The client was interested in finding out the attrition rate of one of its services by analyzing and identifying the cause of the churn and implementing effective strategies to retain the customers. A specific solution was provided to the client to understand the complexities of the customer churn behavior over specific scenarios.
The challenge was addressed by following the below steps:
Understanding the data flow streams for various product lines and exploring the data statistically.
Analyzing the exploratory analysis for the data and bringing out meaningful insights to identify significant variables to show a considerable effect on the product lines.
Development of various statistical and machine learning models to understand the various transactions containing the products.
Shaping out the methodologies to sort out categories of product lines for the stores.
Generation of priority list of product lines to optimize the product distribution systems by understanding various evaluation metrics.
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.
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.
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.