How to build a data driven customer success program that drives growth

Discover the secret to customer health with scientific rigor and an analytical approach to understanding customer churn, adoption, retention, and expansion.

"When it comes to customer success, it's not just a Boston Tea Party - it's a long-term process that requires scientific rigor and analytical understanding to get to the nirvana of customer health and happiness much quicker."

Manasij Ganguli is the founder and CEO of Zap Scale, a software that helps B2B SaaS companies retain customers, reduce churn, and increase upsells. He has two decades of experience in customer success, and is passionate about helping businesses streamline their customer success process.

Manasij Ganguli, a SaaS business owner, realized that retaining customers was a key factor in making a successful business. He and his team spent four years of trial and error to understand what health metrics could measure customer happiness and retention. He wanted to make sure no one had to go through the same hassle, so he developed a data-driven customer success program that would drive growth.

His program uses eight data sources to analyze customer health and create a machine-learning model to predict churn, adoption, retention, and expansion. Manasij's goal is to make customer success easier, faster, and more scientifically valid.

In this episode, you will learn the following:

• Is there value in building out a customer success program if so why
• Why is customer success being conducted in at least some instances without any science behind it
• What metrics and science to look at in order to ensure our customer success program works
• How to unlearn preconceived notions around customer service and success to better serve customers
• How ZapScale caters to the nuances of a particular company and their brand
• How can customer success teams reduce the time it takes for them to understand their customers' health metrics and measure customer happiness?
• What methods can customer success teams use to understand customer health in an SMB or midmarket context where one customer success person is handling hundreds of accounts?
• How can customer success teams use data points such as product usage, ticketing, feature requests, communications, meetings, and NPS to get a better understanding of customer health?
• and much much more ...