Customer Churn Analytics

Objective

The client was looking for an automated solution to provide recommendations for replenishing their stores with various lines of products according to consumer’s buying behavior. A major cost-effective solution was achieved to eliminate the manual intervention in suggesting replenishment orders for their stores.

Approach

  • Data Preparation and analysis of the masked variables. Inclusion and exclusion of variables were done based on various criteria required for the analysis.
  • Applying statistical transformation methods to carefully understand the variabilities within the variables.
  • Conducting Feature transformations to understand its impact on the techniques applied.
  • Understanding the statistical relations through various visualization options.
  • Predicting out the best possible solutions to understand the period of churning out and the impact of it on the business.

Benefits

  • It helped to understand the customer behavior, demographics and usage pattern to detect out the best possibility to understand the churn metrics.
  • It helped to convert structured and unstructured data into meaningful insights to predict the churn capabilities.
  • It helped to identify the various causes of churn and helped strategize to resolve the issues.
  • It ensured more engagement with the customers to foster better relationships.
  •  

Result

  • The churn analysis helped the client to retain existing customers by 3 times more than acquiring new customers.
  • The analysis showed a clear pattern of the risk tolerance of the business with due respect to the churn probability.
  • This also gave a way to measure the customer lifetime value with the churn probabilities in the future for the client.
  • This helped to understand the key trends to improve the product lifecycle.