Telecom_Churn_Project _Summary.txt

Client:Mobicom
Domain:Telecom
Designation:Business Analyst

Project Details:
Mobicom is concerned that the market environment of rising churn rates and declining ARPU will hit them hard as churn rate at Mobicom is relatively high. The management team is keen to take initiatives on this front. One of these is to roll out targeted proactive retention programs, which include usage enhancing marketing programs to increase minutes of usage (MOU), rate plan migration, and a bundling strategy among others.

Data Details:
The data given had 86 attributes of 66297 records, each describing overall usage of each customers,their demo graphic information and if they have unsubscribed their service or not as the churn column.

Techniques Used:
Descriptive Statistics.
Variable Profiling.
Logistic Regression.
Clustering.

Role:
Understanding the whole documentation of the business problem and acquiring knowledge about each attributes in the given dataset.
Derived variable to use correct parameter for Network and service quality and Whether customers are on optimal plan or not.
Cleaning data,Deleted all the columns where only 10% of information was given.
Outlier Treatment,Profiling of each variable to impute missing values where required.
Built logistic regression model using backward selection algorithm and validated it. 
Predicted churn rate for each customer.

Suggestion given to client:
Roll out family bundles for families with 7 unique subscribers. 
Roll out plans and special offers for customers who makes retention calls at the earliest as per their grieviances.
Roll out special offers for people with Asian Ethnicity.
Roll out special plans for customers located in NORTHWEST/ROCKY MOUNTAIN AREA and SOUTH FLORIDA AREA. 
Roll out plans to increase data usage by customers is very important.
Roll out rate plan migration strategy for customers with high churn and high overage revenue.
Roll out offers for high monthly usage users and high churn rate