What Is Content Affinity?
Content affinity is a user’s likelihood of interest in an individual piece of content or a category of content. Platforms who serve media content often rely on machine learning software to predict content affinity for various segments of users.
How Do Media and Entertainment Companies Use Content Affinity?
Media and entertainment companies have traditionally relied on demographic data to segment audiences and drive content decisions. But while demographics describe who customers are, they do not describe what customers actually do. Behavioral data about customers’ interests and actions, on the other hand, provides powerful information about the content affinities of single, specific, individual customers. For example, demographics about a viewer’s gender, age, and location do not reveal what shows he or she watches. However, first-party behavioral data showing that a customer has watched several horror movies indicates they have a high affinity for that type of content and would likely engage with similar content recommendations.
Marketers can use content affinity data to create meaningful profiles of existing and potential customers and then to create and optimize high-precision, multi-touchpoint campaigns that deepen their relationship with those customers.
How Can Content Affinity Help Reduce Subscriber Churn?
Marketing teams with real-time insight about content affinity can increase subscriber retention rates and reduce churn. Content affinity can be used to segment subscribers at a more targeted level, identify key touchpoints during the customer lifecycle, and ultimately enable highly personalized experiences.
Content affinity data empowers companies to reengage subscribers and convert trial users with personalized promotions and content recommendations. The ability to build customer profiles based on high, medium, and low risk of cancellation provides real value to marketers who often rely on broad segmentation across demographics for their campaigns.
How Are Artificial Intelligence and Machine Learning Changing Content Affinity?
Media and entertainment providers have long embraced personalization. Only recently have companies had access to artificial intelligence (AI) and machine learning (ML) technology that can create a real-time quantitative measurement of a viewer’s content affinity. Put simply, machine learning is an artificial intelligence technique. ML gives computers the ability to learn from data autonomously without being trained by a programmer; thus an ML-enabled solution is capable of self-improving its algorithms. Most commercial AI solutions possess ML capabilities.
Content creation and acquisition teams who leverage data-driven AI technologies understand their subscribers on a level heretofore unseen in media and entertainment. Media executives can use AI-powered data analysis to detect content affinity, maximize engagement, and deliver personalized content recommendations at scale.
The most digitally sophisticated media and entertainment companies, such as Netflix and Amazon, are already leveraging ML algorithms to optimize customer experiences and generate more revenue from content while lowering operating costs. In fact, global spending on AI is expected to more than double from $118 billion in 2022 to $300 billion in 2026.
Artificial Intelligence and Content Affinity
Enterprise AI software can track the types of content a viewer visits most often, the length of time a viewer engages with certain types of content, and the amount of content available to a viewer with a certain content affinity. AI software can identify content affinity across demographics, genres, and talent to align relevant content recommendations with subscribers. Additionally, it can uncover content trends in key regions to help drive geographically targeted marketing campaigns.
AI software applications can build and store a continuous viewer profile across touchpoints and devices. Content affinity tools for media companies may eventually be able to pull in viewers’ data from other platforms such as social media sites. AI can use integrations with other platforms to analyze behavioral data about customer activity such as “likes” on Facebook, groups on LinkedIn, or podcasts in which the customer listens. Knowledge of these content affinities enables companies to understand audiences more deeply and recommend the right media to these viewers at the right time.
Without AI technology, this level of targeted personalized marketing was previously only available to companies that built out their own extensive data science teams. Still, that process requires a huge amount of resources, is time-consuming to replicate at scale, and often relies on reactive strategies based on limited and outdated data. Today, AI software applications have the power to provide insightful content analytics, real-time automation, and workflow optimization that ensures marketers can be proactive with subscriber engagement.
How Does SymphonyAI Media Help with Content Affinity?
SymphonyAI Media is applying AI to behavior analytics to uniquely track and analyze content affinity, reveal valuable real-time insights, and deliver actionable recommendations to maximize revenue. Built by media and entertainment experts, the Revedia platform combines machine learning, topological data analysis, and statistical and geometric algorithms to support business decision-making for content creators, owners, and distributors.