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The ROI of Recommender Engines for Commercialization

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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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Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. The authors of the staff are listed here.


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