Wikipedia defines recommendation engines (and platforms and systems) as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.” But as a tool, technology, and digital platform, recommendation engines are far more intriguing and important than this definition suggests.
In data-driven markets, the most effective competitors reliably offer the most effective advice. When predictive analytics are repackaged and repurposed as recommendations, they transform how people perceive, experience, and exercise choice. The most powerful — and empowering — engines of commerce are recommendation engines.
Recommendation engines have been essential to the success of digital platforms Alibaba, Amazon, Netflix, and Spotify, according to their founders and CEOs. For companies such as these, recommendation engines aren’t merely marketing or sales tools but drivers of insight, innovation, and engagement. Superior recommendations measurably build superior loyalty and growth; they amplify customer lifetime value. Computing compelling recommendations profitably reshapes human behavior.
The influence and purpose of recommendation engines are not limited to customers or consumption. Large employers, most notably Google, have adopted and adapted recommendation engines as internal productivity platforms to nudge workers to their best decision options. Indeed, in late 2016, Laszlo Bock, the senior vice president of people operations at Google, left the company to launch Humu, a recommendation-engine startup for advising workforce behavior change.