SAVVY ANALYTIC SOLUTIONS

PORTFOLIO

CHURN MODEL

Data for this simulation was obtained from IBM.


OBJECTIVE


Use Customer Churn Prediction To help Customer Relationship Management (CRM)


Incentivize potential churners

  • Seize opportunity with non-churners

THE SURVEY DATA



BENEFITS


The predictions from the ML model can help in understanding the customers who might leave and their service. With this information the company can do the following:

  • Lower customer churn

  • Reduction of dispute calls

  • Reduce the operational cost of call centers


churn = -1.41 + 0.14(senior_citizen-yes) -0.03(tenure) + 0.92(internet_service-dsl) + 1.82(internet-service-fiber-optic) -0.88(contract-one-year) -1.68(contract-two-year)


CONCLUSIONS: FACTORS OF IMPORTANCE

  • Tenure

  • Contract

  • Paperless Billing

  • Monthly Charges

  • Internet Service






CONCLUSIONS

  • Customers with month-to-month contracts are less likely to churn.

  • Customers with internet service, in particular fiber optic service, are more likely to churn.

  • Customers who have been with the company longer or have paid more in total are less likely to churn.

IMPLEMENTATION


The predictions from the ML model can help in understanding the customers who might leave and their service. With this information the company can do the following:


Daily Feed: Relationship Managers(RM) get a daily feed on who has the propensity to churn and what are the influencing factors

Customer Engagement: Triggers a conversation with the customer and understand their pain points and possibly rectify the situation

Lifetime Value (LTV): Increases customer entanglement as the RM would reach out to them anticipating issues will eventually increase their lifetime value




Featured Posts
Recent Posts