Upcoming Trends in Media Analytics

Upcoming Trends in Media Analytics

Introduction

Practitioners and analysts alike know social media by its many websites and channels: Facebook, YouTube, Instagram, Twitter, LinkedIn, Reddit and many others.
Social media analytics is the ability to gather and find meaning in data gathered from social channels to support business decisions — and measure the performance of actions based on those decisions through social media.
Social media analytics is broader than metrics such as likes, follows, retweets, previews, clicks, and impressions gathered from individual channels. It also differs from reporting offered by services that support marketing campaigns such as LinkedIn or Google Analytics.
Social media analytics uses specifically designed software platforms that work similarly to web search tools. Data about keywords or topics are retrieved through search queries or web ‘crawlers’ that span channels. Fragments of text are returned, loaded into a database, categorized, and analyzed to derive meaningful insights.
Social media analytics includes the concept of social listening. Listening is monitoring social channels for problems and opportunities. Social media analytics tools typically incorporate listening into more comprehensive reporting that involves listening and performance analysis.

An Overview of social media analytics 

Practitioners and analysts alike know social media by its many websites and channels: Facebook, YouTube, Instagram, Twitter, LinkedIn, Reddit, and many others.
Social media analytics is the ability to gather and find meaning in data gathered from social channels to support business decisions — and measure the performance of actions based on those decisions through social media.
Social media analytics is broader than metrics such as likes, follows, retweets, previews, clicks, and impressions gathered from individual channels. It also differs from reporting offered by services that support marketing campaigns such as LinkedIn or Google Analytics.
Social media analytics uses specifically designed software platforms that work similarly to web search tools. Data about keywords or topics are retrieved through search queries or web ‘crawlers’ that span channels. Fragments of text are returned, loaded into a database, categorized, and analyzed to derive meaningful insights.
Social media analytics includes the concept of social listening. Listening is monitoring social channels for problems and opportunities. Social media analytics tools typically incorporate listening into more comprehensive reporting that involves listening and performance analysis.

 

Why is social media analytics important?

IBM points out that with the prevalence of social media: “News of a great product can spread like wildfire. And news about a bad product — or a bad experience with a customer service rep — can spread just as quickly. Consumers are now holding organizations to account for their brand promises and sharing their experiences with friends, co-workers, and the public at large.”

Social media analytics helps companies address these experiences and use them to:

  • Spot trends related to offerings and brands
  • Understand conversations — what is being said and how it is being received
  • Derive customer sentiment towards products and services
  • Gauge response to social media and other communications
  • Identify high-value features for a product or service
  • Uncover what competitors are saying and their effectiveness
  • Map how third-party partners and channels may affect performance

 

Upcoming Trends of Media Analytics

These insights can be used to not only make tactical adjustments, like addressing an angry tweet, but they can also help drive strategic decisions. IBM finds social media analytics is now “being brought into the core discussions about how businesses develop their strategies.”

Predicting the Interest of the Audience: Media outlets and entertainment channels leverage big data analysis to predict audience interests, especially in the realm of internet streaming. By analyzing various data points such as search history, genre ratings, social media trends, age, and language, these channels can tailor the viewer experience and generate popular program ideas. Big data provides valuable insights to optimize content diversity and cater to a wide range of audience preferences.

Optimization and Monetization: Companies strategically curate their content list by including popular movies based on consumer demand and market trends. This approach generates increased revenue as they monetize such content to retain viewer interest and attract new users seeking similar material. Additionally, companies reserve certain films or shows for exclusive membership-only access, often through paid subscriptions. To further engage users, they may offer a free preview of specific episodes or movies before making them available exclusively to subscribers. Entertainment companies like Netflix, Hotstar, and Amazon Prime employ this strategy by offering differentiated content for members and non-members.

Recognition of Audience Disengagement: Media and entertainment businesses face the challenge of customer disengagement, leading them to offer memberships with access to exclusive content to retain customers. Membership pricing and duration are subjective decisions that can be adjusted over time. Big data provides insights into recurring customers and dedicated fan communities. Customers may opt out of membership programs if they receive excessive push alerts and find the content uninteresting. By analyzing big data, companies can identify mistakes, outdated information, and customer behavior to adapt their content and meet platform demand effectively.

Recognizing the Role of Advertising: Advertising plays a crucial role in a company's market worth and profitability. Analyzing audience behavior enables personalized and timely advertising, facilitated by big data applications. Advertisements are seamlessly integrated into the entertainment industry, appearing during relevant shows or movies to target specific products. For example, 3D glasses may be advertised in science fiction films, while fashion movies promote trendy clothing. Big data helps create content-related ads and develop effective advertising strategies based on factors like weather, timing, and second-screen usage. Streaming platforms on multiple devices enhance advertising opportunities.

Enhancing Media Stream Scheduling: The exponential growth of digital media distribution platforms has lowered the barrier between distributors and end consumers. With 2.62 billion people having social media accounts, big data analytics enables direct communication between media companies and their audience, eliminating the need for intermediaries. Through pre-scheduled video streaming and targeted content, big data enhance audience interaction and increases revenue for media and entertainment companies. It allows for precise identification of the content that resonates with customers, fostering regular engagement.

Increasingly Niche OTT Options: Niche OTT services cater to specialized audiences by carefully selecting titles relevant to specific interests or genres. They prioritize hand-picked content over algorithm-driven recommendations and rely on expertise to deliver a superior viewing experience. Examples include Crunchyroll, specializing in East Asian anime, and Passionflix, offering romance-themed films with a grading system. Competitive pricing and personalized content help niche players create online communities and attract loyal customers. While mass-market services like Netflix dominate in subscriber numbers, specialized services have a place by providing everything for someone rather than something for everyone.

Conclusion

As media affects the business in no. of ways so media analytics becomes a crucial part of the business and analytics.

Reference

1.      https://community.nasscom.in/communities/media-technology/data-analytics-media-entertainment-industry-2023-trends

2.      https://www.ibm.com/topics/social-media-analytics

Hitansh Lakkad

Business Analytics intern at Hunnarvi Technologies Pvt Ltd in collaboration with nanobi analytics.

VIEWS ARE PERSONAL

#media#socialmedia#medianalytics#business#businessanalytics#dataanalytics#data#hunnarvi#nanobi#isme

 


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