How AI-driven offers and experiences can transform publishing

How AI-driven offers and experiences can transform publishing

News publishers face unprecedented competition, rapidly changing consumer behaviors, and technological disruptions. Leveraging advanced technologies such as Artificial Intelligence (AI) and Reinforcement Learning (RL) has become essential for achieving more predictable outcomes. With RL, outcomes are decisive and established upfront. As a result, revenue increases, time to value shortens, and customer effort decreases. These advantages enable companies to navigate the complexities of the market more effectively, ensuring they stay ahead in an increasingly dynamic environment. 

 

With insights from industry experts Andreas Martin and Jonathan Harris, this article explores how AI-driven offers and experiences can revolutionize the way media companies align with key trends and market demands.

 

The evolving media landscape

“We are in the midst of a market shakeout,” explains Andreas Martin, Senior Director and Solutions Lead for B2C and Media at Zuora. “There was massive success with the shift to the Subscription Economy, and now we have reached a point where things are a lot more competitive. Everyone is competing for the same thing: people’s time, attention, and share of wallet.”

The proliferation of subscription services has led to market saturation, where companies must compete for the limited time and attention of consumers.

Traditional publishers now face competition from a myriad of digital content providers, including streaming services and social media platforms. This shift from a supply-driven to a demand-driven market necessitates a new approach to customer engagement and retention​​​​.

To remain competitive, media companies must focus on personalization and relevancy.

“It’s about personalization.” Andreas emphasizes, “It’s also about bundling and unbundling to find the package that works for your subscribers.”

Consumers expect tailored experiences, which requires leveraging vast amounts of data to understand their preferences and behaviors. Because of this better understanding of consumers, there’s now a push toward bundling and unbundling content to meet their diverse needs.

Why should media companies pay close attention to this? Because customer retention is paramount in the recurring revenue model. A staggering 70-80% of annual recurring revenue for most businesses stems from existing subscribers. This means that to maintain market leadership, companies must prioritize retention strategies just as much as customer acquisition. Each hard-earned subscriber is a valuable asset, and fighting to keep them engaged and satisfied is crucial for sustained growth and success.

To have staying power, media companies must embrace AI to have the agility needed to meet ever-evolving consumer demand.

 

The role of AI in transforming news publishing

The promise of AI to up-end curation, production, and the customer experience within media will challenge existing orthodoxies, improve efficiencies, and create new products. AI technologies, particularly reinforcement learning, offer powerful tools for media companies to enhance their operations and drive better outcomes. Reinforcement learning, unlike traditional propensity scoring, provides a dynamic and adaptive approach to decision-making.

 

Reinforcement learning vs. propensity scoring

Propensity scoring involves using historical data to predict future behaviors. While useful, it is inherently static and cannot adapt to real-time changes in the environment, says Jonathan Harris, founder and CEO of Sub(x), a marketing technology company recently acquired by Zuora.

“Reinforcement learning, however, allows the agent to adapt to the environment, making decisions about what it should and should not do to achieve the desired outcomes,” he adds.

This adaptability is crucial for responding to changes in audience behavior and market dynamics.

Jonathan outlines the three key components of reinforcement learning systems: policy, feedback loop, and cumulative reward. 

“The policy defines what you want the agent to achieve, such as increasing average revenue per user,” he says. “The feedback loop provides continuous updates on the effectiveness of the actions taken, while the cumulative reward ensures the system is constantly striving to achieve the set goals.”

 

Why manual testing is being replaced by self-learning AI

Traditional manual testing methods also are becoming obsolete in favor of AI, because they’re:

 

  • Too time-consuming – In manual testing, marketers segment customers, run tests, and analyze results. This time-consuming process often lags behind fast-changing markets making results outdated.

 

  • Prone to bias and error – Manual testing for simple choices is adequate, but today’s marketing decisions have many variables, this leads to overwhelming combinations and often results in less effective guess work.

 

  • Not truly personalized – A/B tests often overlook minority preferences, and segment-based testing doesn’t fully utilize the abundance of available first-party data, leading to decisions that are never really personal. More on first-party data in a moment.

 

If a business still relies on outdated A/B testing and propensity scoring to drive revenue and acquisition growth, it risks falling behind as these methods struggle with complex challenges and large data sets. It is highly recommended that a best-in-class, latest technology Automated AI alternative be considered, provided by the people with the highest level of expertise and domain experience.

 

Leveraging AI for better outcomes

Publishers need to embrace rapid experimentation to identify the highest converting offers and experiences. AI can automate this process, allowing companies to test and refine their strategies quickly and efficiently.

“There is no set-and-forget anymore,” Andreas says. “Continuous learning and optimization are essential for success.”

The shift toward first-party data is driven by increasing regulatory scrutiny and changing consumer preferences. AI can enhance the value and impact of this data by providing deeper insights into customer behaviors and preferences.

“Reinforcement learning can coordinate first-party data in a way that propensity scores cannot, using data at a very granular level to extract more value,” Jonathan says.

Also, creating frictionless subscriber experiences is essential in today’s market.

“Consumers only have one benchmark,” Andreas says, “Everyone has their favorite streaming service, and that sets the standard for their expectations.” AI can help streamline these experiences by automating various aspects of customer journey management, from personalized recommendations to dynamic pricing and content delivery.

 

The strategic imperative of AI in media and entertainment

The integration of AI into the media and entertainment industry is not just a technological shift but a strategic necessity. As Andreas Martin emphasizes, “The only way to be successful is to constantly iterate,” highlighting the need for continuous improvement and adaptation in a dynamic market.

AI systems, particularly reinforcement learning, offer significant advantages by adapting and optimizing operations. These systems are exceptionally well-suited for the ever-changing media landscape. By enhancing personalization, streamlining operations, and driving continuous improvement, AI enables media companies to position themselves for long-term success, staying ahead of the competition and delivering greater value to their customers.

For media and entertainment companies, the adoption of AI-driven offers and experiences is crucial to thrive in a demand-driven market. Reinforcement learning provides a dynamic approach to decision-making, allowing companies to refine strategies and achieve better outcomes. By focusing on rapid experimentation, leveraging first-party data, and enhancing subscriber experiences, these companies can remain competitive and innovative.

As the industry evolves, AI will undoubtedly be a key driver of innovation and growth. Zuora is pleased to announce the acquisition of Sub(x), accelerating subscriber acquisition and retention with new AI capabilities for the media industry. By embracing these technologies, media and entertainment companies can meet and exceed the ever-evolving expectations of their audiences, ensuring sustained success in the subscription economy.

Recommended for you

From data to dollars: Practical tips to make AI work for you
Preparing for the next IPO wave: Essential steps for exit readiness
Genesys forges a path to GenAI innovation and monetization