How to Set Up Your Data-driven Streaming Strategy for Success?

In the quest for data evolution in streaming, we have seen more and more discussion around the growing importance of data-driven insights among video services, and how they are seeking effective ways to harness them. As they turn to data-driven strategies to personalize content, optimize performance, and reduce churn, the question remains: What does it take to build a sustainable and efficient data infrastructure? What challenges must video businesses anticipate to ensure their strategy is positioned for long-term success?

We spoke with Ernest Axelbank, VP Solutions at Accedo to learn his expert opinions on the topic. During the interview, he lays out the key challenges, such as data fragmentation, and the real opportunities in developing a successful data-first approach. 

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In your experience working with streaming companies who seek to implement a data-driven strategy, what is the most common use case they ask for? What are the common challenges?

The most common reason our clients look for a data-driven strategy in streaming is to boost viewer engagement and retain users. They often seek ways to achieve better personalization—tailoring recommendations based on real-time behavior and context. Through analyzing viewer behavior, they want to uncover what content resonates with specific audiences and why, helping them make more informed decisions on content strategy and user segmentation.

In addition, optimizing the Quality of Experience (QoE) is also important to our clients. Many video services are focused on reducing disruptions and enhancing streaming performance to keep users satisfied. Ultimately, they want actionable insights to proactively address potential churn, ensuring that they can make data-driven decisions to improve engagement and optimize their service.

However, the challenge is that streaming companies often don’t have a suitable data infrastructure or integration capabilities to bring all these data points together in a meaningful way. They're not just struggling to make sense of the data—they’re dealing with fragmented, siloed systems that prevent them from getting a complete picture of their users.

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Data fragmentation and a lack of unified insights are surely among the bigger challenges that we see being discussed often. But are there other key hurdles that tend to get overlooked?

Absolutely. One thing that doesn’t get enough attention is how important it is to plan out your data architecture and governance framework. You need to think about how you’re collecting and using data, but also how transparent you are with your viewers about it. Building and maintaining trust is paramount—it’s essential to operate openly about the type of data you're gathering and how it’s being used in your strategy.

Another major challenge is data quality. It’s easy to overlook the ongoing work needed to maintain and improve the quality of the data you're collecting. Without regular cleansing and review processes in place, any issues with data quality can seriously impact your ability to pull meaningful insights from all that information.

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If a company wants to become a data-first streaming service, what would you recommend as the first steps?

The first step is to build a robust data infrastructure. It starts with figuring out what metrics are most valuable to your business and to establish a baseline—things like engagement, retention, and quality of experience. Once you know what you're measuring, you can identify the data sources that matter and begin integrating them. From there, it’s all about having the right tools in place to unify that data across platforms, devices, and departments.

But it’s not just about tech—there’s also a mindset shift required. You need to foster a data-driven culture, where teams at all levels are empowered to make decisions based on data.

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Handling large-scale data to predict things like subscriber behavior or churn can be costly. How can OTT video businesses manage all that data without breaking the bank?

You can optimize costs at several points along the data pipeline. For instance, streamlining the extraction process and minimizing redundant or overly complex data can go a long way. Efficient storage is another big factor—being smart about how and where you store data makes a considerable difference.

When you’re dealing with massive amounts of data, it’s important to understand your data models well. You want to ensure that your data is normalized as much as possible before processing to avoid unnecessary complexities. Also, leveraging aggregation functions can help you reduce the size of your data, so it’s easier and cheaper to store and query. Setting up sensible data retention rules and offloading older data to low-cost storage solutions is also a great way to keep costs down while still having access to the historical data you need.

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AI and machine learning are often described as game-changers for data strategies in streaming. What’s the real impact? Do you think they really enable hyper-personalization, or is that just hype?

In my view, AI definitely lives up to the hype when it comes to hyper-personalization. It plays a key role in delivering tailored recommendations based on user preferences, viewing habits, and real-time behavior. AI is also used to spot shifts in demand, which helps service providers make smarter decisions around content acquisition. And let’s not forget AI-powered churn prediction models, which help companies proactively reach out to at-risk subscribers.

These are just a few examples of how AI is already transforming the streaming landscape, and it’s clear that the impact is only going to grow.

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Conclusion

The importance of a solid data-driven approach cannot be overstated in an industry so technology-driven and innovation-focused such as media streaming. Navigating the challenges of data fragmentation, quality, and infrastructure are essential steps toward unlocking the full potential of data insights. 

For companies looking to optimize their strategies and deliver exceptional viewer experiences, understanding the role of observability and QoS monitoring is key. By adopting a proactive, data-driven approach, streaming services can ensure smooth performance and make informed decisions that enhance engagement and reduce churn—without compromising user experience. To learn more about how you can build effective strategies for your streaming service, check out Accedo’s joint report with New Relic - The Observability Challenge, where we dive deeper into best practices for service optimization and how to stay ahead in the competitive OTT landscape.