With today’s generations expecting relevant content from their experiences, it’s up to news publishers to employ personalisation tactics that increase engagement and drive business success. As such, personalisation has become a key tool for digital publishers looking to improve engagement and drive business success.

Considering this, it’s important to look at the intersection of personalisation benefits for digital publishers, the role of AI, and the future of custom experiences.

The push for personalisation

Personalisation is increasingly becoming a crucial success factor for digital publishers. A study found 77% of Gen Z believe it’s important for B2C businesses to customise interactions, and 76% are looking for B2C companies to send digital communications they can customise based on their own preferences.

With these stats in mind, we can see why personalisation has garnered so much attention, allowing publishers to deliver highly relevant content to their audience, which leads to increased customer satisfaction and loyalty.

But to have truly tailored experiences, publishers need to know their audiences. This is where striking the right balance for data collection becomes essential.

It starts with the data

Even the most advanced personalisation strategy requires data from users to make predictions. But when collecting that data, publishers might want to evaluate how intrusive their collection process is.

Progressive profiling offers a way to collect data from users in a gradual way, rather than asking for a lot of information all at once. This helps to build trust with users and ensures the overall data collected is detailed and relevant without feeling intrusive.

“(Progressive profiling) means your users can immediately engage with your product, experiencing it first before you overload them with questions,” explained Martin Gontovnikas, co-founder and GTM advisor for HyperGrowth Partners.

As with all data collection, progressive profiling should be done in a way that respects the user’s privacy. The user should be informed about the data being collected and have the option to opt out. Being transparent with this information is beneficial as it builds trust and makes users less likely to feel uncomfortable about sharing valuable insights.

The AI revolution

Artificial Intelligence (AI) has the potential to revolutionise personalisation by using algorithms to analyse large amounts of data and automatically make predictions about which types of content a particular user is likely to be interested in.

There are several different types of AI algorithms that can be used to deliver personalised content recommendations. These include:

• Collaborative filtering: This uses user behaviour data such as browsing history to identify patterns in the content that users are engaging with. It then uses this information to make accurate and automatic predictions by recommending content that is similar to what the user has engaged with before.
• Content-based filtering: This uses information on the content itself, such as keywords, topic tags, and themes, to make predictions. For example, does a particular user tend to read more articles with political keywords, themes, or tags?
• Hybrid filtering: This is a combination of the two above algorithms, garnering even more accuracy for future predictions.

So, there are multiple opportunities where AI can help with personalisation. But is it the only way forward?

Should we rely on AI?

While AI may very well be the future of personalised content recommendations, it can be expensive to implement and difficult to train and implement across teams.

The research report People Plus Machines by Frontier and the Publisher’s Association found large publishers face common barriers to AI, including a lack of skills and difficulty applying AI solutions to existing infrastructure. With external skills and technology required to overcome these challenges and onboard AI solutions, it can be costly.

So, for many, it may be worth considering alternatives in the meantime in order to implement personalisation more quickly. Building a best-of-breed tech stack offers an alternative that allows publishers to communicate with a range of technologies in order to handle data, segment audiences, customise experiences, and make intelligent use of insights.

With the ability to swap in new tech at any moment, a best-in-breed tech stack may offer another option until AI becomes more attainable.

Future implications

Personalisation is a powerful tool for digital publishers looking to drive business success. As part of that, AI has seen a rise in recent years, specifically for its use in analysing vast amounts of data on user behaviours, preferences, and interests.

And, as AI is continually advancing, news publishers may want to consider future-proofing their business to keep up. Taking a forward-thinking approach to a tech stack will ensure they’re able to meet current personalisation needs and be ready for new AI developments as they evolve in future.

While AI models are likely to become more mainstream, it doesn’t take much to get started with some form of personalisation. Publishers who start sooner with simpler tools will likely gain a head start over competition.

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