Enabled revenue lift through predictive churn modelling for a leading equipment rental company

We leveraged our knowledge in marketing analytics to tackle this challenge in a methodical manner:

• We segmented customer accounts based on purchase behavior
• Analyzed order gaps to de?ne the at-risk periods for accounts and determine customer samples for the churn model
• This was followed by identifying factors that led to customer accounts becoming inactive
• These inputs were then used to create a predictive model and perform validation against various time frames as well as the existing model