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Personalization is set to be the key to marketing success over the coming years. Advances in technology are driving more human experiences in the consumer web space, and with a surge in digital behaviors post-pandemic, it’s vital that organizations adapt to the growing trend for personalized interactions. 

Content recommendation is one branch of the personalization family tree. At its simplest, general recommendations can be offered to users based on the popularity of content. At a more complex level, AI-powered content recommendation engines can be trained against product catalogs and customer/consumer/user data to provide more personal recommendations.

What is a content recommendation engine?

A content recommendation engine is a software solution which leverages artificial intelligence and machine learning technology to analyze data, in order to provide more personalized user experiences on a website or app. 

Advanced engines are no longer under the auspices of “big tech” companies, like Google, Amazon, Netflix or other market leaders. Now, third-party recommendation solutions enable businesses to make full use of their site’s data, providing personalized content or suggestions which achieve higher retention, consumption, or conversion.

What is content recommendation in media?

Content recommendation systems in media provide personalized content for users and subscribers based on consumer data and what’s trending. Personalized recommendations can be applied to video and music streaming sites, publishers, social media networks, and other media and news organizations to create relevant experiences that engage users and increase time spent on a site or app.

An in-store bookshop will make the point of employing staff who are sufficiently educated in literature or broadly well-read. On this basis, a sales assistant can make recommendations based on a customer’s book preferences, or point a customer standing in the “fiction” aisle to similar, more popular, and even more relevant content offerings. 

Online, algorithms replace the role of a knowledgeable sales assistant, providing real-time recommendations based on consumer data, creating a personalized experience for the consumer.

Why are media content recommendations important?

A study in the “Four Fundamental Shifts in Media & Advertising During 2020”, undertaken by DoubleVerify, found that content consumption is soaring, with 47% of consumers spending more time reading online news and an equal percentage increasing their use of video streaming services. 

The surge in content consumption presents an opportunity for digital media businesses to leverage the power of content recommendation algorithms and engines which drive user engagement and loyalty in a crowded marketplace.

How does content recommendation in media work?

Recommendations work via algorithms, typically AI-driven, which leverage user data to optimize and personalize content suggestions. The suggestions will be based on cookie data collection and metrics such as age, sex, and other demographic information, along with past viewership and search history. 

Recommendation engines’ algorithms will operate on various models, providing:

  • Popular content: popularity-based algorithms offer up content based on what’s popular or trending. If a particular piece of content (e.g. an article or video) is gaining traction, it will be offered up to other users. These algorithms ensure a trending piece of content “rides the wave” of its success, taking advantage of its popularity and building upon it for a greater reach with audiences and website or app visitors.
  • Associated content: association-based algorithms evaluate the strength of the relationship between various pieces of similar content. If an affinity exists between two news articles, for example, with most users reading both, this data can be used suggestively, prompting other users to consider the second option. These prompts exist across websites under headings like “customers also read these”, or “view more like this”.
  • Historical content: content-based algorithms look at the similarities between the types of content a user has accessed in the past to prompt present-future recommendations. With loyal and recurring users, a complex user preference profile can be established, based on likes, dislikes and consumption patterns. Classifications such as genre and format (“horror”, “tv show”) narrow down the preferences of a given user, along with time-consumption data and more. All of this data provides users with a more personalized media offering. This reduces  the complexity of their content search and discovery experiences, and enables more pertinent, valuable interactions that result in greater efficiency, engagement and retention.

The benefits of content recommendation in media

The benefits of content recommendation in media are plentiful. Here are just a few:

  • Consumer retention: A successful content recommendation engine will simulate the experience delivered by a high-performing sales assistant. Suggestions will be knowledge-driven, relevant and appeal to the specific tastes of a user. A successful suggestive path (e.g. a trail of recommendations which are acknowledged by the web viewer) will create high retention on a site or app. 

Personalized recommendations can be the difference between a  consumer engaging for two minutes or 45. Customization and personalization is embedded into the product offerings of all leading technology companies today, so having these features is necessary to compete in the modern marketplace.

  • Customer & consumer loyalty: Establishing brand loyalty requires trust. A consumer’s time is valuable and if recommendations for content are sub-par they will switch to a competitor which “knows them better”. 

With advanced content recommendation engines, a business ensures that a consumer’s time spent on their product  is meaningful and optimized, delighting them and helping to build a loyal reader or viewer.

  • More consumption & conversions: Personalized recommendations mean more conversions. On video streaming sites, such as Netflix or YouTube,  data-driven recommendations can prompt conversions to a higher-value subscription package (“if you want to watch this movie, or customize your profile, you’ll need to buy this package or tier”). 

For online news sites, whose main source of revenue comes from advertising or subscriptions, consumer engagement creates a stickier, higher-value site. This helps fuel the advertising business model, and additional recommended content, after X free pieces help drive greater subscription adoption. 

The power of personalization

To realize the power of personalization, you don’t have to look far. Content recommendations are now “the norm” on media properties and platforms and, as outlined in a Twilio Segment Report, provide a key indicator of brand success. In 2021, 60% of consumers say they will likely become repeat buyers after a personalized site experience, up from 44% in 2017. 

However, less than a quarter of businesses have adopted such solutions, meaning there is a real gap between consumer expectations of personalization and the user experience offered  by most businesses.

Third-party content recommendation engines, such as Algolia Recommend, provide a solution for businesses who want to leverage the power of an advanced recommendation engine that would put them on par with the likes of major tech leaders. 

With Algolia Recommend, developers can use a simple API to build AI-structured recommendations on a media site or app using as little as six lines of code. Closing the gap between what a consumer expects and how a site performs is crucial for businesses to retain users, build brand loyalty, and drive revenue. In a saturated media market, it’s time to get personal – and fast. 

If you’re looking to engage consumers with more personalized content, get in touch with the Algolia team or request a free demo.

About the author
Vincent Caruana

Senior Digital Marketing Manager, SEO

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