Big Data in Media And Entertainment- Monetizing Content

Big Data in Media and Entertainment (M&E) helps to identify unrecognized hidden data patterns in data lakes. The exceptional applications of big data in media and entertainment can provide insights that are easy to use in the daily operations of the M&E industry. Based on these insights, M&E firms can maximize their revenues through personalized marketing, driving audience insights, optimized publishing, and reliable content generation.

The M&E companies are leveraging big data tools as their applications can provide an advantage over the limitations of legacy business models. This improves customer intimacy, operation efficiency, and providing information as a service and helps M&E in providing more customized content, and services to every individual. The US media and entertainment industry is the earliest adopter of big data tools and it is expected to reach $825 billion by 2023 according to the reports of PWC. 

Role of Big Data in Media & Entertainment

Historically, M&E companies used data warehouse and business intelligence tools to report on operations, and customer subscriptions, and analyze scheduled programs. The growth of active users on digital media has increased from 20% to 80% in the last five years and it is considered that every second 25 new users are watching content on digital media. The information extracted from these data sources to understand customer demands, social media, web browsing patterns, enterprise data from the operational department, data from data aggregates, advertising response data, demographic data, etc, are in large volumes. The companies are, hence, struggling with legacy business models to drive insights from it.

The data generated by M&E companies are in huge volumes and big data technologies can harness these datasets. Contemporary media operators can leverage their big data assets to drive more value at every stage of the data life cycle, where content can be generated, curated, shared, and republished by anyone having access to an internet-enabled device and solves the problems like-

1. Improving customer intimacy

2. Improving operational efficiency

3. Providing information as a service

Benefits of applying Big Data Analytics

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• Content generation

According to the reports of Hootsuite global predictions 2019, the average amount of content posted in social, digital, and print media is 1850,000 hrs per day, with 5000 blogs written and posted every five minutes. Presenting content to the target audience through the right medium is a difficult job. Using big data analytical tools, the M&E services can forecast the content that the consumer wants. The forecasting is done based on the consumers’ impressions, clicks, responses, and comments on digital platforms. Based on the insights, the media and content creators can create content on trending topics, based on their specific group of target audience and the medium through which content is published.

• Audience Insights

As consumers’ data is collected from various sources, observing and analyzing these data sets is done based on consumer profiles, tastes, preferences, and purchase choices. The insights generated pave way for personalized content, subscription offers, coupons, and deals on the products.

According to could tweak, google processes 3.5 billion GB of data daily, and Facebook generates 500 TB of daily data which includes videos, content, likes, impressions, shares, etc. By 2020, it is considered that the data generated will be 40 ZB. E-commerce giant Amazon drives data from 152 billion customers to observe the interests, purchase patterns, and sentiments of the consumers.

• Vital publishing

According to Statista, 5.4 billion people have active social media accounts, and every quarter, M&E enterprises are driving more customer insights. Big data tools can provide an understanding of these insights for M&E companies to know when customers are more likely to watch their content and what devices they are using for it. With the scalable processing of content, the business can meet objectives like efficient targeting, managing and generating unique content, and maximum utilization of internal resources like -staff, and brand equity.

• Cross-Selling 

Content monetization is the primary goal of any M&E enterprise. Understanding and knowing what content consumers want is a cost-effective and time-saving task. By leveraging big data analytics, M&E companies can perform real-time analysis and provide insights that help in optimizing advertising content and channels to reach the target audience with the right content.

Using big data tools and machine learning algorithms, the M&E companies can create customized recommendation engines to promote up-sell and cross-sell the content through various channels. It not only provides a competitive edge but also drives new users through various platforms. 

Youtube knows its customers very well, it uses big data tools to understand the behavioral and hidden response patterns of every individual customer to provide customized and relevant content to a particular customer at the right time, thus utilizing data in providing an enhanced customer experience. 

• Social media analysis

Understanding customer behavioral patterns has been a crucial task for any business. Analyzing insights to extract the reasons for unsubscription, switching from one channel to another, etc can be solved by performing sentiment analysis on the data collected from various social and digital media sources. As social media became a part of peoples’ lives in the last few years, there are huge volumes of unstructured data that help M&E companies in finding idle customer needs and wants.

The M&E companies are leveraging big data tools in harnessing the data to observe real-time analytics of their customers to promote and cross-sell their content in different formats that help to improve their growth and service. 

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