Business Trends in Practice by Bernard Marr

Author:Bernard Marr [Marr, Bernard]
Language: eng
Format: epub
ISBN: 9781119795582
Publisher: Wiley
Published: 2021-09-30T00:00:00+00:00

Inspiring Examples of Personalized Services

Two potent examples of personalized services are Google's personalized search function and Netflix's personalized content recommendations. Both of these are driven by recommendation engines.

A quick look at recommendation engines

A recommendation engine is any system that suggests information, products, and services based on data about the user (age, gender, etc.), past activity (such as ratings, reviews and purchases), and the behavior of similar users (“people who bought X also bought Y”). Recommendation engines are an incredibly efficient way for businesses to provide customers with personalized products, services, and information – and generally provide a better, more meaningful user experience.

Based on the amount of data Google now has about most of us, it can go way beyond a traditional web search engine (which yields results based on web ranking factors) and instead functions more as a recommendation engine, by serving up results based on what it knows about you. That means that if you and I both searched the same keyword or phrase on Google, we would see quite different results come up, because Google bases its results on factors like demographics, location, interests, and search history.

Netflix is another great example of this in action. When you log into Netflix, the system serves up a handy selection of content that you're likely to enjoy, based on things you've watched previously (and whether you watched it all the way through), and what similar viewers have enjoyed. Netflix even personalizes the thumbnail images that you see for each series or movie – in theory, showing an image most likely to grab your attention. Take the movie Good Will Hunting as an example. If you watch a lot of comedies, you might be presented with a thumbnail showing Robin Williams. But if you're more of a romance fan, your thumbnail might show a still of Matt Damon and Minnie Driver gazing lovingly at each other. (This has landed the streaming giant in hot water in the past for allegedly serving up thumbnails based on ethnicity – for example, showing a black viewer a thumbnail with a black character, who it turns out has only a minor role in the movie.)5

But there's no doubt the recommendation engine helps Netflix deliver an experience that's personalized to each user. And it works – 80 percent of Netflix content watched is discovered through the recommendation system.6 According to estimates, this saves Netflix around a billion dollars a year in cancelled subscriptions because, well, fewer people want to cancel.7

Let's explore some other examples of companies successfully deploying personalized recommendations and services.

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