5 AI-Driven Use Cases for Big Data in Media & Entertainment

10.18.2022 | Ray Gilmartin

Big data in the media industry is a big deal – just look at Netflix, whose leadership in the OTT marketplace can be directly attributed to successfully harnessing the power of big data.

Big data in media industry next year

Big data and a robust analytics program have been a competitive advantage for Netflix over the past decade, but with events like the COVID-19 pandemic accelerating the global transition from traditional cable TV to digital streaming services, media companies everywhere are moving quickly to leverage their own data resources. In a quickly evolving media landscape, the companies that capitalize on big data will be those who prioritize investments in artificial intelligence (AI).

In this article, we’ll explore opportunities and challenges for big data, how AI can lead to data insights, and 5 use cases for big data in media and entertainment.

We will explore ways that media companies of all sizes are using artificial intelligence and big data to acquire new viewers, reduce subscriber churn, maximize revenue, and grab a bigger slice of the marketplace.


Big Data in the Media Industry: Opportunities and Challenges

Big Data Opportunities

Media companies generate and collect data from a variety of sources, but with the emergence of new revenue models and content distribution methods like DTC, AVOD, and vMVPDs, the challenge of analyzing and extracting meaningful insights from that data is ever-growing. To create actionable, revenue driving strategies with big data, top media and entertainment companies are leveraging AI software. Here are some of the biggest opportunities in M&E for companies looking to use their data to generate audience insights, financial strategies, and precise content valuation.   

Audience Data

Audience data breaks down into three broad categories: personal data, demographic data, and behavioral data

  • Family on laptopsPersonal data describes the identity of individual audience members, including their name, email address, payment method, telephone number, and physical address. This data is provided to media companies by audience members when they create an account or purchase a subscription.
  • Demographic data describes the audience in aggregate, including things like age, gender, education, income level, marital status, and employment status. Media companies may conduct surveys or utilize data from external sources to uncover demographic data that pertains to their audiences.
  • Behavioral data captures how audiences interact with the media platform, including their viewing frequency, habits, and preferences. Media companies use automated event-tracking technology to capture behavioral data from audiences as they browse or consume content.

With over 200 million subscribers, Netflix leverages its wealth of audience data to deliver personalized content recommendations and platform experiences, resulting in a 74% customer retention rate (compared to 67% for Hulu and <30% for Amazon).


Financial Data

For media companies generating direct-to-consumer (DTC) revenue, financial data is centered around viewer transactions and includes payment methods, credit card data, and transaction records. 

For media companies who license content to OTT platforms or movie theaters, financial data includes licensing agreements and profit-sharing reports. Profit-sharing reports include details about how many times the content was viewed, how much revenue the content generated, and how much of that revenue will be shared as part of the licensing agreement. These reports come directly from distributors, often in a spreadsheet or as a PDF invoice.

As the world’s most popular streaming platform, Netflix analyzes its financial data to forecast demand for content, estimate its financial impact, and invest dollars to bring audiences the content they want. This resulted in the strategic allocation of over $17 billion to original content production and licensing costs in 2021.


Content Data 

Content data includes popularity, consumption, and engagement metrics that may be segmented by genre, platform, device, or across other dimensions. Media firms collect data on the popularity of content from distribution partners in the licensing model, or via digital platforms in the DTC model.


By analyzing this data, media firms can better understand audience preferences, forecast demand for specific types of content, offer personalized experiences and recommendations, optimize their distribution strategies, negotiate better licensing terms, and ultimately maximize their revenue to compete more successfully in the entertainment marketplace.

Netflix uses OTT content data to understand and deliver on subscriber content preferences, resulting in massively successful original releases like Money Heist, Bridgerton, and Squid Game.


Big Data Challenges

Most media firms are already doing some big data analytics, but the technical challenges of efficiently pooling data from multiple sources and extracting insights may be preventing them from using their data to its full potential. These challenges include:

Siloed Data by Source

Big data in the media industry may be collected from subscribers, generated internally and stored in a database, provided by distribution partners, or sourced from a third-party organization. These multiple sources of data live in separate systems (data silos), and this segmentation means they can’t easily be integrated for analytics applications.

Non-standardized Formats

Big data in the entertainment industry comes from multiple sources and may be in a variety of different formats. A media firm that licenses content to ten different distributors might receive ten different profit-sharing reports, each with their own fields and format. The lack of a standardized format means that this data must be normalized before it can be analyzed effectively.

Siloed Revenue by Monetization Model

Financial data is often siloed by monetization model, with separate systems for tracking licensing revenue and DTC revenue. The lack of an integrated system for managing financial data from multiple sources makes it difficult for media companies to truly know which content is driving viewer satisfaction and revenue.

Manual Data Processes

While some media companies are using complex algorithms to process big data, others are still dependent on manual processes for data aggregation, normalization, analytics, and reporting. But as big data continues to grow, manual processes become more time-consuming, insights are delayed, and the overall impact and value of big data diminishes.


Leveraging AI to Overcome Big Data Challenges in Media and Entertainment

To overcome big data challenges, accelerate data science operations, and maximize the value of their data, media firms should adopt enterprise AI solutions that can automate the processes of aggregating from multiple data sources, normalizing data into a consistent format, analyzing the data, and generating insights that inform business decision-making.

big data in media industry

AI can help media companies overcome big data challenges like data silos and non-standardized formatting by automating the data aggregation and normalization processes, a change that eliminates error-prone and tedious manual data processes, and results in accelerated time to insights.

Enterprise AI solutions for the media industry can be programmed or “trained” using machine learning technology to rapidly, precisely, and autonomously extract insights from big data. Machine learning allows AI-driven software systems to self-improve their algorithms over time as they encounter more data, increasing the quality and accuracy of recommendations and predictive insights.

At SymphonyAI Media, we’ve trained our predictive AI with years of data from the media and entertainment industry, empowering our customers to rapidly generate relevant insights when analyzing their audience, financial, and content data on the Revedia platform


5 AI-Driven Use Cases for Big Data in the Media Industry

The most competitive media companies are adopting enterprise AI solutions to help manage their data and generate new insights. Below, we highlight five of the most important AI-driven use cases for big data in the media industry.


1) Predictive and Prescriptive Insights

Big data in the media industry can yield three different types of insights: diagnostic, predictive, and prescriptive:

  • Diagnostic insights reveal information about events that happened in the past. 
  • Predictive insights reveal information about events that might happen in the future.
  • Prescriptive insights are recommendations to make a business decision or take action in a certain way.

Enterprise AI solutions are critical to the development of predictive and prescriptive insights using big data. Predictive insights are generated by feeding data into an AI application that has been trained to forecast potential outcomes based on input variables. Prescriptive insights are also the domain of artificial intelligence, requiring the AI application to translate its forecasts into actionable recommendations that support strategic business objectives.


2) Data Management

Data management, including aggregation and normalization, is a time-consuming task for media companies who still depend on manual processes. Enterprise AI solutions with multi-platform integration features can streamline and automate the process of aggregating data from multiple sources into a centralized repository and normalizing the data to prepare it for analytics.


3) Content Optimization

big data in media industry

Content optimization is the ongoing practice of maximizing revenue from content distribution and licensing agreements. Enterprise AI solutions can help media companies forecast audience demand and assess the revenue potential of various types of content. As a result, media companies can make better strategic decisions about licensing or producing content and distribution teams can leverage insights to maximize licensing terms.

AI can also help media companies automate the process of applying tags and structured metadata to their content, making content more discoverable and platforms more user-friendly for audiences.


4) Distribution Strategy

Media companies typically store licensing agreements as unstructured data, so the process of searching through contracts or comparing partnership terms and pricing between contracts is manual and time-consuming. Enterprise AI solutions can help by normalizing the unstructured data in these documents, allowing data scientists to perform searches or run analytics on media licensing agreements.

Enterprise AI solutions can also help validate the accuracy of profit-sharing reports from distributors to ensure compliance with contract terms. Finally, media companies can leverage AI-driven insights to strengthen their negotiation position with distributors and secure better contract terms that result in greater revenue.


5) Churn Mitigation

Preventing churn is a constant battle for any subscription-based product or service. Enterprise AI solutions can use predictive modeling to anticipate when a subscriber is likely to churn and reach out to them with a personalized offer or recommendation that encourages them to re-engage with the platform. Doing this manually would be impossibly demanding, but AI makes it possible to mitigate churn at scale with real-time, data-driven interventions that work.

How Can AI Impact Business Results for the Media Industry?

Improve Quality of Insights

Media companies who leverage AI to analyze their big data can generate more accurate and higher-quality insights than those who stick to manual methods. Automating big data analytics with AI also accelerates time-to-insights, giving executives better opportunities to make an impact by implementing AI recommendations.


Enhance Operational Efficiency

Enterprise AI solutions can help media companies reduce or eliminate the tedious process of aggregating and normalizing big data manually, resulting in time and cost savings. Data scientists can perform their roles more effectively with AI-driven tools, leaving them with more time available to innovate or engage with other more valuable tasks.


Drive Revenue Growth 

Enterprise AI solutions like Revedia drive revenue growth by revealing opportunities for media companies to mitigate subscriber churn, improve content licensing terms, make strategic licensing and production decisions with accurate demand forecasting, and maximize the value of their content.


Unlock Hidden Opportunities

Technical limitations and human bias mean that without the right analytics technology in place, media companies might analyze subsets of data but miss important, relevant inputs that significantly impact revenue. 

Here’s one example we love to share: 

A popular streaming service implemented the Revedia AI platform to start analyzing their audience and content data. Within 24 hours, our AI discovered 15,000 user accounts that were registered for a free trial with no end date – an oversight that amounted to over $1 million in annual lost revenue. After discovering this error with Revedia, the streaming service was able to expire those free accounts and convert some into paying subscribers.

Enterprise AI solutions like Revedia offer media companies the ability to discover the answers to questions they hadn’t even thought to ask, unlocking hidden insights and opportunities to grow revenue.


Maximize the Value of Your Data with Revedia

the datasheetWith media companies searching for new ways to make the most of their data, SymphonyAI Media offers a powerful AI-driven solution that leverages big data to drive organizational performance for our customers.

The Revedia platform is powered by the world’s most advanced AI platform and purpose-built by media and entertainment experts to accelerate end-to-end data science and generate actionable insights at scale, with use cases that include data management, content optimization, distribution strategy, churn mitigation, and predictive insights.


*This blog was updated and republished in October 2022.

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