A project on customer churn

1. CUSTOMER CHURN ANALYSIS
Mustafa Zaki, Jeet Desai, and Hetanshee Shah

2. CONTENTS
Introduction
Methods
Results
Conclusions
Acknowledgements
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3. INTRODUCTION
Churn is a metric that shows customers who stop doing
business with a company or a particular service, also known as
customer attrition. By following this metric, what most
businesses could do was try to understand the reason behind
churn numbers and tackle those factors, with reactive action
plans.
Based on a Jan 2020 article, Accenture reports that 77% of
consumers now retract their loyalty more quickly than they
did three years ago and industry must therefore work
harder than ever to retain their customer bases. Acquisition
costs far outweigh those of keeping current customers,
further motivating companies to implement innovative
strategies to boost customer retention in the telecom
industry.
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4. METHODS
Dataset: The data set has been collected from an Iranian
telecommunication company’s database over a period of 12 months.
It contains 3150 customer records.
Data Preprocessing:
There may be various incorrect and missing elements in the raw data.
Data cleaning is used to deal with this aspect.
1. Outlier Detection:
- Used Z-score method to detect outliers and replaced them with the
median.
2. Checking for null values:
- We impute median values in place of null values.
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7. Decision Tree: It is supervised machine learning that
categorizes or predicts outcomes based on the answers to a
previous set of questions. Decision Tree yields following
results:
Logistic Regression: Logistic regression is an example of
supervised learning. It is used to calculate or predict the
probability of a binary (yes/no) event occurring. An example
of logistic regression could be applying machine learning to
determine if a person is likely to be infected with COVID-19 or
not.
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Accuracy AUC Precision Recall F1-Score
99.7 100 100 98.6 99.3
Accuracy AUC Precision Recall F1-Score

8. Support vector classifier(svc): it is a linear model that
can be used to solve classification and regression
problems. It can solve both linear and nonlinear
problems. The algorithm generates a line or hyper-plane
that divides the data into categories. Support vector
classifier yields following results:
Since our data is class imbalanced, we majorly rely on f1-
score, recall and precision.
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Accuracy AUC Precision Recall F1-Score
90.37 91.61 84.33 47.29 60.6

9. RESULTS
In this analysis we experimented with three classification techniques using
an Iranian telecommunication company dataset. Decision Tress performed
the best on the data set. This study also investigated that complaints and
customer status are the major factors that influence customer churn
significantly. Based on these factors, we suggest potential approaches that
will enable the telecom company reduce the customer attrition rate
considerably.
Approaches for using customer status as an alarm to churn potential:
a) Monitor the change in customer status as an alarm to churn potential.
b) Provide special offers and services to customer with inactive status.
c) Identify factors that make the customer status inactive and try to avoid
them.

10. Since our data is class imbalanced, we majorly rely on F1-
Score, Recall and Precision.
Approaches to avoiding customer churn due to dissatisfaction:
a) Conduct direct and indirect polling to determine customer
expectations and perceptions about operator services.
b) Consider programs for rewarding long-term customers as
lucrative assets of the organization.
c) Try to improve network coverage
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11. CONCLUSION
Lack of access to different types of data is the main
limitation of this study, for example service costs,
geographic location, types of service(phone/internet),
dependence of customer recount as an important factor of
customer churn. Having multiple service providers data
could be very useful for understanding customer retention
behavior more thoroughly. Overcoming these limitations
can be done in future research. Also, customer probable
churn time could be considered. Using time series
methods can be useful in extracting churn prediction
function and calculating customer churn probability in
certain time interval.
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12. ACKNOWLEDGEMENTS
Dataset Link: https://tinyurl.com/TelecomCustomerChurnDataset
Automating EDA using Pandas Profiling, Sweetviz and Autoviz in Python
(analyticsindiamag.com)
Customer Retention Strategies in Telecom Industry | TechSee
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