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Pricing a Subscription Service to Maximize Customer Lifetime Value

As more and more businesses switch to subscription-based offering, traditional pricing & revenue management models need to factor in the long-term effect of price on customer’s lifetime value. In this presentation, we demonstrate a way to combine the effect of pricing on acquisition of new subscribers as well as the effect of pricing on renewal likelihood to promote a pricing structure that maximizes the overall lifetime value of a customer. Incorporating the effect of price on customer’s likelihood of renewal in subsequent years allows the business to make better pricing that creates a higher value customer pool that is more likely to renew the subscriptions without additional incentives. Early results from the implementation of this model indicates a 5%-10% increase in number of subscriptions along with a 2%-5% revenue increase in the first year.

Anand Srinivasan image

Anand Srinivasan

Anand Srinivasan

Chief Data Scientist at Kaizen Analytix

Anand Srinivasan is a seasoned machine learning and artificial intelligence professional with over 10 years of experience in design, development, and delivery of AI-powered solutions to drive business value. He has a proven track record of delivering AI/ML solutions in challenging time frames to several Fortune 500 companies.

In his current role as a Chief Data Scientist at Kaizen Analytix, Anand heads the data science team that is responsible for design and development of analytical models and supports a cross-functional team to deliver analytically sound solutions.

Prior to joining Kaizen Analytix, Anand worked at McKinsey & Company in the marketing and sales practice. He served retail, travel, transport and logistics clients on pricing and revenue management related business problems. In addition to solving the analytical problems on the projects, Anand also spent his time helping to build analytics capabilities for the clients. Before McKinsey & Co., Anand was an operations research consultant at Revenue Analytics, Inc. where he helped clients across hospitality, automotive OEMs and automotive retail industry on solving problems like channel optimization, inventory and supply chain optimization, and pricing and revenue management.

Anand holds an MS in operations research from University at Buffalo, SUNY and a bachelors in mechanical engineering from VNIT, Nagpur India.