Telcom churn .pptx
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The document discusses predicting customer churn in the telecom industry. It analyzes customer data using machine learning models to identify customers at risk of leaving. It finds that an XGB classifier model achieves the highest accuracy of 82% in predicting customer churn. The document recommends conducting regular customer feedback surveys and analyzing changes in plans to reduce churn and help telecom companies increase profits.
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Telecom Churn Analysis
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
MIS637_Final_Project_Rahul_Bhatia
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
Customer churn prediction for telecom data set.
This document analyzes customer data from a telecom company to predict customer churn and develop retention programs. Both logistic regression and random forest models were tested, with random forest found to have slightly higher accuracy at 83% versus 79% for logistic regression. The top four factors influencing churn were identified as total spending in 2017, data consumption, off-net spending, and customer tenure. The document recommends introducing attractive SMS packages, new off-net calling schemes, and more flexible data schemes tailored to individual customer spending to help increase customer retention.
Churn prediction
This document discusses various computational intelligence methods for predicting customer churn in telecommunication companies. It begins by introducing the problem of high customer churn rates in a competitive telecom market. It then discusses approaches like basic classifiers, data preprocessing techniques, and ensembles of classifiers. The document evaluates several specific techniques - multilayer perceptrons, genetic programming, self-organizing maps, and negative correlation learning. It concludes by discussing future work areas and published research applying these methods to improve churn prediction.
Customer_Churn_prediction.pptx
The document discusses building a machine learning model to predict customer churn for a telecommunications company using a dataset containing customer characteristics. It describes preprocessing the data, exploring the features, training various classification models including logistic regression, support vector machines, random forests and decision trees, and evaluating model performance. Logistic regression achieved the best results with 79% accuracy at predicting whether customers will churn. Future work could include reducing more features and testing additional models to improve accuracy for predicting telecom customer churn.
Telecom customer churn prediction
1. The document describes a project to predict customer churn for a telecom company using logistic regression, KNN, and Naive Bayes models.
2. Exploratory data analysis was conducted on usage, contract, payment and other customer data, finding some variable correlation.
3. Logistic regression performed best with 75% accuracy. KNN accuracy was also good with K=2.
4. The models identified contract renewal and monthly charges as critical factors for churn, suggesting the company focus on these areas.
churn prediction in telecom
The document discusses machine learning algorithms for predicting customer churn in a prepaid mobile network. It presents an overview of supervised and unsupervised learning techniques including support vector machines, k-nearest neighbors, neural networks, decision trees and naive Bayes. The document outlines features for a churn prediction model, describes a demo of the model using different algorithms, and evaluates the classification accuracy and churn rates.
Telecom Churn Prediction
UT had the highest difference in average international call minutes between churning and non-churning customers, indicating international call rates were a likely reason for churn. Several other states also showed differences in international minutes, suggesting international plan rates affected churn. The report analyzed call minute differences and built a logistic regression model to predict churn behavior from the telecom usage data.
Recommended
Telecom Churn Analysis
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
MIS637_Final_Project_Rahul_Bhatia
- The document describes a project to predict customer churn for a telecom company using classification algorithms. It analyzes a dataset of 3333 customers to identify variables that contribute to churn and builds models using KNN and C4.5.
- The C4.5 model achieved higher accuracy (94.9%) than KNN (87.1%) on the test data. Key variables for predicting churn were found to be day minutes, customer service calls, and international plan.
- The model can help the telecom company prevent churn by focusing retention efforts on at-risk customers identified through these important variables.
Customer churn prediction for telecom data set.
This document analyzes customer data from a telecom company to predict customer churn and develop retention programs. Both logistic regression and random forest models were tested, with random forest found to have slightly higher accuracy at 83% versus 79% for logistic regression. The top four factors influencing churn were identified as total spending in 2017, data consumption, off-net spending, and customer tenure. The document recommends introducing attractive SMS packages, new off-net calling schemes, and more flexible data schemes tailored to individual customer spending to help increase customer retention.
Churn prediction
This document discusses various computational intelligence methods for predicting customer churn in telecommunication companies. It begins by introducing the problem of high customer churn rates in a competitive telecom market. It then discusses approaches like basic classifiers, data preprocessing techniques, and ensembles of classifiers. The document evaluates several specific techniques - multilayer perceptrons, genetic programming, self-organizing maps, and negative correlation learning. It concludes by discussing future work areas and published research applying these methods to improve churn prediction.
Customer_Churn_prediction.pptx
The document discusses building a machine learning model to predict customer churn for a telecommunications company using a dataset containing customer characteristics. It describes preprocessing the data, exploring the features, training various classification models including logistic regression, support vector machines, random forests and decision trees, and evaluating model performance. Logistic regression achieved the best results with 79% accuracy at predicting whether customers will churn. Future work could include reducing more features and testing additional models to improve accuracy for predicting telecom customer churn.
Telecom customer churn prediction
1. The document describes a project to predict customer churn for a telecom company using logistic regression, KNN, and Naive Bayes models.
2. Exploratory data analysis was conducted on usage, contract, payment and other customer data, finding some variable correlation.
3. Logistic regression performed best with 75% accuracy. KNN accuracy was also good with K=2.
4. The models identified contract renewal and monthly charges as critical factors for churn, suggesting the company focus on these areas.
churn prediction in telecom
The document discusses machine learning algorithms for predicting customer churn in a prepaid mobile network. It presents an overview of supervised and unsupervised learning techniques including support vector machines, k-nearest neighbors, neural networks, decision trees and naive Bayes. The document outlines features for a churn prediction model, describes a demo of the model using different algorithms, and evaluates the classification accuracy and churn rates.
Telecom Churn Prediction
UT had the highest difference in average international call minutes between churning and non-churning customers, indicating international call rates were a likely reason for churn. Several other states also showed differences in international minutes, suggesting international plan rates affected churn. The report analyzed call minute differences and built a logistic regression model to predict churn behavior from the telecom usage data.
Customer Churn Analysis and Prediction
This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
Customer Churn, A Data Science Use Case in Telecom
This document discusses customer churn prediction in the telecom industry. It outlines the business problem of reducing customer churn and retention costs. It describes collecting internal customer and transaction data as well as external data, preparing the data through ETL, feature selection including filtering and engineering added features, and modeling techniques like gradient boosting, random forest and neural networks. Key metrics for evaluating imbalanced classification models on churn are discussed, as well as balancing model excellence with business priorities in deployment.
Data mining and analysis of customer churn dataset
The document discusses a study conducted by a mobile phone company to analyze factors related to customer churn. The company provided a dataset of 3,332 customer records to build a neural network model that can predict which customers are likely to switch providers. Examining the data showed that increased usage of night, evening, and day minutes, as well as more customer service calls, correlated with higher churn. International calling plans also had a major impact on churn rates. The model achieved a misclassification rate of 7.11% and identified key variables for the company to address to reduce churn, such as international call pricing and infrastructure issues.
Telecom Churn Prediction Presentation
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
Churn in the Telecommunications Industry
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
Ways to Reduce the Customer Churn Rate
This document discusses customer churn, which refers to the rate at which customers leave a company. It states that reducing churn by 5% can boost profits by 75% and that US companies lose $1.6 trillion per year to churn. Common causes of churn include poor customer service, price, functionality, and changing customer needs. The document promotes MECBot, a data analytics product that can detect, prevent, and reduce churn in real-time through capabilities like churn scoring, campaign management, and customer lifetime value maximization.
Customer attrition and churn modeling
This document discusses variables and modeling approaches for customer churn and attrition modeling in banking. It identifies several key factors related to customer churn, including spikes in churn rates at the end of deposit periods and different churn patterns for different account types. It outlines various groups of variables that can be used in modeling, including customer transaction history, demographic and personal profile data, and business-related variables like account balances and transaction amounts. Finally, it reviews several common modeling approaches and their performance based on literature, including decision trees, random forests, support vector machines, logistic regression, and neural networks. Proper customer segmentation is identified as important for precise modeling.
Churn prediction data modeling
This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.
Churn Prediction in Practice
BigData Republic teamed up with VodafoneZiggo and hosted an meetup on churn prediction.
Telecom companies like VodafoneZiggo have long benefited from the fine art/science of predicting churn. Currently, in the booming age of subscription based business models (e.g. Netflix, Spotify, HelloFresh), the importance of predicting churn has become widespread. During this event, VodafoneZiggo shared some of its wisdom with the public, after which BDR Data Scientist Tom de Ruijter presented an overview of the modeling tools at hand, both classical, as well as novel approaches. Finally, the participants engaged in a hands-on session showcasing the implementation of different approaches.
PART 1 — Churn Prediction in Practice by Florian Maas
At VodafoneZiggo we are incredibly excited about Advanced Analytics and the enormous potential for progress and innovation. In our state of the art open source platform we store the tremendous amount of data that is generated every single second in our mobile and fixed networks. This means that we have a vast body of rich information, which if unlocked, can lead to something very special. As a company with a primarily subscription-based service model, churn plays a vital role in the daily business. Not only is the churn rate a good indicator of customer (dis)satisfaction, it is also one out of two factors that determines the steady-state level of active customers. During this talk, we will show how data science provides added value in the process of churn prevention at VodafoneZiggo. We will talk about the data and the modeling approach we use, and the pitfalls and shortcomings that we have encountered while building the model. We will also briefly discuss potential improvements to the current approach, which brings us to talk #2.
PART 2 — The Churn Prediction Toolbox by Tom de Ruijter
The second talk will show you the fine intricacies of predicting churn through different approaches. We’ll start off with an overview of different modeling strategies for describing the problem of churn, both in terms of a classification problem as well as a regression problem. Secondly, Tom will give you insights in how you evaluate a churn model in a way such that business stakeholders know how to act upon the model results. Finally, we’ll work towards the hands-on session demonstrating different model approaches for churn prediction, ranging from classical time series prediction to recurrent neural networks.
Churn customer analysis
Customer churn occurs when customers or subscribers stop doing business with a company or service.
Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers – earning business from new customer’s means working leads all the way through the sales funnel, utilizing your marketing and sales resources throughout the process.
Prediction of customer propensity to churn - Telecom Industry
- A logistic regression model was found to best predict customer churn with the highest AUC and accuracy.
- The top variables increasing churn risk were credit class, handset price, average monthly calls, billing adjustments, household subscribers, call waiting ranges, and dropped/blocked calls.
- Cost and billing variables like charges and usage were significant, validating an independent survey.
- A lift chart showed targeting the highest risk 30% of customers could identify 33% of potential churners. The model allows prioritizing retention efforts on the 20% riskiest customers.
Churn Analysis in Telecom Industry
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
IRJET - Customer Churn Analysis in Telecom Industry
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
Bank churn with Data Science
In this presentation I apply Logistic Regression, Support Vector Machine and Random Forest to gather insights that lead to why leave a bank
Telco churn presentation
This document discusses using machine learning models to predict customer churn for a telco company and provide targeted recommendations to reduce churn. The data was explored and customers were segmented into 5 clusters based on usage. Decision tree and random forest models were used to determine why customers churn and which customers churn for each cluster. Targeted recommendations were then provided for different customer categories within each cluster, such as offering free international minutes, discounted calling rates, or prioritized customer service. The results showed the potential to avert $555,700 in annual revenue loss through reducing customer churn.
Data analytics telecom churn final ppt
This document discusses predicting customer churn for mobile operators to prevent customer loss, increase satisfaction and optimize network usage. It provides details on churn rates in India, data collection methods used including oversampling, and analysis performed on factors like network coverage, call quality and customer support to develop a churn prediction model. The analysis was conducted by a team of 7 people using techniques like p-value analysis, affinity diagramming and tweaking oversampling through trial and error.
Predicting Bank Customer Churn Using Classification
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
Churn modelling
This document outlines a predictive churn modeling process for a telecom company. It describes the business problem of customer churn, variables in the dataset, exploratory data analysis conducted, feature selection, data preprocessing, and development of several models including decision tree, logistic regression, support vector machine, and random forest. Key evaluation metrics like AUC ROC curve and confusion matrix are used to analyze model performance. Finally, it discusses customer lifetime value (CLTV) calculation and segmentation to identify high and low value customers.
Telecommunication Analysis (3 use-cases) with IBM watson analytics
1. The telecommunications company is concerned about customer churn and needs to understand the factors influencing why customers are leaving.
2. The analysis found that the two year contract has very few current customers and is influencing churn. Focusing on modifying the two year contract and its rate plans could increase customers.
3. For cross-selling and up-selling, the analysis found that prepaid customers are not using services broadly. Payment methods should be focused on and policies added to promote the company, such as discounts for cash payments or collaborating with banks.
A case study on churn analysis1
The document discusses a case study report on churn analysis for a telecom service provider. It outlines the business scenario of a telecom provider losing customers and profitability with average churn rates of 8%, 12%, and 15% over three quarters. The solution proposed uses advanced modeling techniques like neural networks and logistic regression to construct a model that scores each customer's probability of churn. The model helps identify high-value customers likely to churn and informs the provider's retention strategy.
Customer churn classification using machine learning techniques
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
Data Mining on Customer Churn Classification
Implemented multiple classifiers to classify if a customer will leave or stay with the company based on multiple independent variables.
Tools used:
> RStudio for Exploratory data analysis, Data Pre-processing and building the models
> Tableau and RStudio for Visualization
> LATEX for documentation
Machine learning models used:
> Random Forest
> C5.0
> Decision tree
> Neural Network
> K-Nearest Neighbour
> Naive Bayes
> Support Vector Machine
Methodology: CRISP-DM
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This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
Customer Churn, A Data Science Use Case in Telecom
This document discusses customer churn prediction in the telecom industry. It outlines the business problem of reducing customer churn and retention costs. It describes collecting internal customer and transaction data as well as external data, preparing the data through ETL, feature selection including filtering and engineering added features, and modeling techniques like gradient boosting, random forest and neural networks. Key metrics for evaluating imbalanced classification models on churn are discussed, as well as balancing model excellence with business priorities in deployment.
Data mining and analysis of customer churn dataset
The document discusses a study conducted by a mobile phone company to analyze factors related to customer churn. The company provided a dataset of 3,332 customer records to build a neural network model that can predict which customers are likely to switch providers. Examining the data showed that increased usage of night, evening, and day minutes, as well as more customer service calls, correlated with higher churn. International calling plans also had a major impact on churn rates. The model achieved a misclassification rate of 7.11% and identified key variables for the company to address to reduce churn, such as international call pricing and infrastructure issues.
Telecom Churn Prediction Presentation
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
Churn in the Telecommunications Industry
Strategic Business Analysis Capstone Project Telecommunications Churn Management
Churn is a significant problem that costs telecommunications companies billions of dollars through lost revenue. Now that the market is more mature, the only way for a company to grow is to take their competitors customers. This issue
combined with the greater choice that consumers have gained means that any adverse touch point with a consumer can result in a lost customer.
Ways to Reduce the Customer Churn Rate
This document discusses customer churn, which refers to the rate at which customers leave a company. It states that reducing churn by 5% can boost profits by 75% and that US companies lose $1.6 trillion per year to churn. Common causes of churn include poor customer service, price, functionality, and changing customer needs. The document promotes MECBot, a data analytics product that can detect, prevent, and reduce churn in real-time through capabilities like churn scoring, campaign management, and customer lifetime value maximization.
Customer attrition and churn modeling
This document discusses variables and modeling approaches for customer churn and attrition modeling in banking. It identifies several key factors related to customer churn, including spikes in churn rates at the end of deposit periods and different churn patterns for different account types. It outlines various groups of variables that can be used in modeling, including customer transaction history, demographic and personal profile data, and business-related variables like account balances and transaction amounts. Finally, it reviews several common modeling approaches and their performance based on literature, including decision trees, random forests, support vector machines, logistic regression, and neural networks. Proper customer segmentation is identified as important for precise modeling.
Churn prediction data modeling
This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.
Churn Prediction in Practice
BigData Republic teamed up with VodafoneZiggo and hosted an meetup on churn prediction.
Telecom companies like VodafoneZiggo have long benefited from the fine art/science of predicting churn. Currently, in the booming age of subscription based business models (e.g. Netflix, Spotify, HelloFresh), the importance of predicting churn has become widespread. During this event, VodafoneZiggo shared some of its wisdom with the public, after which BDR Data Scientist Tom de Ruijter presented an overview of the modeling tools at hand, both classical, as well as novel approaches. Finally, the participants engaged in a hands-on session showcasing the implementation of different approaches.
PART 1 — Churn Prediction in Practice by Florian Maas
At VodafoneZiggo we are incredibly excited about Advanced Analytics and the enormous potential for progress and innovation. In our state of the art open source platform we store the tremendous amount of data that is generated every single second in our mobile and fixed networks. This means that we have a vast body of rich information, which if unlocked, can lead to something very special. As a company with a primarily subscription-based service model, churn plays a vital role in the daily business. Not only is the churn rate a good indicator of customer (dis)satisfaction, it is also one out of two factors that determines the steady-state level of active customers. During this talk, we will show how data science provides added value in the process of churn prevention at VodafoneZiggo. We will talk about the data and the modeling approach we use, and the pitfalls and shortcomings that we have encountered while building the model. We will also briefly discuss potential improvements to the current approach, which brings us to talk #2.
PART 2 — The Churn Prediction Toolbox by Tom de Ruijter
The second talk will show you the fine intricacies of predicting churn through different approaches. We’ll start off with an overview of different modeling strategies for describing the problem of churn, both in terms of a classification problem as well as a regression problem. Secondly, Tom will give you insights in how you evaluate a churn model in a way such that business stakeholders know how to act upon the model results. Finally, we’ll work towards the hands-on session demonstrating different model approaches for churn prediction, ranging from classical time series prediction to recurrent neural networks.
Churn customer analysis
Customer churn occurs when customers or subscribers stop doing business with a company or service.
Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers – earning business from new customer’s means working leads all the way through the sales funnel, utilizing your marketing and sales resources throughout the process.
Prediction of customer propensity to churn - Telecom Industry
- A logistic regression model was found to best predict customer churn with the highest AUC and accuracy.
- The top variables increasing churn risk were credit class, handset price, average monthly calls, billing adjustments, household subscribers, call waiting ranges, and dropped/blocked calls.
- Cost and billing variables like charges and usage were significant, validating an independent survey.
- A lift chart showed targeting the highest risk 30% of customers could identify 33% of potential churners. The model allows prioritizing retention efforts on the 20% riskiest customers.
Churn Analysis in Telecom Industry
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
IRJET - Customer Churn Analysis in Telecom Industry
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
Bank churn with Data Science
In this presentation I apply Logistic Regression, Support Vector Machine and Random Forest to gather insights that lead to why leave a bank
Telco churn presentation
This document discusses using machine learning models to predict customer churn for a telco company and provide targeted recommendations to reduce churn. The data was explored and customers were segmented into 5 clusters based on usage. Decision tree and random forest models were used to determine why customers churn and which customers churn for each cluster. Targeted recommendations were then provided for different customer categories within each cluster, such as offering free international minutes, discounted calling rates, or prioritized customer service. The results showed the potential to avert $555,700 in annual revenue loss through reducing customer churn.
Data analytics telecom churn final ppt
This document discusses predicting customer churn for mobile operators to prevent customer loss, increase satisfaction and optimize network usage. It provides details on churn rates in India, data collection methods used including oversampling, and analysis performed on factors like network coverage, call quality and customer support to develop a churn prediction model. The analysis was conducted by a team of 7 people using techniques like p-value analysis, affinity diagramming and tweaking oversampling through trial and error.
Predicting Bank Customer Churn Using Classification
This document describes a study that used classification models to predict customer churn for a bank. The authors collected a dataset of 10,000 bank customers from Kaggle and preprocessed the data. They then explored relationships between features and the target variable of whether a customer churned. Two classification models were tested - KNN and Decision Tree. After hyperparameter tuning, Decision Tree achieved the best accuracy of 84.25%, outperforming KNN. However, both models struggled to accurately predict customers who would churn. The authors concluded Decision Tree was the best model but recommend collecting more data on churning customers.
Churn modelling
This document outlines a predictive churn modeling process for a telecom company. It describes the business problem of customer churn, variables in the dataset, exploratory data analysis conducted, feature selection, data preprocessing, and development of several models including decision tree, logistic regression, support vector machine, and random forest. Key evaluation metrics like AUC ROC curve and confusion matrix are used to analyze model performance. Finally, it discusses customer lifetime value (CLTV) calculation and segmentation to identify high and low value customers.
Telecommunication Analysis (3 use-cases) with IBM watson analytics
1. The telecommunications company is concerned about customer churn and needs to understand the factors influencing why customers are leaving.
2. The analysis found that the two year contract has very few current customers and is influencing churn. Focusing on modifying the two year contract and its rate plans could increase customers.
3. For cross-selling and up-selling, the analysis found that prepaid customers are not using services broadly. Payment methods should be focused on and policies added to promote the company, such as discounts for cash payments or collaborating with banks.
A case study on churn analysis1
The document discusses a case study report on churn analysis for a telecom service provider. It outlines the business scenario of a telecom provider losing customers and profitability with average churn rates of 8%, 12%, and 15% over three quarters. The solution proposed uses advanced modeling techniques like neural networks and logistic regression to construct a model that scores each customer's probability of churn. The model helps identify high-value customers likely to churn and informs the provider's retention strategy.
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Similar to Telcom churn .pptx
Customer churn classification using machine learning techniques
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
Data Mining on Customer Churn Classification
Implemented multiple classifiers to classify if a customer will leave or stay with the company based on multiple independent variables.
Tools used:
> RStudio for Exploratory data analysis, Data Pre-processing and building the models
> Tableau and RStudio for Visualization
> LATEX for documentation
Machine learning models used:
> Random Forest
> C5.0
> Decision tree
> Neural Network
> K-Nearest Neighbour
> Naive Bayes
> Support Vector Machine
Methodology: CRISP-DM
Predictive modelling
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive analytics uses techniques from data mining, statistics, modeling, machine learning and AI to analyze current data to predict future events. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating model performance. Predictive models are commonly used to predict customer behavior, risk levels, and other business outcomes.
Project crm submission sonali
Customer churn has been evolving as one of the major problems for financial organizations. The incessant competitions in the market and high cost of acquiring new customers have made organizations to drive their focus towards more effective customer retention strategies.
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORS
This document summarizes research on customer churn prediction and retention strategies in the telecommunications industry. It provides an abstract for a research paper on using a hybrid machine learning classifier and rule engine to predict customer churn and recommend retention plans. The document then reviews related work applying techniques like fuzzy logic, ordinal regression, artificial neural networks, and ensemble methods to predict churn. It outlines the problem of predicting churn and proposing recommendation rules. The proposed solution is a hybrid machine learning model to predict churn with high accuracy and explain reasons for churn to help recommend plans to retain customers.
Predicting churn with filter-based techniques and deep learning
Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.
EVALUTION OF CHURN PREDICTING PROCESS USING CUSTOMER BEHAVIOUR PATTERN
This document summarizes research on predicting customer churn in the telecommunications industry. It first defines customer churn as the rate at which customers stop doing business with a company. It then reviews several past studies that have used techniques like decision trees, neural networks, and data mining to predict churn. The proposed research aims to develop a new churn prediction model using natural language processing (NLP) and machine learning approaches to improve accuracy. It will identify customer behavior patterns and evaluate factors that influence prediction accuracy. The model will be trained and tested on a telecommunications data set to calculate churn rates on both monthly and daily bases. This will help enhance customer service. Gaps in past research identified include issues with imbalanced data, high error rates, and
PROVIDING A METHOD FOR DETERMINING THE INDEX OF CUSTOMER CHURN IN INDUSTRY
Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
Machine Learning Approaches to Predict Customer Churn in Telecommunications I...
This project aimed to develop machine learning models to predict customer churn in the telecommunications industry. Four algorithms were evaluated - logistic regression, support vector machine, decision tree, and random forest. Logistic regression performed best with an accuracy of 79.25% and AUC score of 84.08%. The models analyzed customer attribute data to identify patterns and predict churn, helping telecom companies understand churn reasons and develop retention strategies. The results provide insights to improve customer experience and reduce costly customer churn.
CUSTOMER CHURN PREDICTION
This document discusses customer churn prediction using machine learning and deep learning models. It summarizes the work of four students who developed an artificial neural network model to predict customer churn. The model aims to identify which customers will continue using a service and which will cancel their subscription. The paper compares the performance of the ANN model to other machine learning algorithms like random forest, logistic regression, support vector machines, and decision trees. It finds the deep learning model achieved better classification accuracy than the other models. The final predicted results are displayed in a live dashboard for visualization.
69.pdf
a very important document for educators and students as well to get a vast knowledge about the respective course.
Insurance Churn Prediction Data Analysis Project
Dive deep into the world of insurance churn prediction with this captivating data analysis project presented by Boston Institute of Analytics. Our talented students embark on a journey to unravel the mysteries behind customer churn in the insurance industry, leveraging advanced data analysis techniques to forecast and anticipate customer behavior. From analyzing historical data and customer demographics to identifying predictive indicators and developing churn prediction models, this project offers a comprehensive exploration of the factors influencing insurance churn dynamics. Gain valuable insights and actionable recommendations derived from rigorous data analysis, presented in an engaging and informative format. Don't miss this opportunity to delve into the fascinating realm of data analysis and unlock new perspectives on insurance churn prediction. Explore the project now and embark on a journey of discovery with Boston Institute of Analytics. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Data Mining to Classify Telco Churners
Many customers often switch or unsubscribe (churn) from their telecom providers for a variety of reasons. These could range from unsatisfactory service, better pricing from competitors, customers moving to different cities etc. Therefore, telecom companies are interested in analyzing the patterns for customers who churn from their services and use the resultant analysis to determine in the future which customers are more likely to unsubscribe from their services. One such company is Telco Systems. Telco Systems is interested in identifying the precise patterns for their churning customers and have provided the customer data for this project.
Projection pursuit Random Forest using discriminant feature analysis model fo...
A major and demand issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who attrites from the current provider to competitors searching for better service offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial than gain newly recruited customers. Researchers and practitioners are paying great attention to developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) while, the second method used Linear Discriminant Analysis (LDA) to achieve linear splitting of variables during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It is found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Lift, H-measure, AUC. Moreover, PPForest based on LDA delivers effective evaluators in the prediction model.
Is deep learning is a game changer for marketing analytics
This document provides an overview of deep learning including its definition, use cases in marketing, and comparisons to traditional analytics methods. It discusses how deep learning models can be used for segmentation, brand optimization, and media strategy. The document also outlines experiments comparing the predictive accuracy of deep learning models to traditional regression for credit card choices, finding deep learning models achieved up to 73% accuracy compared to 70.5% for regression.
churn_detection.pptx
The document discusses building a customer churn prediction model for a telecom company in Syria using machine learning techniques. It proposes using the XGBoost algorithm to classify customers as churners or non-churners based on their customer data over 9 months. XGBoost builds sequential decision trees and increases the weights of misclassified variables to improve predictive performance. The model achieved an AUC of 93.3% and incorporated social network features to further enhance results. The document outlines the hardware, software and methodology used to develop and test the model on a large dataset from SyriaTel to predict customer churn.
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...
1. The document presents a comparative analysis of machine learning algorithms for predicting customer churn in the telecom industry.
2. Logistic regression, random forest, and balanced random forest classifiers were evaluated on a dataset of 25,000 customers described by 111 variables.
3. The balanced logistic regression model that used SMOTE to address class imbalance achieved the best performance with an area under the ROC curve of 0.861, accurately predicting churn with an accuracy of 77% and recall of 76% on the test set.
¿Como los modelos predictivos cambian los negocios?
Un acercamiento al estudio de modelos predictivos para mover la forma en la que analizamos los negocios
ML_project_ppt.pdf
This document summarizes a study analyzing customer churn in an Iranian telecommunications company. It describes the methods used, including preprocessing the dataset, feature selection, and applying decision trees, logistic regression, and support vector classification models. Decision trees performed best. The results show that complaints and customer status are major factors influencing churn. The conclusions recommend approaches like monitoring customer status changes and offering promotions to inactive customers to reduce churn.
Automated Feature Selection and Churn Prediction using Deep Learning Models
This document discusses using deep learning models for churn prediction in the telecommunications industry. It begins with an introduction to churn prediction and feature selection challenges. It then provides an overview of deep learning techniques, including artificial neural networks, convolutional neural networks, and their applications. The document proposes three deep learning architectures for churn prediction and experiments with them on two telecom datasets. The results show deep learning models can achieve performance comparable to traditional models without manual feature engineering.
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Customer churn classification using machine learning techniques
Customer churn classification using machine learning techniques
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORS
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORS
Predicting churn with filter-based techniques and deep learning
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EVALUTION OF CHURN PREDICTING PROCESS USING CUSTOMER BEHAVIOUR PATTERN
EVALUTION OF CHURN PREDICTING PROCESS USING CUSTOMER BEHAVIOUR PATTERN
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Machine Learning Approaches to Predict Customer Churn in Telecommunications I...
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Projection pursuit Random Forest using discriminant feature analysis model fo...
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Is deep learning is a game changer for marketing analytics
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¿Como los modelos predictivos cambian los negocios?
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Telcom churn .pptx
- 1. Telecom Churn Prediction STUDENT ID UNIVERSITY NAME
- 2. Introduction Customer Churn is common issue prevailing in service sector. This issue is highly faced by credit card companies, cable services, telecom services etc. The customer churn metrics aid in the improvement of customer retention. To predict customer churn in telecom, companies typically collect and analyze large amounts of customer data, such as call data records, billing data, usage patterns, customer demographics, and customer service interactions. The data is then used to build predictive models that can identify customers who are at risk of leaving the service.
- 3. Aim To predict the customer churn among Telecom industry Framing out key variables used for churn prediction Usage of machine learning models to build, evaluate and predict the customer churn Estimating optimal machine learning model for customer churn
- 4. Dataset description The source dataset is basically a comma separated variable. There are totally 38 variables in the dataset Churn variable illustrates whether the customer is churned or not Churn is the target variable
- 6. Methods Exploratory data analysis (EDA) method was used in this study to analyse the data. Customer was the control variable Model building which includes defining the purpose if model, determine the model boundary, build the model, create an interface and export the model. Evaluating machine learning algorithm is an essential part of project.
- 7. Methods The data was analyzed using python software The libraries used are Numpy, pandas, plotly, matplotlib, seaborn, sklearn. Exploratory data analysis (EDA) is an approach to analyse data sets & to summarize their main characteristics, often with visual methods. A Statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modelling or hypothesis
- 10. Histogram of categorical variables
- 12. Heat map
- 13. Box plot by customer churn status
- 14. Visualisation
- 15. Accuracy of Various Models Models Accuracy Random forest classifier 78% Logistic regression 78% Gaussian NB 36% Decision tree classifier 77% XGB classifier 82%
- 16. XGB classifier
- 17. Accuracy
- 18. Recommendations Offering better service for the customers Regular collection of feedback and surveys with the customer will reduce the churn While taking the any change in plans, it is better to conduct a customer survey, to just predict the positive and negative share of that plan. If it is negative, it is best not to implement.
- 19. Conclusion The importance of this type of research in the telecom market is to help companies make more profit. It has become known that predicting churn is one of the most important sources of income to Telecom companies. Hence, this research aimed to build a system that predicts the churn of customers i telecom company. These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 80% for training and 20% for testing.
Editor's Notes
- Most of the customer were found to be retained especially young aged customers. Churning was highly seen in elderly customers.
- Correlation: Dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data. With the help of Correlation matrix, we can find interdependency between variables 1)Least dependency of variables for predicting churn are tenure and contract. 2)Churn variable is depending more on monthly charges.
- We found outliers in exiting customers which is out of whiskers. An Outlier is an observation that is numerically distant from the rest of the data. • Using Skew() method we found that Churn data is inconsistent with tenure • Customers who disconnecting their subscription plans are selecting short tenure Telecom Company need to offer better plans for those customers who choose short tenures.
- Churning was largely seen among Single customers than Married. Most of the married customers stayed within the company. There are no gender biases in churning status among customers.