is Using Predictive Analytics?
Netflix utilizes predictive analytics to give personalized suggestions, but how exactly?
Netflix is continuously gathering information. Netflix’s powerful analytics engine uses AI-powered algorithms to identify what the user might be interested in seeing next based on the user’s watch history, search history, demographics, ratings, and preferences.
Whenever you access Netflix, it identifies a pattern based on titles, such as their genre, categories, actors, release year, the device used to access, how long the user watched a title, and time of day the user watched it.
Based on that data it recommends users, similar tastes & preferences in the same genre that other users also watched or related to. All of these pieces of data are used as inputs that Netflix process in its algorithm to choose titles within the row and then ranks the rows using algorithms and complex systems to provide a personalized experience.
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