Data-Driven Artificial Intelligence (AI) for Churn Reduction

 By opendatascience - 2020-12-03

Description

A telecommunications company was losing customers (churn rate was 49.9%) and wanted to identify why customers were leaving them. Using data-driven Artificial Intelligence (AI), the key reasons for cus

Summary

  • A telecommunications company was losing customers (churn rate was 49.9%) and wanted to identify why customers were leaving them.
  • As we can see, FiberOptic, DSL and MonthtoMonthContract have the highest coefficients and therefore are the top 3 influential variables driving churn.
  • The overall accuracy of the Churn Model was 0.7415, this means that 74% of customers were accurately identified as churners or no-churners.
  • The telecommunication was able to reduce customers from leaving them by investigating their ‘FiberOptic’ and ‘DSL’ services and through the discovery that the ‘FiberOptic’ and ‘DSL’ speed was extremely slow and often caused interruptions to TV shows and Moving Viewing.

 

Topics

  1. Backend (0.23)
  2. Machine_Learning (0.18)
  3. Database (0.15)

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