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An integrated approach to renew software contract using machine learning

Shylu John, , Varun Dixit, Amol Wani
Published in Taylor & Francis

Contract renewal is critical to maintaining a company’s recurring revenue source. Therefore, there is a significant emphasis on setting up an efficient process for renewal. In this study, a machine learning technique was followed to improve contract renewal rates. In addition to this, key factors affecting renewal rates were also studied in detail. The solution presented in this study used an unsupervised machine learning technique to segment high-risk resellers with relatively lower probability of renewal, which was further actioned upon by a proactive contact strategy soliciting a contract renewal. This solution was tested and monitored for a period of three quarters. It resulted in an incremental improvement in the renewal rate for the company. As part of the implementation, a user interface application was also developed, which enabled the sales specialist to list and contact high-risk (or underperformer) resellers quarter-on-quarter.

About the journal
JournalJournal of Business Analytics
PublisherTaylor & Francis
Open AccessNo