In recent years, streaming and broadcasting companies have undergone rapid change, with constant shifts in how media is consumed and the increasing popularity of streaming content. They must also navigate consumers’ growing concerns about data privacy alongside the proliferation of data across many channels and tools. Snowflake’s David Fisher, Industry Principal for Advertising, Media & Entertainment in EMEA, highlights several key trends and dynamics impacting this evolving industry in the EMEA region.
1. Evolving streaming business models
In line with the increasing digitisation of content consumption, the streaming business model has evolved. Today, the direct-to-consumer (DTC) model for subscription businesses has emerged as a compelling alternative to traditional distribution networks. Whether pay-per-view, subscription, or ad-based, this model allows broadcasters and content creators to engage directly with their audience, bypassing traditional intermediaries. The DTC model provides significant benefits, including improved subscriber insights, greater control over the brand narrative, and enhanced revenue opportunities.
Concurrently, many broadcasters are turning to cloud data platforms as the base of their martech stack, enabling them to break down data silos and connect disparate data sets from across the organization to gain a 360-degree view of subscribers. This customer 360 capability, along with composable customer data platforms (CDPs), enable them to enrich customer profiles to achieve a comprehensive view of subscriber behaviour. By understanding their audience’s who, what, when, and why, broadcasters can tailor their content and marketing strategies to increase viewer retention, reduce churn, and ultimately boost their bottom line by winning new subscribers and optimising the value of existing ones. It also turns the traditional on-prem data approach on its head; instead of moving data to various applications, workloads, and applications are brought to the data itself.
2. AI and the shift to the data cloud
Fundamental to leveraging AI is a data cloud strategy. Given the vast amount of data required for AI, there can be no AI strategy without an effective data strategy. AI enables broadcasters to dynamically analyse, interpret, and make decisions on a wide range of data in near real-time. Whether it’s through sophisticated personalized content recommendations curating viewing experiences or advanced analytics identifying audience trends, AI acts as a catalyst, elevating the overall customer experience, amongst other use cases such as real-time translation and predictive analytics. Rapid advancements in AI technology mean broadcasters can now foresee market shifts, adapt to changing audience demands and roll out content that resonates more profoundly. These capabilities are paving the way for an industry that is more responsive, agile, and in tune with its audience than ever before.
To effectively manage and harness the massive troves of data needed for AI, many organisations in the industry are adopting a data cloud-centric approach. This approach enables them to consolidate all data into a single, secure location, creating a ‘centre of data gravity’.
3. Changing viewing patterns
The broadcasting landscape across the EMEA region is witnessing a transformative shift in viewing patterns. This transformation results from strategic initiatives by broadcasters and streaming companies leveraging data collaboration and interoperability (data sharing across organizations and partners) to decipher intricate patterns in viewership behaviour, demographics, and engagement metrics. The data acquired from these efforts forms the bedrock of their broadcasting and distribution strategies, enabling them to tailor their offerings to meet viewers’ dynamic needs and preferences.
4. Hybrid content distribution
As traditional broadcasting and digital streaming continue to converge, broadcasters are increasingly adopting hybrid distribution models. These models amalgamate traditional broadcasting with over-the-top (OTT) streaming services, allowing them to offer their viewers a more comprehensive and diverse range of content. By optimising the mix of distribution channels, broadcasters can reach a wider range of audiences and gain the flexibility and adaptability they need in a rapidly evolving and increasingly competitive media landscape.
Data science plays an indispensable role in this hybrid approach. It is the key that unlocks data-driven decisions, guiding the choice of distribution channels and the mix of content offered. Advanced data analytics powers content recommendations and personalisation, delivering a bespoke viewing experience that captivates viewers and fosters loyalty. In addition to providing binge-worthy content, broadcasters may choose to run scheduled releases to create a sense of scarcity, increasing its perceived value and, thus, making it more desirable to the audience.
5. Evolving measurement
Finally, the realm of broadcasting measurement is also undergoing a significant shift, moving from traditional viewership metrics towards more sophisticated and granular forms of measurement. These new approaches provide a comprehensive understanding of viewer behaviour, including viewing habits, content preferences, engagement levels, and more. This granular understanding allows broadcasters to customise their content and advertising strategies, enhancing viewer satisfaction and maximising their ROI.
This evolution is being propelled by modern cloud data platforms. Modern cloud data platforms provide a secure, flexible, and scalable solution for storing, processing, and analysing vast quantities and types of data—a critical capability in today’s data-rich broadcasting environment. Meanwhile, its data collaboration functionality allows broadcasters to share insights from across the business to learn from each other and innovate collectively, thereby pushing the boundaries of what’s possible across the industry. Data clean rooms also play a critical role by providing a privacy-preserving way for broadcasters and their partners to securely analyse data, allowing them to deliver targeted and personalised ads to viewers, enhancing their overall content engagement.