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Context : This dataset is part of a data science project focused on customer churn prediction for a subscription-based service. Customer churn, the rate at which customers cancel their subscriptions, is a vital metric for businesses offering subscription services. Predictive analytics techniques are employed to anticipate which customers are likely to churn, enabling companies to take proactive measures for customer retention.

Content : This dataset contains anonymized information about customer subscriptions and their interaction with the service. The data includes various features such as subscription type, payment method, viewing preferences, customer support interactions, and other relevant attributes. It consists of three files such as "test.csv", "train.csv", "data_descriptions.csv".

Columns :

CustomerID: Unique identifier for each customer

SubscriptionType: Type of subscription plan chosen by the customer (e.g., Basic, Premium, Deluxe)

PaymentMethod: Method used for payment (e.g., Credit Card, Electronic Check, PayPal)

PaperlessBilling: Whether the customer uses paperless billing (Yes/No)

ContentType: Type of content accessed by the customer (e.g., Movies, TV Shows, Documentaries)

MultiDeviceAccess: Whether the customer has access on multiple devices (Yes/No)

DeviceRegistered: Device registered by the customer (e.g., Smartphone, Smart TV, Laptop)

GenrePreference: Genre preference of the customer (e.g., Action, Drama, Comedy)

Gender: Gender of the customer (Male/Female)

ParentalControl: Whether parental control is enabled (Yes/No)

SubtitlesEnabled: Whether subtitles are enabled (Yes/No)

AccountAge: Age of the customer's subscription account (in months)

MonthlyCharges: Monthly subscription charges

TotalCharges: Total charges incurred by the customer

ViewingHoursPerWeek: Average number of viewing hours per week

SupportTicketsPerMonth: Number of customer support tickets raised per month

AverageViewingDuration: Average duration of each viewing session

ContentDownloadsPerMonth: Number of content downloads per month

UserRating: Customer satisfaction rating (1 to 5)

WatchlistSize: Size of the customer's content watchlist

Acknowledgments : The dataset used in this project is obtained from Data Science Challenge on Coursera and is used for educational and research purposes. Any resemblance to real persons or entities is purely coincidental.

1) Data Introduction • The Customer Churn Dataset is a dataset that collects various customer characteristics and service usage information to predict whether or not communication service customers will turn.

2) Data Utilization (1) Customer Churn Dataset has characteristics that: • The dataset consists of several categorical and numerical variables, including customer demographics, service types, contract information, charges, usage patterns, and Turn. (2) Customer Churn Dataset can be used to: • Development of customer churn prediction model : Machine learning and deep learning techniques can be used to develop classification models that predict churn based on customer characteristics and service use data. • Segmenting customers and developing marketing strategies : It can be used to analyze customer groups at high risk of departure and to design custom retention strategies or targeted marketing campaigns.

Churn prediction aims to detect customers intended to leave a service provider. Retaining one customer costs an organization from 5 to 10 times than gaining a new one. Predictive models can provide correct identification of possible churners in the near future in order to provide a retention solution. This paper presents a new prediction model based on Data Mining (DM) techniques. The proposed model is composed of six steps which are; identify problem domain, data selection, investigate data set, classification, clustering and knowledge usage. A data set with 23 attributes and 5000 instances is used. 4000 instances used for training the model and 1000 instances used as a testing set. The predicted churners are clustered into 3 categories in case of using in a retention strategy. The data mining techniques used in this paper are Decision Tree, Support Vector Machine and Neural Network throughout an open source software name WEKA.

Dive into Market Research Intellect's Customer Churn Analysis Software Market Report, valued at USD 2.1 billion in 2024, and forecast to reach USD 4.8 billion by 2033, growing at a CAGR of 10.2% from 2026 to 2033.

The Customer Churn Classification dataset is a vital resource for businesses seeking to understand and predict customer churn, a critical metric that represents the rate at which customers stop doing business with a company over a given period. Understanding churn is essential for any customer-focused company, as retaining customers is generally more cost-effective than acquiring new ones. The dataset is designed to provide a detailed view of customer characteristics and behaviors that could potentially lead to churn, allowing companies to take preemptive action to improve customer retention.

Breakdown of Dataset Features This dataset includes several features, each contributing valuable information for analyzing customer behaviors and identifying potential churn risks:

Customer ID: A unique identifier for each customer. This column is useful for keeping track of individual customers without revealing personal details like names or contact information. It is essential for organizing data and ensuring that individual records can be tracked over time.

Surname: This column contains the surname of the customer. While it might not directly influence churn, it could be used in personalized marketing strategies. For example, companies could address customers by their last names in emails or other forms of communication to foster a sense of personal connection.

Credit Score: A key financial indicator, the credit score reflects a customer's creditworthiness and financial health. A low credit score might indicate a higher likelihood of churn, as these customers may be more prone to financial difficulties or more likely to switch to competitors offering better financial terms.

Geography: The geographical location of customers. This feature helps businesses understand regional patterns in customer behavior, such as churn rates varying between different countries or cities. Geographic data might reveal that certain areas have more competitive markets, which could lead to higher churn.

Gender: This feature identifies the gender of customers, which can be useful in understanding churn trends across different demographics. Some studies suggest that churn rates can differ between men and women due to varying expectations, needs, and preferences in service.

Age: Age plays a significant role in customer churn, as different age groups tend to have distinct purchasing habits and loyalty tendencies. Younger customers might be more open to exploring competitor options, while older customers might exhibit more loyalty but could churn if they feel underappreciated.

Tenure: This feature reflects how long a customer has been with the company. Longer tenure typically correlates with greater loyalty, as these customers have built a more robust relationship with the company. However, if long-tenured customers churn, it could signal deeper issues with service quality or product offerings.

Balance: The account balance of customers, which provides insight into their financial involvement with the company. Customers with higher balances may be less likely to churn, as they are more financially invested in the company, while customers with lower balances may have less at stake and are more likely to switch to competitors.

Number of Products Held: The number of products or services the customer is subscribed to. Generally, customers who use multiple products are more likely to remain loyal, as switching would involve more effort and a higher cost in terms of time and disruption to their routine.

Credit Card Status: This feature identifies whether the customer has a credit card issued by the company. Customers who own a credit card might have a stronger financial relationship with the company and, as a result, could exhibit lower churn rates. However, if customers are dissatisfied with their credit card, it might lead to a higher chance of churn.

Active Membership Status: Indicates whether the customer is actively using their membership or account. Customers with active accounts are usually more engaged with the company's products or services and are less likely to churn. In contrast, customers with inactive memberships might be at risk of churn due to disinterest or dissatisfaction.

Estimated Salary: A customer's estimated salary provides an indication of their financial well-being. Higher-income customers may have different expectations of service quality and could churn if they feel that the company isn't meeting their standards. Conversely, lower-income customers might be more sensitive to pricing and more prone to switch for better deals.

Exited: This is the target column, which indicates whether the customer has churned (1 for churned and 0 for not churned). This is the dependent variable that is predicted based on the other features, and it forms the basis of churn prediction models.

Importance of Churn Prediction The Custo...

1) Data Introduction • The Telco Customer Churn Dataset includes carrier customer service usage, account information, demographics and churn, which can be used to predict and analyze customer churn.

2) Data Utilization (1) Telco Customer Churn Dataset has characteristics that: • This dataset includes a variety of customer and service characteristics, including gender, age group, partner and dependents, service subscription status (telephone, Internet, security, backup, device protection, technical support, streaming, etc.), contract type, payment method, monthly fee, total fee, and departure. (2) Telco Customer Churn Dataset can be used to: • Development of customer churn prediction model: Using customer service usage patterns and account information, we can build a machine learning-based churn prediction model to proactively identify customers at risk of churn.

This dataset has been designed for the Employee Churn Prediction problem, simulating real-world organizational data. It contains 10,000 records of employee information, including demographic details, job-related factors, performance metrics, and historical churn indicators. The dataset is imbalanced, reflecting realistic churn scenarios where a smaller proportion of employees leave the organization.

Key Features Demographic Details: Includes age, gender, education level, marital status, and distance from home. Job-Related Factors: Covers job role, department, work location, salary, and tenure with the organization. Performance Metrics: Includes performance ratings, projects completed, training hours, promotions, and overtime hours. Historical Churn Indicators: Captures satisfaction level, work-life balance, absenteeism, and average monthly hours worked. Additional Features: Manager feedback scores and a breakdown of promotions and work-life-related factors. Target Variable (Churn): A binary variable indicating whether the employee has left the organization (1 = churned, 0 = retained). Target Variable Distribution Retained Employees (0): ~80% of the dataset. Churned Employees (1): ~20% of the dataset.

The provided data asset is relational and consists of four distinct data files.

1. address.csv: contains address information

2. customer.csv: contains customer information.

3. demographic.csv: contains demographic data

4. termination.csv: includes customer termination information.

5. autoinsurance_churn.csv: includes merged customer churn data generated from this notebook.

All data sets are linked using either ADDRESS_ID or INDIVIDUAL_ID. The ADDRESS_ID pertains to a specific postal service address, while the INDIVIDUAL_ID is unique to each individual. It is important to note that multiple customers may be assigned to the same address, and not all customers have demographic information available.

Size of the data set

The data set includes 1,536,673 unique addresses and 2,280,321 unique customers, of which 2,112,579 have demographic information. Additionally, 269,259 customers cancelled their policies within the previous year.

Note

Please note that the data is synthetic, and all customer information provided is fictitious. While the latitude-longitude information can be mapped at a high level and generally refers to the Dallas-Fort Worth Metroplex in North Texas, it is important to note that drilling down too far may result in some data points that are located in the middle of Jerry World, DFW Airport, or Lake Grapevine. The physical addresses provided are fake and are unrelated to the corresponding lat/long.

The termination table includes the ACCT_SUSPD_DATE field, which can be used to derive a binary churn/did not churn variable. The data set is modelable, meaning that the other data available can be used to predict which customers churned and which did not. The underlying logic used to make these predictions should align with predicting auto insurance churn in the real world.

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Analysis of ‘JB Link Telco Customer Churn’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnflag/jb-link-telco-customer-churn on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This is a customized version of the widely known IBM Telco Customer Churn dataset. I've added a few more columns and modified others in order to make it a little more realistic.

My customizations are based on the following version: Telco customer churn (11.1.3+)

Below you may find a fictional business problem I created. You may use it in order to start developing something around this dataset.

JB Link Customer Churn Problem

JB Link is a small size telecom company located in the state of California that provides Phone and Internet services to customers on more than a 1,000 cities and 1,600 zip codes.

The company is in the market for just 6 years and has quickly grown by investing on infrastructure to bring internet and phone networks to regions that had poor or no coverage.

The company also has a very skilled sales team that is always performing well on attracting new customers. The number of new customers acquired in the past quarter represent 15% over the total.

However, by the end of this same period, only 43% of this customers stayed with the company and most of them decided on not renewing their contracts after a few months, meaning the customer churn rate is very high and the company is now facing a big challenge on retaining its customers.

The total customer churn rate last quarter was around 27%, resulting in a decrease of almost 12% in the total number of customers.

The executive leadership of JB Link is aware that some competitors are investing on new technologies and on the expansion of their network coverage and they believe this is one of the main drivers of the high customer churn rate.

Therefore, as an action plan, they have decided to created a task force inside the company that will be responsible to work on a customer retention strategy.

The task force will involve members from different areas of the company, including Sales, Finance, Marketing, Customer Service, Tech Support and a recent formed Data Science team.

The data science team will play a key role on this process and was assigned some very important tasks that will support on the decisions and actions the other teams will be taking : - Gather insights from the data to understand what is driving the high customer churn rate. - Develop a Machine Learning model that can accurately predict the customers that are more likely to churn. - Prescribe customized actions that could be taken in order to retain each of those customers.

The Data Science team was given a dataset with a random sample of 7,043 customers that can help on achieving this task.

The executives are aware that the cost of acquiring a new customer can be up to five times higher than the cost of retaining a customer, so they are expecting that the results of this project will save a lot of money to the company and make it start growing again.

--- Original source retains full ownership of the source dataset ---

Dataset Card for Telco Customer Churn

This dataset contains information about customers of a fictional telecommunications company, including demographic information, services subscribed to, location details, and churn behavior. This merged dataset combines the information from the original Telco Customer Churn dataset with additional details.

  Dataset Details





  Dataset Description

This merged Telco Customer Churn dataset provides a comprehensive view of customer… See the full description on the dataset page: https://huggingface.co/datasets/aai510-group1/telco-customer-churn.

Context and Methodology

The dataset originates from the research domain of Customer Churn Prediction in the Telecom Industry. It was created as part of the project "Data-Driven Churn Prediction: ML Solutions for the Telecom Industry," completed within the Data Stewardship course (Master programme Data Science, TU Wien).

The primary purpose of this dataset is to support machine learning model development for predicting customer churn based on customer demographics, service usage, and account information.
The dataset enables the training, testing, and evaluation of classification algorithms, allowing researchers and practitioners to explore techniques for customer retention optimization.

The dataset was originally obtained from the IBM Accelerator Catalog and adapted for academic use. It was uploaded to TU Wien’s DBRepo test system and accessed via SQLAlchemy connections to the MariaDB environment.

Technical Details

The dataset has a tabular structure and was initially stored in CSV format. It contains:

• Rows: 7,043 customer records

• Columns: 21 features including customer attributes (gender, senior citizen status, partner status), account information (tenure, contract type, payment method), service usage (internet service, streaming TV, tech support), and the target variable (Churn: Yes/No).

Naming Convention:

• The table in the database is named telco_customer_churn_data.

Software Requirements:

• To open and work with the dataset, any standard database client or programming language supporting MariaDB connections can be used (e.g., Python etc).

• For machine learning applications, libraries such as pandas, scikit-learn, and joblib are typically used.

Additional Resources:

• Source code for data loading, preprocessing, model training, and evaluation is available at the associated GitHub repository: https://github.com/nazerum/fair-ml-customer-churn

Further Details

When reusing the dataset, users should be aware:

• Licensing: The dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

• Use Case Suitability: The dataset is best suited for classification tasks, particularly binary classification (churn vs. no churn).

• Metadata Standards: Metadata describing the dataset adheres to FAIR principles and is supplemented by CodeMeta and Croissant standards for improved interoperability.

Customer Churn Software Market Outlook

The global customer churn software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.8 billion by 2032, growing at a CAGR of 13.7% during the forecast period. This robust growth is driven by several factors, including the increasing importance of customer retention in competitive markets, advancements in AI and machine learning technologies, and the growing adoption of digital transformation initiatives across industries.

One of the primary growth factors propelling the customer churn software market is the increasing emphasis on customer satisfaction and retention. In today's highly competitive business environment, retaining existing customers is more cost-effective than acquiring new ones. Companies are realizing the value of customer loyalty, and as a result, they are investing heavily in tools that can help predict and mitigate churn. Customer churn software offers advanced analytics and predictive capabilities, enabling organizations to identify at-risk customers and take proactive measures to retain them.

Another significant driver is the advancement in artificial intelligence (AI) and machine learning technologies. These technologies have revolutionized the way customer data is analyzed and interpreted. AI-powered customer churn software can process vast amounts of data from multiple sources, identify patterns, and generate actionable insights. This ability to leverage big data and predictive analytics is crucial for businesses aiming to stay ahead of the competition. As AI and machine learning continue to evolve, the effectiveness and efficiency of customer churn software are expected to improve further.

The increasing adoption of digital transformation initiatives across various industries is also contributing to the market growth. As businesses undergo digital transformation, they generate enormous amounts of data related to customer behavior, preferences, and interactions. Customer churn software helps organizations make sense of this data, enabling them to develop personalized strategies to enhance customer experience and loyalty. The shift towards data-driven decision-making is compelling companies to invest in advanced analytics solutions, thereby driving the demand for customer churn software.

From a regional perspective, North America holds a significant share of the customer churn software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as the rapid digitalization of economies, increasing investments in AI and machine learning, and the growing focus on customer-centric strategies in emerging markets are fueling the demand for customer churn software in this region.

Component Analysis

The customer churn software market is segmented into two primary components: software and services. The software segment includes the actual customer churn solutions, while the services segment encompasses implementation, training, support, and consulting services. The software segment is expected to dominate the market due to the high demand for advanced analytics and predictive tools. Companies across various industries are increasingly adopting software solutions to gain insights into customer behavior and predict churn. The software segment's growth is further supported by continuous advancements in AI and machine learning technologies, which enhance the capabilities of customer churn solutions.

The services segment, although smaller in comparison to the software segment, plays a crucial role in the market. Services such as implementation and training ensure that organizations can effectively deploy and utilize customer churn software. Support and consulting services are equally important, as they help companies optimize their software usage and develop customized strategies to address specific churn-related challenges. The demand for these services is expected to grow in tandem with the adoption of customer churn software, as businesses seek to maximize their return on investment and achieve better customer retention outcomes.

Moreover, the integration of customer churn software with existing CRM systems and other business applications is becoming increasingly important. This integration enables a seamless flow of data and enhances the overall efficiency of customer retention efforts. As a result, solutions that offer robust integration capa

Analysis of ‘Customer Churn’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hassanamin/customer-churn on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Binary Customer Churn

A marketing agency has many customers that use their service to produce ads for the client/customer websites. They've noticed that they have quite a bit of churn in clients. They basically randomly assign account managers right now, but want you to create a machine learning model that will help predict which customers will churn (stop buying their service) so that they can correctly assign the customers most at risk to churn an account manager. Luckily they have some historical data, can you help them out? Create a classification algorithm that will help classify whether or not a customer churned. Then the company can test this against incoming data for future customers to predict which customers will churn and assign them an account manager.

Content

The data is saved as customer_churn.csv. Here are the fields and their definitions:

Name : Name of the latest contact at Company

Age: Customer Age

Total_Purchase: Total Ads Purchased

Account_Manager: Binary 0=No manager, 1= Account manager assigned

Years: Totaly Years as a customer

Num_sites: Number of websites that use the service.

Onboard_date: Date that the name of the latest contact was onboarded

Location: Client HQ Address

Company: Name of Client Company

Once you've created the model and evaluated it, test out the model on some new data (you can think of this almost like a hold-out set) that your client has provided, saved under new_customers.csv. The client wants to know which customers are most likely to churn given this data (they don't have the label yet).

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

--- Original source retains full ownership of the source dataset ---

Comparison of the proposed algorithms with other ensemble models.

Although the results were close, the industry in the United States where customers were most likely to leave their current provider due to poor customer service appears to be cable television, with a 25 percent churn rate in 2020.

Churn rate

Churn rate, sometimes also called attrition rate, is the percentage of customers that stop utilizing a service within a time given period. It is often used to measure businesses which have a contractual customer base, especially subscriber-based service models.

mohab-yasser2/telecom-churn-model dataset hosted on Hugging Face and contributed by the HF Datasets community

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

Analysis of ‘Churn Modelling’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shrutimechlearn/churn-modelling on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

This data set contains details of a bank's customers and the target variable is a binary variable reflecting the fact whether the customer left the bank (closed his account) or he continues to be a customer.

Acknowledgements

Big thanks to https://www.superdatascience.com/pages/deep-learning Banner Photo by Sharon McCutcheon on Unsplash

--- Original source retains full ownership of the source dataset ---