The long list of movies and TV shows suggested by Netflix is a fantastic example of a personalized user experience. In fact, about 70% of all that users watch is a personalized recommendation, according to the company.
It has not been easy to get there and the improvement of its referral system is a continuous process. Netflix has spent well over a decade developing and refining its recommendations.
In 2006, he launched the Netflix Award to search for machine learning experts who could improve his previous algorithm. A team of algorithmic scientists surpassed the company's algorithm by 10% – a small percentage, do you think, but it was convincing enough for the company to wait? to huge improvements in the future. The team's efforts earned them a prize of one million dollars.
Referral engines can help marketers and organizations to increase the likelihood of making recommendations tailored to the activity or past behavior of a user by using in-depth knowledge based on the 39, big data analysis.
In this article, I will explore how companies can increase their return on investment by successfully leveraging customization and recommendations. I will divide the potential business benefits of recommendation engines into three categories based on analysis by my company of dozens of use cases of recommendation engines.
[Read the full article on MarTech Today.]
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