How Big Data and AI Are Reshaping the Media Industry

Big data in media is transforming the landscape, and few companies illustrate its power better than Netflix. Netflix’s rise to dominance in the streaming space is largely due to its ability to turn data into a strategic advantage. Now, as digital streaming overtakes traditional cable accelerated by events like the COVID-19 pandemic, media companies of all sizes are racing to tap into their own data goldmines. 

This article explores how media firms can unlock the full potential of big data and AI, the challenges they must overcome, and five real-world use cases demonstrating what’s possible when data is used right. 

The Power of Big Data in Media 

Media and entertainment (M&E) companies are sitting on mountains of data—from how audiences engage with content to how content generates revenue. However, the real competitive edge lies in how that data is used. AI and machine learning (ML) technologies are proving essential in translating raw information into insights that guide content strategy, viewer engagement, and monetization. 

Key Types of Data Media Companies Can Leverage 

1. Audience Data

• Personal: Collected during sign-ups (e.g., names, emails, payment methods). 

• Demographic: Age, gender, income, etc. useful for segmentation. 

• Behavioral: What users watch, when, and how often. 

Netflix, for instance, uses behavioral data to fine-tune its recommendation engine, helping it achieve a 74% retention rate. 

2.Viewers and Co-Viewers Data 

Primary Viewers: The registered account holder who typically drives platform engagement. 

Co-Viewers: Family members or friends who watch content on the same device or under the same profile. 
Tracking co-viewer behavior, such as age ranges, content preferences, and interaction patterns , enables better personalization and advertising segmentation. For example, understanding that a teen frequently co-views animated series with a parent can inform multi-demographic content recommendations. 
AI can infer co-viewership patterns from device switching, simultaneous streams, or viewing times, offering deeper household-level insights. 

2. Financial Data 

• Includes DTC payment records and licensing revenue. 

• Used for forecasting content ROI and guiding investment decisions. 

• Netflix allocated over $17B in 2021 based on such insights. 

3. Content Data

• Measures engagement by genre, platform, device, etc. 

• Helps forecast demand and improve content discoverability. 

• Fueled the success of shows like Squid Game and Bridgerton. 

Challenges with Big Data in Media 

Despite high potential, media firms face several technical hurdles: 

• Siloed Data: Systems across departments or partners don’t talk to each other. 

• Non-standard Formats: Data from various sources often lacks consistency. 

• Separate Revenue Systems: Difficult to analyze licensing vs. DTC performance together. 

• Manual Processing: Slows down insights and increases risk of human error. 

How AI Overcomes These Barriers 

AI technologies can automate: 

• Data aggregation and normalization 

• Predictive modeling and recommendation engines 

• Contract validation and financial compliance 

Machine learning ensures these systems improve over time, delivering deeper, more accurate insights. 

5 AI-Powered Use Cases in the Media Industry 

1. Predictive & Prescriptive Insights 
   Use AI to forecast viewer behavior and recommend content or business decisions accordingly. 

1. Data Management Automation 
   Eliminate manual tasks in collecting and cleaning data from multiple sources. 

1. Content Optimization 
   Forecast content demand, automate metadata tagging, and improve licensing decisions. 

1. Smarter Distribution Strategy 
   Analyze unstructured contract data and optimize revenue share agreements. 

1. Subscriber Churn Prevention 
   Predict when users are likely to churn and trigger real-time personalized retention tactics. 

The Business Impact of AI in Media 

• Better Insights: More accurate, actionable, and faster than manual analytics. 

• Operational Efficiency: Reduced data wrangling leads to cost savings. 

• Revenue Growth: Improve licensing, retention, and monetization strategies. 

• Uncovering Hidden Value: Like the platform that uncovered 15,000 unlimited free trial users—saving over $1M annually. 

Make the Most of Your Data with Priorise 

At Priorise, we empower media and entertainment companies to unlock the full value of their data with custom-built, AI-driven data science solutions. From automating audience analytics to optimizing content strategy and reducing churn, our expertise helps businesses turn fragmented data into strategic growth. Whether you’re looking to scale insights, improve operational efficiency, or maximize revenue opportunities, Priorise brings deep domain knowledge and powerful data capabilities to help you lead in a digital-first media world.