The document analyzes customer data from QWE Inc. to predict customer churn. Logistic regression analysis identified three key factors for predicting churn probability: Customer Happiness Index in December, change in Customer Happiness Index from November to December, and change in days since last login between the two months. The analysis calculates churn probabilities for three sample customers based on these factors. It also provides a list of the top 10 customers most likely to churn. While decision tree analysis validates the logistic regression findings, logistic regression is recommended as the best model for QWE to use going forward to identify at-risk customers and reduce churn.
- The document presents the results of a predictive model built to identify customers at high risk (80%) of churning from their services in the next two months. The top three predictors of churn were identified as days since last login, number of logins, and account age.
- A decision tree model was recommended over logistic regression due to its ability to provide a shortlist of high-risk customers with at least 67% accuracy, allowing for more cost-effective targeting. The top customers to focus on engaging have accounts less than 22 months old.
- To reduce churn, the company should prioritize engaging primary target customers identified by the model through phone calls for feedback, and send promotional emails to secondary targets about new features
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.
The document discusses customer churn risk and how to develop predictive churn models. It defines risk as having two components: uncertainty and exposure to that uncertainty. When building a churn model, the key steps are: defining active vs churned customers, selecting relevant customer data, analyzing characteristics to identify predictors, developing a predictive score using methods like logistic regression, and evaluating the model's ability to identify customers likely to churn. The goal of a churn model is to provide insights for preventing churn, not just statistical precision.
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.
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.
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.
Data mining and analysis of customer churn datasetRohan Choksi
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.
- The document presents the results of a predictive model built to identify customers at high risk (80%) of churning from their services in the next two months. The top three predictors of churn were identified as days since last login, number of logins, and account age.
- A decision tree model was recommended over logistic regression due to its ability to provide a shortlist of high-risk customers with at least 67% accuracy, allowing for more cost-effective targeting. The top customers to focus on engaging have accounts less than 22 months old.
- To reduce churn, the company should prioritize engaging primary target customers identified by the model through phone calls for feedback, and send promotional emails to secondary targets about new features
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.
The document discusses customer churn risk and how to develop predictive churn models. It defines risk as having two components: uncertainty and exposure to that uncertainty. When building a churn model, the key steps are: defining active vs churned customers, selecting relevant customer data, analyzing characteristics to identify predictors, developing a predictive score using methods like logistic regression, and evaluating the model's ability to identify customers likely to churn. The goal of a churn model is to provide insights for preventing churn, not just statistical precision.
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.
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.
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.
Data mining and analysis of customer churn datasetRohan Choksi
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.
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.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
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.
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
This document outlines a 3-step approach to data analytics: 1) data exploration to understand the properties, limitations, and transformations needed for the given datasets, 2) model development including formulating hypotheses, statistical testing, and model performance testing, and 3) interpretation and reporting of results including visual presentation of main variables and coefficients from a commercial perspective to support arguments.
Ayush sharma ,sprocket central report pptAyush Sharma
This data analytics report analyzes customer datasets from Sprocket Central to identify and recommend the top 1000 customers to target. Key findings include that most new and old customers are aged 40-49, females make up the majority of bike purchases, and customers in manufacturing and financial services represent the largest groups. RFM analysis identified customers who recently purchased frequently and generated high revenues as most valuable, including those classified as "Platinum Customer" and "very loyal." The report concludes by recommending the top 1000 customers to target based on this analysis.
Introducing Customer Churn Prevention Powerpoint Presentation Slides. Discuss various ways through which a company can manage customer churn with this PPT slide deck. Showcase methods and ways by which a company can prevent the customer from reducing their purchase of products and services. Our readily available PPT slide deck helps to present the types of customer churn, methods to handle customer attrition, the impact of successful implementation of churn management, dashboard, churn propensity model, etc. Take the assistance of customer churn management PPT slideshow to depict several ways by which a firm can experience customer churn such as when customers stop spending, churn due to product quality, etc. Showcase four stages of customer churn management which allow the company to handle customer attrition. Present how the firm can prevent customer churn by using customer churn analysis PPT infographics. You can easily highlight information about the various marketing campaigns in order to retain its customer from churning. Provide ways to prevent churn through predictive analysis by incorporating our professionally designed customer churn prediction PPT presentation. https://bit.ly/3p6AR7S
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!!
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.
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.
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.
- 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.Kuldeep Mahani
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.
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
- The document provides information about Konigsbrau A.G., a large brewery based in Munich, and its Ukrainian subsidiary Konigsbrau TAK.
- Wolfgang Keller has turned the Ukrainian subsidiary around in 3 years as managing director but has a hands-on leadership style that lacks delegation.
- Dmitri Brodsky was hired as commercial director and has a more formal, hands-off style that conflicts with Keller's approach.
- Problems have arisen between Keller and Brodsky regarding performance evaluations and management style differences.
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.
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.
This is a practical project demonstrating the entire workflow of data analysis from raw data assessment to the final modeling. It filled with detailed storytelling of the different variables of the target company's consumer sales data with the aid of the Tableau data visualization technique.
Uses of Business Analytics in the Telecom IndustryAhannaHerbert
The document discusses the use of business analytics in the telecom industry. It describes how analytics help telecom companies improve customer experience, reduce unnecessary service calls, analyze new product offerings, and reduce customer churn. Specific applications of analytics mentioned include social media analysis, network optimization, predictive modeling, and fraud detection. Major analytics tools used by telecom companies are also listed, including Adobe Systems, SAP, HCL, IBM, and Oracle.
HP faced problems with high costs and complexity from managing a large and diverse product portfolio. [1] They used operations research techniques to analyze cost structures and drivers throughout products' lifecycles. [2] This allowed them to use ROI calculations to screen new product proposals and identify $11 million in savings by eliminating 3300 underperforming products. [3] The changes reduced order cycle times, improved profits by $500 million over three years, and increased customer satisfaction by bringing more structure and data-driven decision making to portfolio management.
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.
1) The document describes a project analyzing FICO credit score data using linear regression to determine which demographic variables influence an individual's score.
2) Through variable selection, the final model included average months in file, maximum delinquency in last 12 months, maximum delinquency ever, and net fraction revolving burden as predictors of FICO score.
3) While the model performed reasonably well, some assumptions like independent errors and homoscedasticity were violated, so the results should be interpreted cautiously despite consistency between training and test sets.
This is a linear regression model my team and I developed using the R programming language that accurately predicts an individual's FICO credit score based on several variables. The data set is comprised of 10,000 observations provided by FICO Analytic Cloud. I was the project team lead for this project.
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.
Predicting Bank Customer Churn Using ClassificationVishva Abeyrathne
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.
This document summarizes key insights from a McKinsey presentation on customer journey analytics and big data. It finds that companies are storing large amounts of data but few know how to extract value from it. Analyzing customer journeys rather than individual touchpoints provides more predictive insights into customer satisfaction and churn. Mapping important customer journeys in an industry reveals opportunities to improve the customer experience and reduce costs. The presentation provides an example of a retail bank that identified ways to decrease service costs and improve customer satisfaction by analyzing its customer journey data.
This document outlines a 3-step approach to data analytics: 1) data exploration to understand the properties, limitations, and transformations needed for the given datasets, 2) model development including formulating hypotheses, statistical testing, and model performance testing, and 3) interpretation and reporting of results including visual presentation of main variables and coefficients from a commercial perspective to support arguments.
Ayush sharma ,sprocket central report pptAyush Sharma
This data analytics report analyzes customer datasets from Sprocket Central to identify and recommend the top 1000 customers to target. Key findings include that most new and old customers are aged 40-49, females make up the majority of bike purchases, and customers in manufacturing and financial services represent the largest groups. RFM analysis identified customers who recently purchased frequently and generated high revenues as most valuable, including those classified as "Platinum Customer" and "very loyal." The report concludes by recommending the top 1000 customers to target based on this analysis.
Introducing Customer Churn Prevention Powerpoint Presentation Slides. Discuss various ways through which a company can manage customer churn with this PPT slide deck. Showcase methods and ways by which a company can prevent the customer from reducing their purchase of products and services. Our readily available PPT slide deck helps to present the types of customer churn, methods to handle customer attrition, the impact of successful implementation of churn management, dashboard, churn propensity model, etc. Take the assistance of customer churn management PPT slideshow to depict several ways by which a firm can experience customer churn such as when customers stop spending, churn due to product quality, etc. Showcase four stages of customer churn management which allow the company to handle customer attrition. Present how the firm can prevent customer churn by using customer churn analysis PPT infographics. You can easily highlight information about the various marketing campaigns in order to retain its customer from churning. Provide ways to prevent churn through predictive analysis by incorporating our professionally designed customer churn prediction PPT presentation. https://bit.ly/3p6AR7S
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!!
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.
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.
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.
- 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.Kuldeep Mahani
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.
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
- The document provides information about Konigsbrau A.G., a large brewery based in Munich, and its Ukrainian subsidiary Konigsbrau TAK.
- Wolfgang Keller has turned the Ukrainian subsidiary around in 3 years as managing director but has a hands-on leadership style that lacks delegation.
- Dmitri Brodsky was hired as commercial director and has a more formal, hands-off style that conflicts with Keller's approach.
- Problems have arisen between Keller and Brodsky regarding performance evaluations and management style differences.
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.
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.
This is a practical project demonstrating the entire workflow of data analysis from raw data assessment to the final modeling. It filled with detailed storytelling of the different variables of the target company's consumer sales data with the aid of the Tableau data visualization technique.
Uses of Business Analytics in the Telecom IndustryAhannaHerbert
The document discusses the use of business analytics in the telecom industry. It describes how analytics help telecom companies improve customer experience, reduce unnecessary service calls, analyze new product offerings, and reduce customer churn. Specific applications of analytics mentioned include social media analysis, network optimization, predictive modeling, and fraud detection. Major analytics tools used by telecom companies are also listed, including Adobe Systems, SAP, HCL, IBM, and Oracle.
HP faced problems with high costs and complexity from managing a large and diverse product portfolio. [1] They used operations research techniques to analyze cost structures and drivers throughout products' lifecycles. [2] This allowed them to use ROI calculations to screen new product proposals and identify $11 million in savings by eliminating 3300 underperforming products. [3] The changes reduced order cycle times, improved profits by $500 million over three years, and increased customer satisfaction by bringing more structure and data-driven decision making to portfolio management.
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.
1) The document describes a project analyzing FICO credit score data using linear regression to determine which demographic variables influence an individual's score.
2) Through variable selection, the final model included average months in file, maximum delinquency in last 12 months, maximum delinquency ever, and net fraction revolving burden as predictors of FICO score.
3) While the model performed reasonably well, some assumptions like independent errors and homoscedasticity were violated, so the results should be interpreted cautiously despite consistency between training and test sets.
This is a linear regression model my team and I developed using the R programming language that accurately predicts an individual's FICO credit score based on several variables. The data set is comprised of 10,000 observations provided by FICO Analytic Cloud. I was the project team lead for this project.
The objective was to develop a neural network model to predict loan defaults using variables like number of derogatory reports, number of delinquent credit lines, debt-to-income ratio, age of oldest credit line, and loan divided by value. The best model had a training profit of $25.48 million and testing profit of $27.48 million, outperforming logistic regression. Key changes were reducing variables and lowering the probability cutoff, which increased profits and lift. The neural network model was simpler, more consistent and predictable than complex models, with training and testing profits varying less than $150,000.
Supply Chain Metrics That Matter: A Closer Look at the Cash-To-Cash Cycle (20...Lora Cecere
Executive Overview
When it comes to metrics that matter, the cash-to-cash cycle is one of the top metrics cited by supply chain professionals. It is among the best financial metrics to provide a comprehensive picture of a company’s supply chain and the management of working capital.
The supply chain is a complex system. Successful management requires both orchestration and balance. To drive supply chain excellence, companies are required to balance four competing priorities: growth, profitability, cycle management and complexity. Several popular metrics, including the cash-to-cash cycle, for a variety of industries are presented in table 1.
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
This document summarizes a study that used logistic regression to predict the probability of a second date between speed dating participants. It used variables like age, attractiveness ratings, and shared interests to build a model. The best model used only shared interests rated by the male and female as predictors. A threshold of 48% probability maximized sensitivity of predicting positive matches at 89%, though overall accuracy was only 67%. While not perfect, the model provides a reasonable way to forecast speed dating success based on participant ratings.
Seasonality effects on second hand cars salesArmando Vieira
This document analyzes seasonality effects on car sales using weekly aggregated car deal data from October 2012 to November 2014. It finds that:
1) A sudden drop in the last week's sales can be explained by statistical fluctuations based on the normal distribution of weekly deals over the period.
2) Months with the lowest deals (November and December) still show that last week's sales of 154 were a normal occurrence based on the mean and standard deviation for those months.
3) Google trends data for the keyword "used cars" shows a clear seasonality pattern of decreasing searches before the end of the year and increasing searches at the start and middle of the year.
Cross selling credit card to existing debit card customersSaurabh Singh
The document describes a process for identifying existing debit card customers who may be good candidates for credit cards using cluster analysis. Transaction and customer data will be analyzed to group customers into clusters. Debit card customers in clusters that also include credit card holders will be identified as potential new credit card customers. Two campaign programs are proposed: offering credit cards when a debit customer makes an unusually large transaction, and incentivizing the remaining identified potential customers.
This study examines factors that affect interest rates in peer-to-peer lending by analyzing data from 2,500 loans through Lending Club. A multiple regression model related interest rate to four factors: FICO score, monthly income, debt-to-income ratio, and length of employment. The analysis found that all factors except length of employment had a statistically significant relationship with interest rate, though the effects were small. FICO score had the strongest negative relationship, while income and debt-to-income ratio had a positive relationship. This suggests credit risk still impacts interest rates in peer-to-peer lending, as do income levels and debt burdens to a lesser degree.
This document discusses the relevance and implications of forecasting retail deposits. Forecasting retail deposits involves analyzing macroeconomic data to build models that can accurately predict future deposit levels given economic conditions. Accurately forecasting deposits is important for banks to inform strategic planning and decisions around operations, technology, and infrastructure needs. The implications of deposit forecasting are discussed from social and philosophical perspectives, including how forecasting stems from humans' innate desire to understand and prepare for an uncertain future.
This document provides guidance on implementing propensity score matching (PSM) for empirical analysis. It begins by explaining that PSM is a non-experimental technique used to reduce selection bias in observational studies. It matches treatment and control group units based on propensity scores that represent the probability of being assigned to treatment. The document then offers practical advice on applying PSM, including how to estimate propensity scores, choose matching algorithms, check matching quality, and conduct sensitivity analysis. It recommends testing multiple matching algorithms and sensitivity of results to assess robustness. Overall, the guidance aims to help researchers properly apply PSM to obtain unbiased treatment effect estimates from non-randomized data.
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E-Commerce sales have demonstrated an amazing growth in the last few years. And it is thus clear that the web is becoming an increasingly important channel and companies should strive for a successful web site. In this completion knowing e-customer and predicting his behavior is very important. In this paper we describe e-customer behavior in B2C relationships and then according to this behavior a new model for evaluating e-customer in B2C e-commerce relationships will be described. The most important thing in our e-CLV (Electronic Customer Lifetime Value) model is considering market\'s risks that are affecting customer cash flow in future. A lot of CLV models are based on simple NPV (simple net present value). However simple NPV can assess a good value for CLV, but simple NPV ignores two important aspects of B2C e-relationship which are market risks and big amount of customer data in e-commerce context. Therefore, simple NPV isn\'t enough for assessing e-CLV in high risk B2C markets. Instead of NPV, real option analyses could lead us to a better estimation for future cash flow of customers. With real option analyses, we predict all the future states with probability of each of them. And then calculate the more accurate of future customer cash flow. In this paper after a brief history of CLV, we explain customer behavior in B2C markets especially for e-retailers. Then with using real option analyses, we introduce our CLV model. Two extended examples explain our model and introduce the steps in finding CLV of customer in a B2C relationship.
Predicting e-Customer behavior in B2C Relationships for CLV model
report
1. Predicting Customer Churn at QWE Inc.
Group 11:
Qiang Gong
Jiaxuan Han
Meghan Hickey
Xiangheng Ma
Yawen Yang
2. ID Days Since Last
Login 0-1
Probability of
Churn
354 -1 4.49%
672 2 4.83%
5203 5 5.19%
Executive Summary
In this paper, we will determine QWE Inc.’s customers’ probability of churn by the end of February 2012. This report will
use a mix of modelling methods to classify customers based on their likelihood to churn. Based on logistic regression anal-
ysis,we found that Customer Happiness Index in December, CHI change from November to December and difference
of Days Since Last Login between December and November are three most important factors QWE can use to pre-
dict a customer’s probability of terminating the contract.We will explain the application of this method and then look
at how a decision tree approach further validates the findings from logistic regression. In order to fully demonstrate our
findings, we will use customers 354, 672 and 5203 as illustrations throughout the paper and discuss the specific applica-
tions of the different modeling techniques to each of these three cases. We will also provide a list of 10 potential customers
who have the highest churn probability so that the company can adopt a more proactive way to retain them.
Analysis:
Logistic Regression Analysis
Single Variable Approach
For QWE, Inc., the purpose is to predict if a customer will
terminate their contract based on certain factors and assess
the importance of factors, so that QWE has an accurate
model for future reference. The first step of our analysis was
to run logistic regression as the method is helpful for
analyzing a dataset that has one or more variables whose
outcome is dichotomous.
To figure out the best predictor, we first aim to determine
which variables are significant in relation to the result.
We evaluated the variables by looking at their p-value for
significance and their standardized coefficient values and
were able to filter out the following variables that aren’t
significant at all - Age (how long they have been a custom-
er with QWE), Support Case 0-1 (the difference between
service requests from Nov. to Dec. ), SP 0-1(the difference
between seriousness of cases reported from Nov. to Dec.),
blog articles 0-1 (the difference between blog articles posted
from Nov. to Dec.), views 0-1 (the difference between views
from Nov. to Dec.)
To illustrate this finding, we calculated the probability of
churn of customers 354, 672 and 5203 based on the change
of Days Since Last Login between Nov and Dec. Their
probabilities of churn are listed below.
We can see that the probability of churn has a positive
correlation with the difference of Days Since Last Login
between Dec and Nov. The smaller the difference of Days
Since Last Login between Dec and Nov, that is, the more
active a customer becomes, the lower probability a customer
would churn. We also noticed that even though a customer
becomes more active, namely, the difference of Days Since
Last Login between Nov and Dec decreases, the probability
of churn doesn’t show obvious decrease. All the probabili-
ties are pretty low.
Then we compared the absolute value of the coefficient of
standardized data of the remaining variables. The higher the
absolute value is, the bigger impact the variable has on
predicting churn probability. We found that “Days Since
Last Login” has the biggest absolute value. Therefore, we
conclude that “Days Since Last Login 0-1” is the most
impactful predictor. It makes sense intuitively because this
variable indicates the change in recency between Nov and
Dec, which tells whether a customer becomes more active
or not.
• USE these predictions of probability to help QWE improve
their business
• FIND out the best predictor of prediction of probability of
churn
• ENABLE QWE to use the wealth of data they possess to
identify customers who are most likely to leave
3. Then we used Receiver Operating Characteristic (ROC)
curve to evaluate the performance of the single classifier.
(Please refer to the term explanation below). We start with
the variable Days Since Last Login 0-1 to see if the logistic
regression model with the single variable is accurate enough.
After feeding the model customer behavior data, we can
generate probabilities of churn for all customers in the
database. Then we ran ROC analysis in SPSS and came up
with the following graph:
Therefore, if the entire ROC curve is even below the
benchmark, the model doesn’t perform well because
it is even worse than random guessing. In the case of
QWE, one part of the curve does go under the benchmark
curve. Moreover, the AUC of 0.589 is only slightly bigger
than 0.5, which is the AUC of diagonal. Combining these
two facts, we can conclude that the predict model using
Days Since Last Login is not sensible enough and does not
predict outcomes very accurately.
In fact, when performing the ROC analysis for the
remaining variables, we find that all AUC are similarly low
(slightly higher than 0.5). Hence using a single variable to
predict the customer churn probability may not give us the
best result. We think it’s necessary to devise a better model
that looks at multiple factors at once so QWE can more
accurately track the behavior of their customers and
understand what it means.
Three Factors Approach
Based on previous logistic regression analysis, we selected
the six variables with the highest significance, CHI Month
0, CHI 0-1, Support Cases Month 0, Support Priority 0-1,
logins 0-1 and Days Since Last Login 0-1, to be included in
logistic regression model. However, we found that three of
them actually have no significant impact on the predicted
probability and decided to only analyze the three that
actually did have high significance. With this method, we
determined Customer Happiness Index in December,
CHI change from November to December and
difference of Days Since Last Login between December
and November to be the three best factors for our
prediction modelling because they contribute the most
to the churn probability.
To illustrate, we used this updated model with three key
variables instead of just one to calculate the churn
probability for customers 354, 672 and 5203. Their
probabilities of churn are 4.73%, 3.59%, and 4.46%
respectively, which are pretty low.
The y-axis is TPR and the x-axis is FPR. (SPSS names
them as sensitivity and 1- Specificity respectively, but
they are essentially the same). The diagonal line
represents a benchmark ROC curve using simple guess
method (i.e.flip a coin) to predict positive or negative
outcome.
Term: ROC
ROC is the most commonly
used method to measure whether
your classification is effective and has
many important advantages. First, it gives you
the true positive rate (TPR) and false positive rate
(FPR) by considering all possible cut off points rather
than looking at just one specific cut off point.
Second, the area under curve (AUC) is a
useful metric that represents the overall
accuracy of the model.
Term: TPR and FPR
TPR denotes the percentage
of customers who finally churn
as we predicted out of customers who
actually churn, while FPR denotes the
percentage of customers who didn’t churn but
we predicted they will out of the amount
of customers who didn’t churn in
reality.
4. ID 354 672 5203
CHI Month 0 139 148 37
CHI 0-1 -29 1 32
Days Since
Last Login 0-1
-1 2 5
Probability of
Churn
4.73% 3.59% 4.46%
Based on churn probabilities resulting from the updated
logistic regression model, we created a list of the top ten
customers who have the highest likelihood of leaving the
company. The table below lists the IDs and corresponding
churn probabilities for these ten risky customers. Note that in
our dataset, the probability ranges from 0% to 22.4%. To put
it another way, even though the absolute value of probability
is not as big as 90% or 100%, it is big enough to show the risk
of churn when compared internally. Being able to identify
these risky customers is a huge opportunity for QWE because
it will allow them to understand the specific forces that lead
to churn. With that information, they can more reasonably
attempt to cut this problem off at the head by knowing that a
customer is probably going to terminate their contract before
that customer has even decided it themselves.
The upgraded logistic regression model also showed
vast improvement on ROC curve. The AUC of 0.634 is
higher than that of any single variable, and every part
of the curve is over the diagonal. We find this method
to be much more suitable and appropriate for QWE’s
prediction needs. It’s not one factor, but a combination
of factors that lead to churn and the model that predicts
it must reflect that nuance.
10 Risky Customers
1971
2076
1287
3671
1929
4245
1236
1616
2546
22%
21%
19%
18%
17%
16%
16%
16%
16%
16%
Possibility of Churn
109
5. Precision 19.25%
Accuracy 87.96%
TPR 42.72%
FPR 9.61%
Decision Tree Analysis
In order to give QWE the most thorough recommendations possible, we further modeled the data through a second
approach. Decision trees are a visually representative way for us to predict if a customer will churn or not by
generating a clear, specific path of rules that can easily be understood. The following image is the decision tree we
got from R using the QWE case data. After manually going through this decision process for customer 354, 672,
and 5203 one by one, we predict that these three customer won’t leave.
This method is useful for showing us which variables are key influencers of churn and partitioning of meaningful
patterns of breaking points. The higher the position of a variable (the node) in the tree, the more importance of the
variable. Both the decision tree and Logistic regression pick Days Since Last Login as the best predictor. Furthermore,
we evaluated the performance of decision tree method using different accuracy metrics. Although the TPR is about
average performance, the Accuracy (88%) is much higher than that of logistic regression. However, it’s the nature of
decision tree because it has the tendency to maximumly fit the training dataset so that the accuracy would even reach
to 100%. That is to say, if given new customers, the model may do bad job in prediction. Moreover, decision tree is
extremely sensible to small changes in dataset: the structure of the tree would change correspondingly. In reality, it is
likely to happen because some customers may edit their profile and change some information. In contrast to above
two downsides of decision tree approach, logistic regression can be tailored to particular business circumstances. In
this case, different cutoff point can be set depending on how the manager weight the cost of losing a customer against
the cost of retaining a customer. In conclusion, we recommend QWE.Inc to adopt logistic regression approach.
6. Factors Change in
this factor
How possibility
of churn will be
affacted
CHI Month 0
CHI 0-1
Days Since Last
Login 0-1
Recommendation:
Based on our analysis, we recommend QWE to con-
sider Customer Happiness Index in December, CHI
change from November to December and difference of
Days Since Last Login as three most important drivers
of prediction of churn. Focusing on these variables will
allow QWE to focus on customers who they are in the
highest danger of churn and identify points at which
their business might fail and these customers might
leave. This knowledge can be applied to strategy in all
areas of the business: marketing, product management,
etc. The models we created will help QWE tighten up
their business and better understand their customers and
their behavior. Specific examples of strategy include the
creation of a customer service outreach program where
QWE targets these bottom ten customers and sends
service representatives to engage with them and offer
them incentives to stay with the company.
Through logistical regression, we found a specific
association between these three factors and possibility of
churn:
Using the knowledge about these three priority variables, we
have devised the following recommendations for QWE in
terms of business operation:
Enhance user experience to increase Customer
Happiness Index. To achieve this goal, QWE can take appli-
cations like making user interface more friendly and acceler-
ating loading speed.
Increase user cohesiveness and interaction to
improve customer login recency. It’s critical to maintain
our users’ level of activity on our platform. There is a clear
relationship from being more active of the site in terms of
both content creation and simply volume of activity. For
example, QWE can use better calls to action in order to
incentivize traffic. Other than that, if they can make their
service more mobile-friendly, it will help increase using
frequency as well.