The media and entertainment industry were among the first to embrace the digital revolution in the world. Going digital helps OTT and news providers to scale their business exponentially by acquiring users from all over the world. However, the need of the hour for the media industry today is to go a step further from digital transformation to artificial intelligence transformation for churn prevention, advertising, content generation, personalization, and more with media-specific Analytics and Business Intelligence.

Unlike at the beginning of digital transformation, today, users have several platforms to choose from in the market for content consumption. The slightest hindrance in user experience can lead to customer loss within seconds. Users are only a download away from moving to another media and entertainment platform. Organizations that solely rely on digital tools like Google Analytics are at risk of losing customers for several reasons. For one, Google Analytics does not offer in-depth analytics of users and content, limiting media and entertainment organizations from providing the content users want.

What Is Artificial Intelligence Transformation In Media And Entertainment Industry

With the increase in digital content consumption, the media and entertainment industry is not short on user and content information. Rather the challenge is in leveraging the data to harness its potential. Since organizations’ data collection is enormous, there is a need to have artificial intelligence-based systems to process and deliver insights into user behavior, hyper-relevant advertisement, content personalization, among others for business growth.

While Google Analytics could provide descriptive statistics, companies need to obtain inferential statistics to stay ahead of the curve. Predictive analytics — a part of artificial intelligence transformation — offers insights into users’ interests that help organizations provide personalized content and advertise to increase user engagement. Predictive analytics can also forecast future events like an increase in content consumption and users leaving the platform. Such information enables media and entertainment organizations to quickly act and serve users accordingly to avoid business loss.

But, how would you evaluate if your company has gone through an artificial intelligence transformation? If you can get real-time insights into users’ behaviors, predict churns effectively, personalize content delivery, and react to events, you have moved from digital transformation to artificial intelligence transformation.

Advantages Of Analytics And Business Intelligence With Machine Learning

Cutting-edge analytics and business intelligence with machine learning help media and entertainment organizations improve every aspect of their business from content generation to content intelligence and user journey mapping. Integrating a wide range of analytics capabilities in media company can bring real-time insights and help in the following ways:

1. Churn Prevention

Keeping users engaged on the platform is as tricky as acquiring users for media and entertainment companies. Today, users’ behavior changes rapidly, and if companies cannot cater to the needs of changing users’ interests, there is a high probability of losing customers. Media and entertainment companies have to ensure they can quickly identify the change in users’ patterns and understand what they are looking for. Avoiding churn can be done if companies have better analytics and business intelligence capabilities.

2. Hyper-Targeting

Ads have helped companies gain new customers, but most of the time, due to the lack of hyper-targeting abilities, media and entertainment companies have lost money. However, with proper targeting with analytics and business intelligence, the return on investment in advertising can increase multifold. Millions of users leverage OTT or news platforms, which leads to a colossal amount of data. If the information is processed with superior techniques like time-series modeling, NLP, and more, companies can understand users better and target them with relevant content for increasing the user base.

An adage “Half the money I spend on advertising is wasted; the trouble is I don’t know which half” by a marketing pioneer, John Wanamaker, is still relevant in the digital age. Often companies target users without having a proper understanding of customers, leading to a loss in money. Hyper-targeting not only assists in acquiring new users’ subscribers but also helps keep offering desired content to existing users.

3. Content Generation

Knowing which content is popular is key to offering new content that can add value to users. Understanding content popularity based on clickstream data with Google Analytics tells only a part of the story. Media and entertainment organizations have to understand the relation of content with users’ historical engagement on the platform. This is done in combination with generating rich metadata of content and users’ interest. Such analytics and business intelligence with machine learning can assist in creating content that users prefer. A continuous stream of content that interests users will keep users on the platform for longer than usual.

4. Content Journey Mapping

To better understand users, organizations need to evaluate users’ entire journey on the platform. The way users navigate on platforms can reveal what they seek and what they would wish the platform had. By further comparing users’ journeys, companies can tell what makes customers stay on their platforms. Such insights assist organizations in optimizing users’ journey to provide a better user experience and, in turn, avoid customer churn.

How Can You Get Such Disruptive Insights?

Obtaining rich insights into users and contents in real-time cannot be obtained with Google Analytics. Organizations need to do artificial intelligence transformations that require several machine learning models to automate the delivery of insights. The media and entertainment industry needs to do the basics right with integrating artificial intelligence in their workflows that can generate rich metadata for content and provide content intelligence. Post this, organizations can increase the data collection to map it with users’ behavior.

Collected data then can be used to create clusters — an unsupervised machine learning technique to group users. Over the years, clusters have been the go-to approach for understanding user behavior with pattern discovery. Of the many clustering techniques, K-mean is a widely used technique to determine users’ interest and personalize the advertisement, assimilate churn, and more. There are several ways in which data points are evaluated using distance techniques like Euclidean distance, Manhattan distance, Minkowski distance, Mahalanobis Distance, and more. Based on the results, organizations can also find relationships between patterns and change users’ journey to boost engagement.

Staying Ahead With Artificial Intelligence Transformation

Just like digital transformation was a challenging task for organizations, artificial intelligence transformation takes considerable efforts to revamp the way insights are generated from the industry to keep users, increase subscription through advertising, and more.

RecoSense, a managed service provider, works hand in hand with media and entertainment organizations to bring cutting-edge analytics and business intelligence with supervised and unsupervised machine learning techniques. With the right expertise, media and entertainment organizations can quickly integrate without spoiling the user experience while making artificial intelligence transformations for business growth.

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