Building a Data-Driven Content Strategy for M&E Industry in the OTT Age

1. Data Management & Analytics Services WHITEPAPER Building a Datadriven Content Strategy for the M&E Industry in the OTT Age The Battle for Content Supremacy Is On

2. Building a Datadriven Content Strategy for the M&E Industry in the OTT Age Winter Is Coming and Data Is the Key to Winning Since past few years, we have been living in the era of Peak TV – a slew of streaming services that offer thousands of shows and films, many of which are critically acclaimed, just a tap away. Unlike traditional M&E, where content creation, distribution, and monetization are linear, modern M&E has spurred a revolution. While linear M&E consumers are passive, always playing by the rules of the producers, the tables have turned now. Consumers today have never been more empowered. Content creators are now playing by their rules and their expectations are sky-high. For consumers, opportunity cost has never been lower. And so, the competition for acquiring the best content to come out on top, let alone stay there, has never been more aggressive. Here is how the M&E industry deals with it. TV with a Twist On the television front, trends of creating more ambitious, more experimental content have gained traction. Linear TV and content creators have experimented with different programming/content creation strategies. Remakes, for example, have been quite successful. In the linear TV space, perceptions, and the understanding of how your viewers like to consume content are considered to be important aspects of acquiring content currently, based on reviews collected through online and offline surveys. The Streaming War Is an Algorithm War OTT platforms instead put their money on personalization – the product of algorithms crunching massive amounts of user data to predict their tastes. Indeed, recommended SVOD content is the biggest driver of modern, digital M&E revenue. More data drives better recommendations or personal offerings, which drive higher monetization. Where the Current Strategy Comes up Short • Human behavior is fiendishly complex. Just because users watch an Olympic game or a horror show, algorithms cannot bombard them with only sports or horror content. Making accurate and nuanced predictions is a Herculean task even for cutting-edge machine learning technologies and AI. • Digital content, such as linear content, is still vulnerable to piracy. Protection necessitates a 360° content and service approach to blocking all attack points. AI and machine learning analytics tools can be incorporated at all stakeholder levels, from operational to executive, to ensure the entire chain is secure. • Personalization is more successful than going with your gut. However, platforms need to constantly evolve and identify new ways to leverage data that improves data collection, personalization, customer experience, and strategic decision-making. That’s how you stand out among your competition. This is perhaps their biggest challenge. In fact, far from going with their gut, platforms today, given the myriad considerations, want to target users not audiences. Now, that is personalization. 2

3. Building a Datadriven Content Strategy for the M&E Industry in the OTT Age The Marvel(ous) Data-and-Analytics Universe OTT Sources The OTT or digital component also collects massive amount of data about millions of viewers watching thousands of films and shows. To create a profile of users, instead of audiences, data is collected and analyzed across hundreds of different parameters. However, unlike its linear counterpart, it lacks a standard, industry-backed, system. In the absence of such a system, platforms instead practice transparency, establishing ad-suites and metrics that provide advertisers just the data they need. AVOD or SVOD, the approach has been incredibly successful. Advertising revenue and brand integrations have been growing consistently. Nielsen’s SVOD Content Ratings, for example, provides an independent assessment of subscription-based streamed content, presenting a crisp view of competitive SVOD services. A common system is still on the table, but it seems that platforms will not adopt it anytime soon if transparency continues to generate equal – perhaps even greater – revenue. Then, there is Moat Reach, which offers two ways to determine the overall reach, illustrating the separation between TV buyers and cross-platform digital advertisers. It either measures household reach or audience reach on the scale of individuals. The M&E industry’s data universe has never been richer and denser, and its richness and density are only expected to increase. Linear Sources The linear component of the M&E universe mines data from four primary sources. It first assesses historical data, which sums up past viewership. Next comes viewer opinion, an unquestionably critical KPI reflected in surveys, polls, and social media. Then, there are reviews found on websites such as IMDb, an abundant resource of both quantitative and qualitative data. Finally, TV ratings and viewing metrics are collated by data organizations such as Nielsen using proprietary electronic instruments and statistical analysis techniques that capture what content, station, or network have been tapped into, and via which device. The massive amount of data is collected and analyzed to predict trends in viewership – recording tons of history, opinions, and reviews, and tapping into hundreds of networks and thousands of programs watched by millions of people. digital viewership measurement 3

4. Building a Datadriven Content Strategy for the M&E Industry in the OTT Age SG Analytics’s 360° M&E Strategy To thrive in the digital space, platforms need insights that are tailored for their unique needs. SG Analytics collates these insights for you, merging disparate metadata with rigorous data analysis for highly accurate strategic decision-making. This is a 360° approach to developing a data-driven content strategy. Our all-in-one solution combines the data extracted through the linear pipeline with the data extracted through the OTT pipeline to provide holistic insights into viewing patterns, audience preferences, audience sizing, title buckets, and content performance. The result is a solution that is best suited for your unique and distinctive needs. • Implement modern analytic solutions that help continuously capture audience response from multiple data sources to deliver the right content to the right person at the right time. • Understand platforms to make every interaction personalized and relevant. customers and prospects across Content consumption and audience engagement - we help our clients generate actionable insights by weaving disparate data sources Outputs Data sources Data engineering pipelines Linear Universe (Your show + other network shows) Linear pipeline Extract the data Linear TV insights Historical viewing Viewing patterns Surveys Stitch the data set to gether by title name Build insight pipelines Social media IMDb Audience & content insights OTT Universe (Your shows + other network shows) Extract the data Audience preference analysis Title standardization workflow Stitch the data set together by title name Merge all data and build insight pipelines Audience sizing across locations Similar or comparable titles OTT Universe (Your shows only) OTT pipeline Extract the data OTT insights Content performance Title standardization workflow Stitch the data set together by title name Build insight pipelines 4

5. Building a Datadriven Content Strategy for the M&E Industry in the OTT Age Conclusion The M&E industry’s current strategy to innovate in content creation and acquisition is still limited by the scope of the data sources being leveraged. We propose that coming out on top – and yes, even staying there – can be achieved by adopting a data-driven content strategy that combines disparate data with ML and AI to predict tastes and trends with astonishing accuracy and cost-efficiency. This brings in a lot of opportunities to identify new programming areas, themes that have the potential to become viral, and enriching insights for artists and creators about unexplored concepts and areas. SGA’s data-engineering solutions bring the best out of pay-TV and OTT streaming by helping you: • Assess your content’s performance across platforms based on a comparable user universe • Manage data flow and enrichment, ensuring it is available to the right people (internal or external) at the right time • Look beyond ordinary brackets such as age, gender, and income • Personalize for users and not audiences with the help of ML and AI technologies • Manage and integrate data collected from multiple streaming services into organized data sets for better analysis on viewers’ choice, asset performance, etc. 5

6. Building a Datadriven Content Strategy for the M&E Industry in the OTT Age About the Authors Mihir Saudagar • VP, Analytics Solutioning With a B.Tech in CS from IIT Kharagpur and having worked with companies like Amazon and Barclays in his previous roles, Mihir a seasoned analytics and product development professional with 15+ years of industry experience. He has worked with leading companies and clients across sectors (technology, e-commerce, healthcare and financial services) delivering analytics and building products. Rajesh Viswanath • VP, Analytics Rajesh has over 17 years of experience in delivering enterprise wide analytics and optimization solutions for clients in the US & Europe. Having previously worked with organizations like Genpact, he has significant experience in engaging with clients across sectors like M&E, CPG, healthcare, and BFSI. Yeshi Agarwal • Assistant Manager, Solutioning Yeshi is a research professional with a demonstrated history of working in Data Analytics and Market Research industry. She provides organizations with strategic research support helping them identify growth opportunities in the market space. Disclaimer This document makes descriptive reference to trademarks that may be owned by others. The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or get commercially benefited from it or imply the existence of an association between SGA and the lawful owners of such trademarks. Information regarding third-party products, services, and organizations was obtained from publicly available sources, and SGA cannot confirm the accuracy or reliability of such sources or information. Its inclusion does not imply an endorsement by or of any third party. Copyright © 2021 SG Analytics Pvt. Ltd. www.sganalytics.com GET IN TOUCH New York | Seattle | Austin | London | Zurich | Pune | Hyderabad 6