How media publishers use AI to boost reader engagement

Quality content is no longer enough to build and maintain a loyal readership and publishers know this best.

To gain a sustainable competitive advantage, media publishers must leverage tactics that enable a fully personalized experience that captivates readers’ attention.


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That’s why publishers are in a prime position to reap the benefits of AI.

They have the data and they have the ecosystem. All they need is the right AI platform to deliver the industry’s holy grail: dynamic customized experiences that keep readers coming back for more.

The best part? It also works for publishers who don’t rely on a subscription model to generate revenue.

Learn how to enhance your recommendation systems using AI to deliver content your readers truly enjoy and crave.

Top 10 challenges in media publishing today

  1. Readers’ short attention span
  2. High competition for readers’ time because of low barrier to entry in certain verticals
  3. Little to no reader loyalty for a specific publication
  4. Increasing traffic numbers and traffic quality
  5. Low engagement favored by passive content consumption
  6. Increasing fragmentation in content sources due to channels and devices
  7. Social media as a preferred channel for content recommendations
  8. Fluctuating ad revenues
  9. Difficulty to make accurate recommendations based on readers’ preferences
  10. Balancing automation with human connection and real experiences

79%

of the US population has a social media profile (Statista)

50+ times/day

Is how often Gen Z Americans access apps (Trends in Consumer Mobility Report 2018)

Why we’re on a mission to help publishers achieve more with their existing data

We see access to quality information as a key human right.

With so much of our education happening online, we want to help media publishers connect with the people who can use their ideas to become better and improve the world around them.

We believe AI complements and amplifies human expertise, creativity, and judgement.

This is why we’re building AI applications that integrate into existing business flows and don’t require Machine Learning specialists to operate

Our goal is to help ecommerce extract more value from their existing data

Unify data sources & eliminate data silos

Extract actionable data about readers

Strengthen data integrity

Improve user recognition

Automate data prioritization to generate insights

Ensure compliance with data protection regulations

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How to use AI to grow your readership & engagement

Thinking of AI as a magic wand to fix all the issues media publishers face is unrealistic. However, infusing AI into the newsroom’s workflow can boost efficiency for core KPIs and free up resources for other essential projects.

Working hands-on with AI proves it excels at 3 essential tasks:

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Process a large volume of signals

We believe the users' activity (ex. sessions, page views, time on page, searches, etc.) and history are the most relevant indicators for their probability to churn.

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Extract insights

MorphL uses content consumption patterns to correlate readership KPIs with the risk of churning. We focus on dynamic customized experiences that keep readers coming back for more.

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Automatically tailor digital experiences based on insights

You can apply this key competitive advantage to optimize readers engagement and to make better decisions - often automatically.

For media publishers, MorphL plugs into data sources and aggregates them into a tested model that delivers reliable insights about readers and their habits.

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What MorphL delivers: Churn prediction


Even for non-subscription-based publishers, MorphL provides insights such as:

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How you can use it

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Increase retention by addressing cohorts at a high risk of churning

✓ recommend top performing content that matches their interests
✓ incentivize engagement & community involvement
✓ provide social proof with experiences from users like themselves

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Determine the best and worst performing content formats for specific traffic sources

✓ suggest content that matches the average time spent reading
✓ add a “Read it later” option and engage readers via email
✓ surface potentially engaging comments to stimulate participation

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What makes AI different from rule-based systems

Rule-based systems
Learning systems (AI)
Use static if-then-that rules
vs.
Can create, discard and change rules based on what they learn
Knowledge is encoded as fixed rules
vs.
Knowledge changes through learning (adaptive intelligence)
New situations that require new rules get the system stuck
vs.
The system can adapt to new situations by creating new rules
The larger the data pool, the bigger the risk of introducing conflicting rules
vs.
The larger the data pool, the better the system becomes by learning from specific situations

Get better at understanding readers' behavior

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Data-informed decisions

Make data-informed decisions and design (re)engagement strategies to drive more quality traffic and keep readers engaged.

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We're here to help!

We’re here to support you with knowledge, guidance, and AI expertise so you can make the most of MorphL and support business growth.

Frequently Asked Questions

Need answers? Have any other questions, please get in touch.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779790.