The battle for viewers and user loyalty in the OTT industry is a relentless one, as streaming service options nowadays are myriad, and switching from platform to platform is mere clicks away. The industry continues to face a steady rate of churn on OTT streaming platforms, with subscribers periodically canceling their subscriptions and exploring alternatives. This ebb and flow of users keeps industry players on their toes.
A poor user experience, technical issues, or a lack of personalized content recommendations can very quickly drive users away from a platform. Unlike the fierce competition out there, these factors are controllable by OTT service providers, as they can identify and improve the key UX metrics that affect viewer retention, or further develop their content discoverability features for effectively reducing churn rate. At the same time, it is vital to always stay ahead of the game, constantly experimenting with new technologies such as AI and Machine Learning (ML) to analyze user data, predict churn, and personalize content recommendations in a bid to reduce attrition and foster long-term viewer loyalty.
In this blog, we will take a deep dive into the possible applications of AI and ML on OTT platforms, which can help service providers conquer the challenging quest of retaining subscribers.
ML is a subset of AI that is focused on training computers to learn, adapt, and make decisions by recognizing patterns and drawing insights from information or data, without explicit programming. ML powers and enables AI to perform tasks that mimic human intelligence and continually improve its performance through data-driven learning processes.
With the help of ML in analyzing vast datasets, including viewing habits, content preferences, watch history, and even user feedback, OTT platforms can now be equipped with an unprecedented level of invaluable information and insights about their users.
One of the highlight features of ML is the ability to predict user behavior - identifying those at risk of canceling their subscriptions based on historical behavior and engagement patterns. Such information is then used to calculate and determine a likelihood score of churn for each user in the future, allowing service providers to group users into risk categories and then focus their retention efforts on those at higher risk.
ML programs can also pull out and highlight the contrasting behaviors of loyal users and those at risk of churning. This empowers OTT businesses to concentrate on the metrics and experiences that have the most significant impact on their bottom line. For example, do loyal users tend to consume more live content, series, or movies? What content genres do users who are least likely to churn prefer? Equipped with this knowledge, businesses can make well-informed, data-driven choices about their future content acquisitions.
By analyzing things like how often users log in, what they watch, and how they interact with the platform, ML systems can also categorize users into distinct groups based on shared characteristics and viewing behaviors. As a result, the content recommendations are effective and highly personalized, improving user satisfaction. As user behavior changes, the system also continually learns and adapts, making it a valuable tool and a worthy long-term investment to reduce churn.
Approaching users with high-risk of churn
As discussed above, AI-driven predictive analytics excel in recognizing signs of potential churn, such as declining activity or shifts in content preferences. By being able to pinpoint users who need extra attention, providers can take action before they come to the decision to leave. For instance, if AI forecasts that a user is nearing churn, it might be strategic to offer them a discounted subscription or grant access to premium content at no extra cost to retain their viewership.
In addition, personalized content always plays a big role in OTT user retention strategy, and AI and ML can certainly bring the impact of it to another level. The AI algorithms offer individualized, real-time, and context-aware recommendations, adapting to each user's unique behavior and preferences. It recognizes complex patterns, scales efficiently, and enhances real-time user engagement. Traditional data analytics models, while valuable for broader insights, may not provide the same level of personalization and agility. By responding quickly to the shifted behaviors of high-risk users, and offering content that aligns with their preferences and interests, providers can re-engage these subscribers and keep them interested in the platform.
Creating targeted marketing campaigns
With the insights extracted by AI/ML applications, it is not only content recommendations that can be personalized, but marketing approaches and messages can also be tailored to individuals or groups of users.
Apart from the “high churn risk” club, there are many other ways for you to segment your subscribers into behavior- or preference-based groups, such as "genre enthusiasts," or "content bingers." These distinctive segments make way for unique and targeted marketing campaigns. For instance, “genre enthusiasts” may receive content recommendations tailored to their interests: if a user consistently watches sci-fi series, he or she will get notifications for new sci-fi releases or suggestions for related genres to explore. These responsive campaigns are designed to resonate with users, enhancing their experience and ultimately, reducing churn.
Leveraging dynamic pricing
Price sensitivity is a reality in the OTT industry. Amidst the current macroeconomic climate, an increasing number of users assess their subscriptions' value relative to the price they pay on a frequent basis. If the price becomes a pain point, they may consider canceling. ML programs can help OTT platforms optimize their pricing strategies in a dynamic fashion. In a scenario when a user frequently interacts with content but has shown sensitivity to price changes, the system might adjust subscription prices or offer him a limited-time discount or a tailored pricing plan to retain their loyalty. These AI-driven pricing adjustments are designed to ensure users continue to perceive the subscription as valuable.
It is widely acknowledged that good customer service and support are crucial factors in deterring OTT users from churning by providing a lifeline for those facing issues or dissatisfaction. By offering solutions, reassurance, and a personalized experience in a timely manner, OTT service providers can turn frustrated users into satisfied, loyal subscribers. The challenge here lies in ensuring timely and responsive customer support, a puzzle to which conversational AI provides a compelling solution.
Conversational AI, powered by Large Language Models (LLM) such as GPT, is distinguished by its capacity to analyze, comprehend, and process information, facilitating lifelike interactions between humans and machines via text or speech. More than just resolving issues, it helps forge real-time connections.
In the OTT world, streaming platforms can utilize conversational AI to interact with users at crucial moments. With its help, users can easily navigate the platform, discover new content or quickly get answers about their subscriptions. AI chatbots are the heroes in providing fast solutions to billing or technical problems - which are major causes that lead to involuntary churn. Fast and hassle-free support that meets and exceeds customer expectations is a real competitive advantage that will make you stand out among other OTT platforms in the market. It is worth noting that, in addition to catching the right moments for interaction with subscribers, the use of conversational AI is also beneficial for cultivating an ongoing engagement model. It sustains the dialogue, assesses customer contentment levels, and collects valuable feedback. This individualized and cost-efficient approach plays a pivotal role in mitigating OTT churn rates.
With its great power comes many challenges. Regardless of the tremendous benefits they bring to the streaming platforms’ UX and performance, adopting AI/ML applications is often not an easy decision without concerns. One of the most troubling issues when it comes to such heavily data-based technologies is privacy and security, as questions regarding the collection and handling of customer personal data are serious and sensitive ones. It can also be extremely difficult and complex to build and run AI-powered platforms if you don’t possess relevant expertise and technical knowledge for the tasks. Not to mention that such technologies are very costly to implement if you don’t already have the right infrastructure or tech stack to support them, in which case smaller OTT providers may be at a disadvantage position compared to the bigger, more established ones. However, it is undeniable that the adoption of AI will be the future and soon the norm for OTT services that want to grow and thrive. AI offers substantial benefits to OTT platforms, and if we tread carefully in addressing these challenges, not only can we ensure their successful implementation and long-term sustainability, but also build strong and enticing video experiences that keep viewers stay.
If you would like to know more about how you can use AI to improve viewer retention on your OTT platform and reduce OTT churn, Accedo experts can help. Contact us here.