In the media industry, content is no longer king. Content delivered in the right context is now king. We need to deliver the right content at the right time to the right person.

The dialogue between a news media company and its readers has to change from a blanket broadcast to the masses to a tailored conversation with the individual.

How can we create content and products that replicate natural conversation? If journalists had the ability to sit down and have a conversation with each of their readers, how would they adapt the conversation in order to best challenge, entertain, and enlighten them, based on their level of knowledge on different subjects?

Personalisation is all about improving the user’s experience of the product by adapting the product based on what we know about the user.

What do we know about the user?

There are an endless number of signals that can feed the personalisation of content. There are both implicit and explicit ways of gathering information about a user and how he uses the product. It all depends on how much the user is willing to share with us:

His age, location, sex, race, academic background, reading level, income level, and marital status.

His content browsing history, how much he knows about the story or topic, his mood at the time of reading, political affiliation, the device he is using, and the network he is browsing on.

His religion, referral site, preferred article length, the amount of devices he uses during the day, the time of the day, and the content his friends are reading. The content he has liked, shared, and commented on.

His health, shopping habits, fitness level, sleep pattern, and whether he is bored (scientists can now predict this).

The onslaught of sensors and new devices opens up for hundreds of signals that can potentially be used to improve the user’s experience of the product.

Personalised front and personalised content

The two most popular ways of personalising content based on these signals are in creating personalised feeds and personalised stories.

Personalised feeds

The most common form of personalisation is through algorithmically generated feeds created by different signals known about the user. This form of personalisation focuses on the problem of information overload and using a site’s real estate in the most effective manner possible to create a feed of content with high relevancy for a user.

Social media sites are the ultimate example of personalised feeds.

The last few years has seen a gradual transition from active consumption of news content through traditional media Web sites to more and more passive news consumption through social media Web sites.

News is finding the reader rather than the reader finding the news. Friends are acting as an extra filter for a lot of the news content people consume. A socially filtered recommendation from trusted friends helps people find relevant content with minimal effort.

People are also no longer just passive consumers of content but are also producers, curators, contributors, and commentators of content through their social media accounts. The mix of content sources, type of content, and commentary on shared content creates a socially curated, personalised content stream with high relevance to the user.

Media companies in general have been slow to adapt to this trend. Others have embraced it:

• The Nuzzel app is an example of an app offering a social filter, choosing to aggregate and rank popular content from a user’s social media account.
  
  
• The Daily Beast app uses gameified dashboards displaying analytics about stories read and skipped to try and increase user engagement with its product.
  
  
• The BBC has been working on a personalisation project called myBBC and already has personalised content available through the myNews stream in the BBC news app.
  
  
• The New York Times has created a recommendations page based on the reader’s browsing history on the New York Times Web site.
  
  
• The NPR One app promises to connect a user to a stream of public radio news, stories, and podcasts curated just for him.
  
  
• Google News and other aggregators offer personalised streams based on both explicit decisions made by the user and implicit data recommendations based on context and behaviour (Aggregator apps: Are they a friend or foe?).
  
  
• Flipboard allows users to create their own curated magazines and share these magazines with their friends.

Other examples of personalisation are personalised newsletters offered by the Huffington Post, personalised push messages like the content delivered by the Breaking News app, and the ability to follow stories, authors, or content tags.

Personalised stories

Personalised stories try to adapt both the content and the presentation of a story with what we know about the user. These create different paths for different people through the narrative of a story.

Many companies have highlighted the demise of the article and the transition to producing smaller, reusable, structured content chunks that can be combined in different ways to create different narratives based on what we know about the user. Circa had atoms, Vox has cards, and The New York Times are working on particles.

This approach can not only improve the efficiency of the newsroom by reducing the overhead of using duplicate content in different articles about the same subject, but it also allows for the development of personalised stories by combining atomic content units in different ways for different people.

The majority of media houses have developed interactive articles that use signals either implicit or explicit from readers to adapt the content to the reader. Here are some of the examples:

• To highlight the aging fleet of fire brigade trucks in Norway, VG created a dynamic article that showed the fleet of fire brigade trucks at the location the user chooses within the article. Readers could also contribute to the article by uploading private images of the fire brigade trucks.
  
  
• The New York Times article about the best and worst places to grow up, published in May 2015, uses location data to personalise the article map and text based on the user’s location.
  
  
• As part of a series of special feature articles, VG asked people to contribute their personal stories of family members who died in World War II.
  
  
• Aimed at highlighting the debut of the youngest ever Norwegian soccer player Martin Ødegaard at the age of 15, VG created a dynamic article where users could enter their names and ages to find out who they would have played with if they had their national soccer team debut when they were 15 years old.

This is just the start of the development of personalised stories.

The next major breakthrough in personalised content will be through the use of artificial intelligence and machine learning to generate content.

Within the media industry, the technology is now mainly used for content discovery and curation where machine learning is used to find trending topics or suggest meta-data tags for content. Once the meta data and number of signals we have improve, machine-generated content will become more of a viable option.

When content can be automatically generated and personalised for each individual, it opens up opportunities to create thousands of personalised articles that never would have been possible without the use of AI and natural language generation.

The technology has for a long time been used by trading desks to automatically buy and sell shares. The Associated Press uses it to create 3,000 financial stories per quarter. Forbes has used it to create market reports such as this earnings preview, and it has been used to draft report cards for Yahoo’s fantasy football league players.

Another often quoted example is the generation of Little League baseball content through the use of natural language generation. A machine creates stories outlining the events of Little League games in an exciting manner based on data about the games. These are games that otherwise would never have been written about if they had to be covered by humans.

Two of the companies working on these solutions are Narrative Science and Automated Insights.

Concerns

Trust is an essential part of any serious news site’s value proposition to its readers. You can trust us to give you a factual and balanced view of what’s happening in the world.

When delivering personalised content, this trust now needs to be extended to the personal information shared by the readers. The readers need to trust the news site not to do anything creepy with their information.

The return on investment needs to be apparent, how the data is being used needs to be transparent, the user should be able to easily opt-out, and the privacy policies and terms of conditions need to be simplified and readable without the need for a law degree.

Tim Cook was recently very critical of other companies’ approaches to data privacy and security with Apple taking the approach that users should be in control of their own data. Upworthy’s privacy policy is a good example of a human readable privacy policy.

There are multiple examples of companies overstepping the creepiness mark. The classic example is the U.S. retailer Target and how it predicted when a customer was pregnant based on her purchases. It then sent her promotional material based on this data.

Another example is Facebook’s mood experiment, which oversteps the mark.

There has also been concern that personalisation can help reinforce homophily and create echo chambers, filter bubbles, and a Tyranny of the Majority by prioritising what is popular and potentially exposing people only to the news and opinions that they already agree with.

The challenge is to present the user with a mix of what the editors think the user should know with what we know the user is interested in. News media companies have a responsibility to challenge people’s views and give people a wide angle view of the world.

The advent of more and more algorithmically steered content means media and journalism ethics and responsibilities now need to be extended to the data scientists, designers, and software architects designing the algorithms populating personalised feeds and stories. They need to be aware of the potential pitfalls and design systems to safeguard against them.

Another concern with the personalisation of content has been with how it will affect the brand of the company. In many instances, the manual curation of content broadcast to everyone represents the brand of the media company. The brand identity will need to adapt to the new world of personalised content.

Conclusion

We are living in an era of information overload. We have moved from a time when a few media companies produced and distributed content to an age where everyone is a content producer and can easily distribute that content to a global audience. The need for a trusted, respected filter for this vast amount of information is needed now more than ever before.

The idea of broadcast news is a legacy feature that is from the era of printed newspapers, where the cost of delivering a personal magazine for each user was impossible. Identity-driven, digital distribution allows us to tailor the news for each person. Social media sites have made personalised experiences the norm.

Media companies have dragged print legacies into the digital world and need to pivot quickly to survive.

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