5 min read

In today’s age of continuous innovations and open content generation, the media and publishing industry finds itself in a disruptive state. Streaming & Over-the-top (OTT) platforms, social & viral media, and smart devices have been the key factors behind this disruption. Most of the legacy companies are still trying to cope with this structural shift and the changing ecosystem by bringing about their variations of OTT content and streaming services.

Even as we grapple with this change, an even bigger disruption to generate intelligent media has already made its mark towards restructuring the industry. We know this change by the terms Artificial Intelligence (AI) and Machine Learning (ML). Going forward, intelligent media and content is expected to change the future of media and publishing even more.

Is intelligent media a sustainable innovation that can cause disruption, or is it just another buzzword? To understand the potential of intelligent media, we need to understand its applicability in media’s value chain.


A traditional media value chain and its key challenges

A traditional media value chain comprises: 

Media Value Chain_F

In publishing’s value chain, these steps differ slightly. Publishing follows these steps: 

Publishing Value Chain_F

Despite the differences in the business processes, inherently, both these industries (media and publishing) focus on three main challenges:

  • Engaging the end-user and driving user loyalty
  • Predicting future trends and content demand
  • Effectively monetizing the media and content

Let’s look at how AI helps in such scenarios.

Artificial Intelligence: The solution to challenges

Can AI solve these business challenges? To answer this, let us first comprehend what AI is and what it can do. In layman terms, Artificial intelligence (AI) is the ability of a computer program or a machine to understand its environment and learn. In common usage terms, AI means a program or an algorithm that mimics human cognition. Therefore, if a functional process needs decision-making capabilities based on data, AI is a suitable candidate. AI requires the use of big data and predictive analytics to provide inputs for the decision-making capabilities of an algorithm. But why do we need a decision-making algorithm when we already have competent authorities like the content reviewer, editor, legal, marketing analyst, and target segmentation analyst?



AI-driven recommendation engine: The ticket to engaging the audience and driving user loyalty

Digital has brought about a significant change in the way the media industry works. It has transformed its focus from mass media to personalized media. Earlier, print media catered to all types of readers, and television channels used to broadcast shows to capture every segment of their audience. Today’s smart digital devices have completely usurped this scenario and have given the power of selection entirely to the viewer. People can watch media as per their choice on Netflix or Prime Video.

This is where AI can help in the media industry – its role is essential towards identifying and understanding the content preferences of the target audience. To engage a digitally-savvy audience or reader, companies need to provide a personalized experience instead of delivering generalized content. Delivering any personalized experience requires a state-of-the-art recommendation engine that takes care of individual preferences via the history of media consumption. 


AI implementation at various stages of the media value chain

This is how AI can be implemented during the different stages of the media value chain:

During Stage 0 - Content consumption (Historical):

  • At the consumption stage, AI can do sentiment analysis to understand all the preferences of the audience by using big data.

During Stage 1 - Content creation:
  • AI can automate story generation by enhancing the story with infographics or summary points. Besides this, automated sports highlights can also be generated by using AI.
  • AI can also create automated subtitles and close captions for media.

During Stage 2 - Content distribution:

  • AI can be used to recommend the right content - in the right format - at the right time - to maximize audience engagement.

During Stage 3 - Content consumption:

  • Analyzing the consumed content and performing sentiment analysis of the audience allows the system to improve the recommendations for the next consumption cycle.

Creating a personalized recommendation engine with continuous feedback from all the stages of a value chain is the biggest plus for any media and publishing house.

Leveraging AI to predict the future

Predicting future trends is often the biggest challenge for any media and publishing house. Further, with audiences demanding personalized content increasingly, it has become even more complex. Even here, AI - driven by data and analytics - can be a game changer in the following ways at each stage of the value chain:

AI’s role in Content creation - Predictive analysis of the consumed viral media and sentiment analysis of the consumed owned media can provide guidance on future content trends.

AI’s role in Content aggregation - Automated AI-driven media metadata tagging can help co-relate the diverse media and in identifying the right content.

AI’s role in Content distribution - AI can be used to recommend the right content - in the right format - at the right time, to the audience to maximize engagement.

AI’s role in Content consumption - Based on content consumption analysis and sentiment analysis, future trends can be rediscovered or refined.

Knowing what individuals want to see in the future and corelating their preferences on a mass scale can allow media and publishing companies to invest in creating the right content for their audience and AI can play big role here.Predict Trends & Future Demands with Artificial Intelligence & Analytics

Predict Trends & Future Demands with Artificial Intelligence & Analytics

AI-enabled monetization of media

Traditionally, monetizing the media was dependent on either advertisements or subscriptions.
  • AI can help in the discovery of disengaged subscribed customers by using anomaly detection, thus allowing companies to create improved options to retain subscriptions.
  • AI can be used in real-time price discovery for content through analytics.
  • Companies with a huge amount of legacy content can use AI to identify the value of content by using value extraction techniques.
  • AI can also enable more relevant advertisement insertion in media, based on the co-relation with media. This creates better chances for ad clicks, thus generating more revenue.

AI can solve unique and complex functional problems to help media and publishing companies maximize the monetization of media and content.

Are you ready for AI?

The potential of AI in media and publishing is immense. Many market leaders are capitalizing on AI/ML-powered media, with data and analytics to solve unique business challenges and gain a competitive edge.

Is your media value chain enabled end-to-end with the intelligence of machine learning and data & analytics? If you haven’t boarded the bus yet, hop on - before it gets too late.

Get to know about Nagarro’s offerings in the Media and Publishing space here. For queries, please reach out to info@nagarro.com.