Applying Predictive Analytics to Improve SaaS Churn Reduction
The Business Challenge:
A Software as a Service (SaaS) client was looking for a way to reduce churn and improve customer retention. The organization had purchased Looker, a Business Intelligence tool, to help them identify cohorts at risk of churning. This would allow the customer success teams to proactively reach out to different cohorts and take actions to retain those customers. However, when they tried to use Looker’s pre-built reports to identify cohorts at risk of churn, the reports provided a huge number of customers, far more than the customer success team could reach out to, and few meaningful ways to cull the enormous list. It was impossible for the customer success team to respond to all of the customers at risk of churn, or to sort that list in such a way as to prioritize one set of customers to respond to over another.
The Komodo Solution:
The client had an intuitive sense of what types of information would indicate a customer was at-risk of churning--customers whose renewal dates were coming up, who were not very active with the product, and more. However, the data that would help them identify whether a customer was at risk was stored in many different places, and Looker could not integrate all these different data sources. Komodo built a data pipeline to pull information from multiple sources into a single database so that Looker could produce reports on sets of cohorts based on the client’s needs, rather than on what was programmed into the software as a catch-all for all different types of businesses.
The Results:
With the reporting and an analysis to detect customer churn set up, the client was able to create targeted win-back campaigns within one month of Komodo’s engagement.
“With the reporting and an analysis to detect customer churn set up, the client was able to create targeted winback campaigns within one month of Komodo’s engagement.”
