The ROI of recommendation engines for marketing

Netflix’s long list of suggested movies and TV shows is a fantastic example of a personalized user experience. In fact, about 70 percent of everything users watch is a personalized recommendation, according to the company.

Getting to that point hasn’t been easy, and improving on its recommendation system is an ongoing process. Netflix has spent well over a decade developing and refining its recommendations.

In 2006, it launched the Netflix Prize to search for machine learning experts who could improve its previous algorithm. A team of algorithmic scientists bested the company’s algorithm by 10 percent — a small percentage, you may think, but it was convincing enough for the company to expect huge improvements in the future. The team’s efforts earned them a $1 million prize.

Recommendation engines can help marketers and organizations increase the likelihood of arriving at recommendations tailored to a user’s past online activity or behavior using in-depth knowledge based on big data analysis.

In this article, I’ll explore how companies can increase their ROI by fruitfully leveraging personalization and recommendations. I’ll break down the potential business benefits of recommendation engines into three categories based on my company’s analysis of dozens of recommendation engine use cases.

[Read the full article on MarTech Today.]

Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.

About The Author

Daniel Faggella

Daniel Faggella is an email marketing and marketing automation expert with a focus on the intersection of marketing and artificial intelligence. He runs TechEmergence, a San Francisco-based market research and media platform for artificial intelligence and machine learning applications in business.