APPROACH

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 define 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

RESULTS

Our final 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.