Leveraging AI to Revolutionize Subscription Business Models

Kiran Nagubandi

doi:10.32628/CSEIT241051052

Authors

Kiran Nagubandi SPL Consulting Inc, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051052

Keywords:

Artificial Intelligence, Subscription Business Models, Personalization, Predictive Analytics, Customer Retention

Abstract

This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.

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References

McKinsey & Company, "Thinking inside the subscription box: New research on e-commerce consumers," 2018. [Online]. Available: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/thinking-inside-the-subscription-box-new-research-on-ecommerce-consumers

T. Davenport and R. Bean, "How Big Data and AI Are Driving Business Innovation in 2018," MIT Sloan Management Review, Feb. 2018. [Online]. Available: https://sloanreview.mit.edu/article/how-big-data-and-ai-are-driving-business-innovation-in-2018/

S. Ransbotham, D. Kiron, P. Gerbert and M. Reeves, "Reshaping Business With Artificial Intelligence: Closing the Gap Between Ambition and Action," MIT Sloan Management Review and Boston Consulting Group, Sep. 2017. [Online]. Available: https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

K. Schwab, "The Fourth Industrial Revolution: What It Means and How to Respond," Foreign Affairs, Dec. 12, 2015. [Online]. Available: https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution

C. A. Gomez-Uribe and N. Hunt, "The Netflix Recommender System: Algorithms, Business Value, and Innovation," ACM Transactions on Management Information Systems, vol. 6, no. 4, pp. 1-19, Dec. 2015. [Online]. Available: https://dl.acm.org/doi/10.1145/2843948 DOI: https://doi.org/10.1145/2843948

G. Anadiotis, "The state of AI in 2019: Breakthroughs in machine learning, natural language processing, games, and knowledge graphs," ZDNet, Dec. 28, 2019. [Online]. Available: https://www.zdnet.com/article/the-state-of-ai-in-2019-breakthroughs-in-machine-learning-natural-language-processing-games-and-knowledge-graphs/

F. F. Reichheld and W. E. Sasser Jr., "Zero defections: Quality comes to services," Harvard Business Review, vol. 68, no. 5, pp. 105-111, 1990. [Online]. Available: https://hbr.org/1990/09/zero-defections-quality-comes-to-services

E. Ascarza, S. A. Neslin, O. Netzer, Z. Anderson, P. S. Fader, S. Gupta, B. G. S. Hardie, A. Lemmens, B. Libai, D. Neal, F. Provost, and R. Schrift, "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, vol. 5, no. 1-2, pp. 65-81, 2018. [Online]. Available: https://link.springer.com/article/10.1007/s40547-017-0080-0 DOI: https://doi.org/10.1007/s40547-017-0080-0

P. Baecke and D. Van den Poel, "Data augmentation by predicting spending pleasure using commercially available external data," Journal of Intelligent Information Systems, vol. 36, no. 3, pp. 367-383, 2011. [Online]. Available: https://link.springer.com/article/10.1007/s10844-009-0111-x DOI: https://doi.org/10.1007/s10844-009-0111-x

M. Fisher, S. Gallino, and J. Li, "Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments," Management Science, vol. 64, no. 6, pp. 2496-2514, 2018. [Online]. Available: https://pubsonline.informs.org/doi/10.1287/mnsc.2017.2753 DOI: https://doi.org/10.1287/mnsc.2017.2753

V. F. Farias and B. Van Roy, "Dynamic Pricing With a Prior on Market Response," Operations Research, vol. 58, no. 1, pp. 16-29, 2010. [Online]. Available: https://pubsonline.informs.org/doi/10.1287/opre.1090.0729 DOI: https://doi.org/10.1287/opre.1090.0729

A. Lambrecht and C. Tucker, "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, vol. 65, no. 7, pp. 2966-2981, 2019. [Online]. Available: https://pubsonline.informs.org/doi/10.1287/mnsc.2018.3093 DOI: https://doi.org/10.1287/mnsc.2018.3093

A. Følstad and P. B. Brandtzaeg, "Chatbots and the new world of HCI," interactions, vol. 24, no. 4, pp. 38-42, 2017. [Online]. Available: https://dl.acm.org/doi/10.1145/3085558 DOI: https://doi.org/10.1145/3085558

A. Xu, Z. Liu, Y. Guo, V. Sinha, and R. Akkiraju, "A New Chatbot for Customer Service on Social Media," in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017, pp. 3506-3510. [Online]. Available: https://dl.acm.org/doi/10.1145/3025453.3025496 DOI: https://doi.org/10.1145/3025453.3025496

B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, "Scalable Sentiment Classification for Big Data Analysis Using Naïve Bayes Classifier," in 2013 IEEE International Conference on Big Data, 2013, pp. 99-104. [Online]. Available: https://ieeexplore.ieee.org/document/6691740 DOI: https://doi.org/10.1109/BigData.2013.6691740

M. Brynjolfsson and A. McAfee, "The Business of Artificial Intelligence," Harvard Business Review, vol. 95, no. 4, pp. 3-11, 2017. [Online]. Available: https://hbr.org/2017/07/the-business-of-artificial-intelligence

A. Abdallah, M. A. Maarof, and A. Zainal, "Fraud detection system: A survey," Journal of Network and Computer Applications, vol. 68, pp. 90-113, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1084804516300571 DOI: https://doi.org/10.1016/j.jnca.2016.04.007

J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang, "Learning under Concept Drift: A Review," IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2346-2363, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8496795