Leveraging Data-Driven Insights to Enhance Market Share in the Media Industry

Ankur Mehra

doi:10.55544/jrasb.2.3.37

Authors

Ankur Mehra Independent Researcher, USA.

DOI:

https://doi.org/10.55544/jrasb.2.3.37

Keywords:

Media industry, data analytics, market share, audience segmentation, content optimization, personalization, big data, artificial intelligence, consumer behavior, monetization strategies, competitive intelligence, data-driven culture, regulatory compliance, ROI measurement, predictive analytics

Abstract

This comprehensive research paper explores the pivotal role of data-driven insights in augmenting market share within the media industry. The study investigates various facets of data analytics, including audience segmentation, content optimization, and personalization strategies. It delves into the technological infrastructure required for effective data analysis, examines consumer behaviour insights, and discusses monetization strategies informed by data. The paper also addresses competitive intelligence, content creation and distribution, organizational culture, regulatory compliance, and future trends in media analytics. By synthesizing current research and industry practices, this study provides a roadmap for media companies to leverage data-driven approaches for sustainable growth and competitive advantage.

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References

Adobe. (2019). The State of Data-Driven Personalization in Media and Entertainment. Adobe Systems Incorporated.

Adobe. (2020). Balanced Scorecard Approach to Data KPIs in Digital Publishing. Adobe Systems Incorporated.

Akamai. (2019). Case Study: AI-Driven CDN Optimization in Streaming Services. Akamai Technologies.

Bain & Company. (2020). The Long Game: Measuring ROI in Media Data Initiatives. Bain & Company, Inc.

Blockchain in Media Consortium. (2019). The Future of Rights Management in Media: A Blockchain Perspective. Blockchain in Media Consortium.

Boston Consulting Group. (2019). The Data-Driven Revolution in Media Audience Segmentation. Boston Consulting Group.

Boston Consulting Group. (2020). AI-Driven Dynamic Pricing in Subscription-Based Media. Boston Consulting Group.

Brandwatch. (2019). Leveraging Sentiment Analysis for Media Content Optimization. Brandwatch.

Chartbeat. (2020). AI-Powered Content Analysis for Headline Optimization in News Media. Chartbeat.

Cloudera. (2019). Big Data Processing in Media: Hadoop and Beyond. Cloudera, Inc.

Comscore. (2019). Multi-Platform Engagement Tracking in Media: A Comprehensive Study. Comscore, Inc.

Comscore. (2020). Annual Report 2019. Comscore, Inc.

Content Marketing Institute. (2019). B2C Content Marketing 2019: Benchmarks, Budgets, and Trends. Content Marketing Institute.

Crayon. (2020). AI-Driven Competitor Analysis in Digital Publishing. Crayon.

Data & Marketing Association. (2019). Data-Driven Marketing Metrics: Defining Success in Media. Data & Marketing Association.

Data & Marketing Association. (2020). Building Consumer Trust Through Data Transparency: A Case Study. Data & Marketing Association.

Data Visualization Society. (2019). The Impact of Advanced Visualization Tools on Decision-Making in Media Organizations. Data Visualization Society.

Deloitte. (2019). The Future of Content: AI-Driven Niche Discovery in Streaming Platforms. Deloitte Touche Tohmatsu Limited.

Deloitte. (2020). Blockchain for Data Transparency in Media: Adoption Trends and Projections. Deloitte Touche Tohmatsu Limited.

Digonex. (2019). Dynamic Pricing for Streaming Events: A Sports Broadcasting Case Study. Digonex Technologies, Inc.

Edelman. (2019). Edelman Trust Barometer Special Report: In Brands We Trust? Edelman.

eMarketer. (2019). US Programmatic Digital Display Ad Spending. eMarketer.

eMarketer. (2020). AI-Driven Content Distribution in Media: Performance Metrics and Best Practices. eMarketer.

Epsilon. (2018). The Power of Me: The Impact of Personalization on Marketing Performance. Epsilon Data Management, LLC.

Experian. (2020). 2020 Global Data Management Research. Experian Information Solutions, Inc.

Forrester Research. (2018). The State of Data Management in Media and Entertainment. Forrester Research, Inc.

Forrester Research. (2020). The Future of Media Analytics: Predictive and Prescriptive Horizons. Forrester Research, Inc.

FreeWheel. (2019). The Power of Dynamic Ad Insertion: A Broadcaster's Perspective. FreeWheel Media, Inc.

Gartner. (2017). Data Quality Market Survey. Gartner, Inc.

Gartner. (2019). Market Guide for Attribution and Marketing Mix Modeling. Gartner, Inc.

Gartner. (2020). Hype Cycle for Artificial Intelligence. Gartner, Inc.

Google. (2020). Data-Driven Attribution Modeling in Media: A Case Study. Google LLC.

Harvard Business Review. (2018). How Successful Companies Use Dynamic Pricing. Harvard Business Publishing.

IBM. (2019). Quantum Computing in Media and Entertainment: Potential Applications and Timeline. International Business Machines Corporation.

IDC. (2019). Worldwide Edge Infrastructure Forecast, 2019–2023. International Data Corporation.

Interactive Advertising Bureau (IAB). (2020). Internet Advertising Revenue Report. Interactive Advertising Bureau.

International Association of Privacy Professionals (IAPP). (2020). Privacy Tech Vendor Report. International Association of Privacy Professionals.

Journal of Advertising Research. (2018). Predicting Content Engagement with Eye-Tracking Data: A Machine Learning Approach. Journal of Advertising Research, 58(4), 452-470. https://doi.org/10.2501/JAR-2018-042

Journal of Forecasting. (2018). Machine Learning Models for Viral Content Prediction: Outperforming Traditional Time Series Analysis. Journal of Forecasting, 37(7), 737-751. https://doi.org/10.1002/for.2527

Journal of Marketing Analytics. (2019). Advanced Data Fusion Techniques for Cross-Channel Customer Journey Mapping in Media. Journal of Marketing Analytics, 7, 119–133. https://doi.org/10.1057/s41270-019-00052-x

Journal of Marketing Research. (2018). Churn Prediction in the Media Industry: A Machine Learning Approach. Journal of Marketing Research, 55(2), 218-235. https://doi.org/10.1509/jmr.15.0527

Journal of Marketing. (2018). Machine Learning for Advanced Attribution Modeling in Media Advertising. Journal of Marketing, 82(5), 66-81. https://doi.org/10.1509/jm.17.0163

LinkedIn. (2020). Emerging Jobs Report 2020. LinkedIn Corporation.

McKinsey & Company. (2018). The Business Value of Design. McKinsey & Company.

McKinsey & Company. (2019). The Future of Personalization—and How to Get Ready for It. McKinsey & Company.

McKinsey & Company. (2020). The Next Normal in Media: A Vision for 2025. McKinsey & Company.

MIT Sloan Management Review. (2019). Winning With AI. MIT Sloan Management Review and Boston Consulting Group.

MIT Technology Review. (2018). The Rise of Intelligent Content: How AI is Transforming Media Production. MIT Technology Review.

Netflix Technology Blog. (2017). Netflix Recommendations: Beyond the 5 stars (Part 1). Netflix.

NewVantage Partners. (2020). Big Data and AI Executive Survey 2020. NewVantage Partners LLC.

Nielsen. (2019). Total Audience Report. The Nielsen Company.

Optimizely. (2020). Personalized A/B Testing in Digital Publishing: A Case Study. Optimizely, Inc.

Pew Research Center. (2019). Americans and Privacy: Concerned, Confused and Feeling Lack of Control Over Their Personal Information. Pew Research Center.

PwC. (2019). Global Entertainment & Media Outlook 2019–2023. PricewaterhouseCoopers.

PwC. (2020). Seeing is Believing: How Virtual and Augmented Reality Could Transform Media Analytics. PricewaterhouseCoopers.

Qlik. (2019). The Data Literacy Index. Qlik Technologies Inc.

SAS. (2019). Predictive Analytics for Long-Term Strategy: A Media Conglomerate Case Study. SAS Institute Inc.

Sprout Social. (2019). Sprout Social Index, Edition XV: Empower & Elevate. Sprout Social, Inc.

Sprout Social. (2020). Social Listening in Media: From Insights to Action. Sprout Social, Inc.

Strategic Management Journal. (2018). Predictive Competitor Analysis in Media: A Machine Learning Approach. Strategic Management Journal, 39(10), 2745-2773. https://doi.org/10.1002/smj.2933

Tableau. (2020). Gamification in Data Literacy: A Media Industry Case Study. Tableau Software, LLC.

Udacity. (2019). Upskilling for the AI Era: A Media Conglomerate's Journey. Udacity, Inc.

University of Southern California and Indiana University. (2018). Online Human-Bot Interactions: Detection, Estimation, and Characterization. Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017).

W3Techs. (2020). Usage Statistics of Traffic Analysis Tools for Websites. Q-Success.

World Economic Forum. (2018). The Future of Jobs Report 2018. World Economic Forum.

World Economic Forum. (2020). The Promise of Platform Data Exchanges. World Economic Forum.

Santhosh Palavesh. (2019). The Role of Open Innovation and Crowdsourcing in Generating New Business Ideas and Concepts. International Journal for Research Publication and Seminar, 10(4), 137–147. https://doi.org/10.36676/jrps.v10.i4.1456

Santosh Palavesh. (2021). Developing Business Concepts for Underserved Markets: Identifying and Addressing Unmet Needs in Niche or Emerging Markets. Innovative Research Thoughts, 7(3), 76–89. https://doi.org/10.36676/irt.v7.i3.1437

Palavesh, S. (2021). Co-Creating Business Concepts with Customers: Approaches to the Use of Customers in New Product/Service Development. Integrated Journal for Research in Arts and Humanities, 1(1), 54–66. https://doi.org/10.55544/ijrah.1.1.9

Santhosh Palavesh. (2021). Business Model Innovation: Strategies for Creating and Capturing Value Through Novel Business Concepts. European Economic Letters (EEL), 11(1). https://doi.org/10.52783/eel.v11i1.178

Vijaya Venkata Sri Rama Bhaskar, Akhil Mittal, Santosh Palavesh, Krishnateja Shiva, Pradeep Etikani. (2020). Regulating AI in Fintech: Balancing Innovation with Consumer Protection. European Economic Letters (EEL), 10(1). https://doi.org/10.52783/eel.v10i1.1810

Challa, S. S. S. (2020). Assessing the regulatory implications of personalized medicine and the use of biomarkers in drug development and approval. European Chemical Bulletin, 9(4), 134-146.D.O.I10.53555/ecb.v9:i4.17671

EVALUATING THE EFFECTIVENESS OF RISK-BASED APPROACHES IN STREAMLINING THE REGULATORY APPROVAL PROCESS FOR NOVEL THERAPIES. (2021). Journal of Population Therapeutics and Clinical Pharmacology, 28(2), 436-448. https://doi.org/10.53555/jptcp.v28i2.7421

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2019). Investigating the use of natural language processing (NLP) techniques in automating the extraction of regulatory requirements from unstructured data sources. Annals of Pharma Research, 7(5), 380-387.

Challa, S. S. S., Chawda, A. D., Benke, A. P., & Tilala, M. (2020). Evaluating the use of machine learning algorithms in predicting drug-drug interactions and adverse events during the drug development process. NeuroQuantology, 18(12), 176-186. https://doi.org/10.48047/nq.2020.18.12.NQ20252

Ranjit Kumar Gupta, Sagar Shukla, Anaswara Thekkan Rajan, Sneha Aravind, 2021. "Utilizing Splunk for Proactive Issue Resolution in Full Stack Development Projects" ESP Journal of Engineering & Technology Advancements 1(1): 57-64.

Rishabh Rajesh Shanbhag, Rajkumar Balasubramanian, Ugandhar Dasi, Nikhil Singla, & Siddhant Benadikar. (2021). Developing a Scalable and Efficient Cloud-Based Framework for Distributed Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 288 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6761

Siddhant Benadikar. (2021). Evaluating the Effectiveness of Cloud-Based AI and ML Techniques for Personalized Healthcare and Remote Patient Monitoring. International Journal on Recent and Innovation Trends in Computing and Communication, 9(10), 03–16. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/11036

Challa, S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2019). Investigating the use of natural language processing (NLP) techniques in automating the extraction of regulatory requirements from unstructured data sources. Annals of PharmaResearch, 7(5), 380-387

Dr. Saloni Sharma, & Ritesh Chaturvedi. (2017). Blockchain Technology in Healthcare Billing: Enhancing Transparency and Security. International Journal for Research Publication and Seminar, 10(2), 106–117. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/1475

Saloni Sharma. (2020). AI-Driven Predictive Modelling for Early Disease Detection and Prevention. International Journal on Recent and Innovation Trends in Computing and Communication, 8(12), 27–36. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/11046

Fadnavis, N. S., Patil, G. B., Padyana, U. K., Rai, H. P., & Ogeti, P. (2020). Machine learning applications in climate modeling and weather forecasting. NeuroQuantology, 18(6), 135-145. https://doi.org/10.48047/nq.2020.18.6.NQ20194

Narendra Sharad Fadnavis. (2021). Optimizing Scalability and Performance in Cloud Services: Strategies and Solutions. International Journal on Recent and Innovation Trends in Computing and Communication, 9(2), 14–21. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10889

Patil, G. B., Padyana, U. K., Rai, H. P., Ogeti, P., & Fadnavis, N. S. (2021). Personalized marketing strategies through machine learning: Enhancing customer engagement. Journal of Informatics Education and Research, 1(1), 9. http://jier.org

Bhaskar, V. V. S. R., Etikani, P., Shiva, K., Choppadandi, A., & Dave, A. (2019). Building explainable AI systems with federated learning on the cloud. Journal of Cloud Computing and Artificial Intelligence, 16(1), 1–14.

Vijaya Venkata Sri Rama Bhaskar, Akhil Mittal, Santosh Palavesh, Krishnateja Shiva, Pradeep Etikani. (2020). Regulating AI in Fintech: Balancing Innovation with Consumer Protection. European Economic Letters (EEL), 10(1). https://doi.org/10.52783/eel.v10i1.1810

Dave, A., Etikani, P., Bhaskar, V. V. S. R., & Shiva, K. (2020). Biometric authentication for secure mobile payments. Journal of Mobile Technology and Security, 41(3), 245-259.

Saoji, R., Nuguri, S., Shiva, K., Etikani, P., & Bhaskar, V. V. S. R. (2021). Adaptive AI-based deep learning models for dynamic control in software-defined networks. International Journal of Electrical and Electronics Engineering (IJEEE), 10(1), 89–100. ISSN (P): 2278–9944; ISSN (E): 2278–9952

Narendra Sharad Fadnavis. (2021). Optimizing Scalability and Performance in Cloud Services: Strategies and Solutions. International Journal on Recent and Innovation Trends in Computing and Communication, 9(2), 14–21. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10889

Prasad, N., Narukulla, N., Hajari, V. R., Paripati, L., & Shah, J. (2020). AI-driven data governance framework for cloud-based data analytics. Volume 17, (2), 1551-1561.

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Shah, J., Narukulla, N., Hajari, V. R., Paripati, L., & Prasad, N. (2021). Scalable machine learning infrastructure on cloud for large-scale data processing. Tuijin Jishu/Journal of Propulsion Technology, 42(2), 45-53.

Narukulla, N., Lopes, J., Hajari, V. R., Prasad, N., & Swamy, H. (2021). Real-time data processing and predictive analytics using cloud-based machine learning. Tuijin Jishu/Journal of Propulsion Technology, 42(4), 91-102

Secure Federated Learning Framework for Distributed Ai Model Training in Cloud Environments. (2019). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 7(1), 31-39. https://ijope.com/index.php/home/article/view/145

Paripati, L., Prasad, N., Shah, J., Narukulla, N., & Hajari, V. R. (2021). Blockchain-enabled data analytics for ensuring data integrity and trust in AI systems. International Journal of Computer Science and Engineering (IJCSE), 10(2), 27–38. ISSN (P): 2278–9960; ISSN (E): 2278–9979.

Kumar, A. (2019). Implementation core business intelligence system using modern IT development practices (Agile & DevOps). International Journal of Management, IT and Engineering, 8(9), 444-464. https://doi.org/10.5281/zenodo.1234567

Tripathi, A. (2020). AWS serverless messaging using SQS. IJIRAE: International Journal of Innovative Research in Advanced Engineering, 7(11), 391-393.

Tripathi, A. (2019). Serverless architecture patterns: Deep dive into event-driven, microservices, and serverless APIs. International Journal of Creative Research Thoughts (IJCRT), 7(3), 234-239. Retrieved from http://www.ijcrt.org

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2019). Investigating the use of natural language processing (NLP) techniques in automating the extraction of regulatory requirements from unstructured data sources. Annals of Pharma Research, 7(5),

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2021). Navigating regulatory requirements for complex dosage forms: Insights from topical, parenteral, and ophthalmic products. NeuroQuantology, 19(12), 15.

Tilala, M., & Chawda, A. D. (2020). Evaluation of compliance requirements for annual reports in pharmaceutical industries. NeuroQuantology, 18(11), 27.

AI-Driven Customer Relationship Management in PK Salon Management System. (2019). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 7(2), 28-35. https://ijope.com/index.php/home/article/view/128