Supervised vs Unsupervised Machine Learning in M&E
BlogJan 28, 2022
In this article on supervised vs unsupervised machine learning (ML), we will answer the following questions:
- What is machine learning?
- What is supervised learning vs. unsupervised learning?
- How is machine learning used in media and entertainment?
- How does SymphonyAI Media use ML to accelerate media and entertainment insights?
Section 1: The Basics
What is Artificial Intelligence?
Artificial intelligence (AI) is the science and engineering of building software applications that perform tasks in a human-like way by imitating intelligent human behavior and decision-making.
Applications that imitate human intelligence are usually designed with a narrow purpose, such as performing a task, optimizing a process, or achieving a specific goal. They operate by following rules-based algorithms that simulate the human processes of thinking, reasoning, and decision-making.
What is Machine Learning?
Machine Learning (ML) is a special kind of AI that uses statistical algorithms to automatically learn from data without being explicitly programmed.
Instead, ML applications use machine learning algorithms to process large volumes of training data, develop their own problem-solving models, make predictions, measure the accuracy of those predictions, and adjust their modeling to improve predictive performance over time.
Both ML and AI-driven applications use algorithms to solve problems – but while AI-driven apps follow rules-based algorithms written by software developers, ML-driven applications develop their own problem-solving models by imitating the human process of learning.
Machine Learning: Supervised vs Unsupervised. What’s the Difference?
Machine learning applications use statistical learning algorithms to build problem-solving models and improve their performance over time. Depending on the specific ML use case, programmers can choose from several different types of learning algorithms to support the development of a problem-solving model.
These learning algorithms may be divided into two categories: supervised learning, and unsupervised learning.
Supervised learning uses labeled training data to develop problem-solving models that can make predictions, while unsupervised learning uses unlabeled training data to develop problem-solving models that can find correlations.
Let’s take a closer look at how labels work and the differences between machine learning: supervised vs unsupervised in media and entertainment.
Supervised Learning Explained
In supervised learning, programmers use labels to create a “prediction target” for the algorithm. In this framework, the labels act as an “answer key” that teaches the ML algorithm how to correctly classify the data. As increasing amounts of labeled data are fed into the system, the problem-solving model learns to accurately predict the label of an unlabeled element.
Supervised learning models follow a Remember-Formulate-Predict framework:
- Remember – The model encounters labeled data during the training process and remembers how individual data points are labeled.
- Formulate – Based on the information encountered during training, the model formulates rules for predicting the label of a data point.
- Predict – When an unlabeled data point is presented, the model predicts the correct label for that data point.
A supervised learning model can usually be described as either a classification model or a regression model:
- Classification Modeling – When data is labeled with a name or a type, we can train a supervised learning model to predict the name or type of an unlabeled data point.
- Regression Modeling – When data is labeled with a number, we can train a supervised learning model to predict what that number should be for an unlabeled data point.
Unsupervised Learning Explained
Unsupervised Learning allows your ML algorithm to process large volumes of data at scale, discovering hidden relationships and insights, and answering questions you hadn’t even thought to ask.
Clustering and dimensionality reduction (DR) are two types of unsupervised learning algorithms:
- Clustering – A clustering algorithm can be used to group the elements of a dataset into clusters that are similar.
- Dimensionality Reduction – A Dimensionality Reduction algorithm tries to simplify data as much as possible, describing it with fewer features while still maintaining the big picture.
Section 2: Machine Learning in Media and Entertainment
Media and entertainment companies are facing increased competition and market uncertainty, accelerating the demand for data-driven technologies that use artificial intelligence (AI) and machine learning (ML) to optimize customer experiences and generate more revenue from content while lowering operating costs.
The rise of digital media has led to M&E companies generating and collecting more data than ever before, including content data (e.g. popularity, consumption, engagement, etc.), customer data (e.g. demographics, habits, preferences, etc.), and financial data (e.g. cash flow, budgets, payment contracts, etc.).
The most digitally sophisticated media and entertainment companies (Netflix, Amazon, and Disney) are already leveraging ML algorithms in their data analysis. They are able to accurately segment their customers, deliver content recommendations, efficiently monetize content assets, and predict customer churn – leaving slow adopters of ML at a clear competitive disadvantage.
To successfully compete for market share, companies should begin researching artificial intelligence in media and invest in ML-driven technologies that can extract valuable insights from the wealth of data they’re already generating and collecting each day.
Applying Supervised and Unsupervised Machine Learning to Media and Entertainment Data Sources
Predicting the Value of Content
Content valuation is the practice of treating the titles in your library as financial assets and deploying sophisticated technologies that track content, measure its performance, and make predictions about future performance that help maximize value and revenue generation.
Content creation and acquisition executives can use machine learning algorithms to predict the value of content and make decisions about which content they should acquire, or what kind of film/TV series they should create. Machine learning algorithms can also be used to measure content performance in real-time, accelerating insights that enable M&E executives to optimize their budgets, maximize profitability, and secure an advantage over their competitors.
Predicting Distribution Platform ROI
Since the beginning of the COVID-19 pandemic, we’ve seen many of the largest M&E firms and OTT providers adjusting their content distribution strategies to maximize revenue in a rapidly shifting and unpredictable economic environment.
Disney is a great example here. Over the past two years, the company has experimented with shortening the theatrical window for new motion pictures and distributing new releases in a TVOD model before offering them on the Disney Plus SVOD platform. Disney’s data-driven distribution plan proved a huge success in at least one case, with Black Widow grossing $158M at the box office and an additional $60M on TVOD on opening weekend.
Media companies can emulate Disney’s success by leveraging ML-driven applications to drive the financial performance of new movie releases by predicting the ROI of each distribution platform and developing optimized, multi-platform distribution strategies that maximize overall revenue.
Content Performance Tracking
The top 9 media and tech companies are slated to spend more than 140 billion on content in 2022. With more content changing hands than ever before, content performance tracking is a crucial function for distributor teams attempting to valuate assets.
Distributors who deliver OTT assets to streaming services share the content and associated content metadata. But at that point, the content owner relies on the OTT service to report back on how the asset is performing. Non-standardized naming between the distributor and the OTT service can cause breakdowns between data sets, creating a broken reporting chain for the unique performance of that asset. Elevate that problem to scale, and content owners are attempting to resolve broken content performance reporting chains across thousands of assets being shared with as many as 40-50 different streaming services.
Unsupervised ML can determine the mappings between asset names and ID’s, dramatically reducing the resources required to effectively track the revenue and viewing performance of an asset. By supplying fast, accurate data, ML also allows content owners to make smarter decisions as they distribute and price their asset library.
Artificial intelligence tools have also been shown to help media finance teams track and forecast content performance, giving them a leg up on the competition.
Matching Content to Audience Preferences
Even as the world’s largest M&E firms are purchasing and licensing content at an unprecedented rate, there’s still a strong need for content recommendation engines that can predict what users want to see and suggest content that matches their interests, preferences, and desires. In 2021, 37% of subscribers to a new OTT service in the last year indicated that they were planning to terminate their subscription because of a lack of new content.
Media executives can use machine learning algorithms to monitor audience preferences and maximize engagement by delivering relevant content recommendations at scale. Accurate content recommendations make it faster and easier for subscribers to find content they’re interested in watching, leading to enhanced customer engagement and retention.
Predicting Customer Churn
The proliferation of AVOD and SVOD OTT platforms means that consumers now have more options than ever when it comes to streaming their favorite content. With so much choice in the marketplace, managing subscriber churn has become an important strategic goal for OTT streaming providers.
Research on customer churn in the OTT market has revealed the leading causes for customer cancellations, and OTT providers have developed retention offers and win-back strategies for re-engaging lapsed customers – but the hard part has always been targeting individual subscribers with the right offer or messaging at the right time to influence their decision to churn.
Media and entertainment companies can use machine learning technology to analyze customer engagement data at scale, predict which customers are most likely to churn, and target those customers with customized messaging and offers that entice them to stay.
Effective market segmentation can empower M&E companies to effectively manage customer relationships and drive satisfaction and retention by delivering value to customer groups along the dimensions that matter most.
The classical approach to marketing segmentation has marketing analysts reviewing customer data and grouping customers in terms of their demographics and behavioral analytics (i.e. engagement patterns, and content preferences) – but this process is time-consuming to replicate at scale and requires analysts to make assumptions about the best ways to group customers.
Machine learning algorithms can be used to analyze customer data at scale and group audiences into clusters based on their shared preferences or platform engagement patterns. Armed with these insights, media executives can target each customer segment with customized marketing messages, or work to launch features that appeal to the largest and most profitable customer segments.
Accelerating Media and Entertainment Insights with SymphonyAI Media
The Revedia platform empowers content creators, owners, and distributors in the media and entertainment industry with data-driven insights, backed by the power of machine learning.
Using decades of validated industry data, we’ve trained Revedia’s machine learning algorithms to help our customers forecast demand for content, predict distribution platform ROI, uncover the root causes of customer churn, and maximize revenue.