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

                    KEY BENEFITS

                    • The predictive and prescriptive nature of our solution help zero in on customers at-risk, before they become inactive or attrite
                    • The solution accuracy ensures higher percentage of correct leads, and allows proactive targeting of customers who are likely to churn and retain


                    Our ?nal solution enabled the client to realize a reduction in churn rate by 17% and a gross incremental revenue of $150MM over a period of 12 months.