Table of Contents
How AI-driven offers and experiences can transform publishing
How AI-driven offers and experiences can transform publishing
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08 Minute Read
The evolving media landscape
News publishers face unprecedented competition, rapidly changing consumer behaviors, and technological disruptions. Leveraging advanced technologies such as artificial intelligence and reinforcement learning has become essential for achieving more predictable outcomes. With reinforcement learning, 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 chapter explores how AI-driven offers and experiences can revolutionize the way media companies align with key trends and market demands.
“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.
This includes offering personalized experiences, flexible bundling options, and – critically – adopting the right monetization strategy. Publishers must grapple with crucial decisions regarding paywalls, balancing immediate revenue generation with long-term subscriber retention.
The paywall dilemma: finding the right balance
As publishers navigate this evolving media landscape, a critical decision emerges: to paywall or not to paywall? While a hard paywall may seem like a quick way to generate revenue, it can also lead to customer churn later down the line.
The key is to find a balance between generating revenue and retaining customers. This requires a deep understanding of your customers and their needs, and testing and experimentation to find the right paywall strategy for your business.
One way to strike this balance is to use a dynamic paywall. This type of paywall can be adjusted based on the user’s engagement level. For example, you could offer a free trial period or allow users to access a certain number of articles for free before requiring them to subscribe.
Another option is to use a freemium model. This model offers a basic level of service for free, with the option to upgrade to a premium level for a fee. This can be a good way to attract new customers and give them a taste of what you have to offer.
The right paywall strategy will depend on your specific business goals and audience. However, it’s important to consider the potential impact on customer churn before making a decision.
Moreover, the effectiveness of any paywall strategy can be significantly enhanced through personalization. By understanding individual user preferences and behaviors, publishers can tailor paywall experiences to maximize engagement and conversion.
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.
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.
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 & 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.
However, AI is just one piece of the puzzle. To truly thrive, publishers must also strategically manage and harmonize multiple revenue streams.
Multiple revenue streams: finding synergy
In this complex media landscape, businesses often have multiple revenue streams to manage. These streams can include advertising, subscriptions, e-commerce, and affiliate marketing.
The challenge is to find a way to align these revenue streams so that they work together to support your business goals. This requires a holistic approach that considers the needs of all stakeholders.
Data is essential for understanding how your revenue streams are performing and identifying areas for improvement. By tracking key metrics, you can see which streams are generating the most revenue and which ones need attention.
Testing and experimentation are also important for finding the right balance between your revenue streams. This may involve adjusting your pricing, offering different subscription tiers, or changing your advertising strategy.
This strategic management of revenue streams, combined with the power of AI and a customer-centric approach, will enable media companies to navigate the complexities of the subscription economy and achieve sustainable growth.
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.
While AI can enhance individual experiences, bundling services is another powerful tactic to increase the value perceived by customers and keep them engaged for the long term.
