The customer churn dataset is a collection of customer data that focuses on predicting customer churn, which refers to the tendency of customers to stop using a company's products or services. The dataset contains various features that describe each customer, such as their credit score, country, gender, age, tenure, balance, number of products, credit card status, active membership, estimated salary, and churn status. The churn status indicates whether a customer has churned or not. The dataset is used to analyze and understand factors that contribute to customer churn and to build predictive models to identify customers at risk of churning. The goal is to develop strategies and interventions to reduce churn and improve customer retention

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The Churn Prediction Software market is experiencing robust growth, driven by the increasing need for businesses across diverse sectors to proactively manage customer retention. The market's expansion is fueled by the rising adoption of cloud-based solutions, offering scalability and cost-effectiveness. Key applications include telecommunications, banking and finance, retail, e-commerce, and healthcare, where minimizing customer churn is crucial for profitability. The market is witnessing a shift towards sophisticated predictive analytics and machine learning algorithms that provide more accurate churn predictions, allowing businesses to implement targeted retention strategies. This includes personalized offers, proactive customer support, and improved product/service offerings. Furthermore, the integration of churn prediction software with CRM systems enhances data analysis and facilitates more effective customer relationship management. Competition is intensifying with established players like SAP, Salesforce, and Oracle competing alongside agile startups offering specialized solutions. The market's growth, while positive, also faces certain restraints, such as the high initial investment costs for implementing these sophisticated solutions and the need for skilled data scientists to interpret and leverage the insights derived from the analyses. Despite these challenges, the market's future remains promising. The increasing availability of large datasets, coupled with advancements in artificial intelligence and machine learning, is expected to drive innovation and further enhance the accuracy and effectiveness of churn prediction software. Regional growth will vary, with North America and Europe likely leading the market initially, driven by higher technology adoption rates and established business practices. However, growth in Asia-Pacific is anticipated to accelerate significantly in the coming years as businesses in developing economies prioritize customer retention strategies. The continued development of user-friendly interfaces and the increasing integration of these tools into existing business workflows will further contribute to the overall market expansion and wider adoption across various industries.

Customer Churn Modelling

This dataset provides comprehensive information about a bank's customers, focusing on their demographic, financial, and account activity details. It is designed to help analyze factors influencing customer churn and develop predictive models for customer retention strategies.

Dataset Highlights:

• Customer Demographics: Information such as Gender, Age, and Geographic Location (e.g., country) helps identify trends and patterns in churn across different customer segments.
• Financial Data:
  • Credit Score: A measure of creditworthiness.
  • Balance: Account balance details for each customer.
  • Estimated Salary: Insights into customers' earning potential.
• Account Features:
  • Number of Products: Count of products the customer is subscribed to (e.g., savings accounts, loans).
  • IsActiveMember: Indicates if the customer is actively using the bank’s services.
  • HasCrCard: Identifies customers with a credit card.
• Churn Label: A binary indicator specifying whether the customer exited (1) or stayed (0).
• Tenure: Duration (in years) the customer has been associated with the bank.

Unique Features:

• The dataset is highly structured, making it ideal for cluster tasks.
• Balanced mix of numerical and categorical features, enabling both exploratory data analysis (EDA) and advanced machine learning models.
• Offers insights into customer behavior and retention strategies.

Suggested Use Cases:

• Customer Retention Analysis: Explore demographic and financial factors influencing churn rates.
• Predictive Modeling: Build machine learning models to predict churn and identify at-risk customers.
• Business Insights: Develop strategies for targeted marketing or improving customer loyalty.
• Feature Engineering: Generate new features to enhance prediction accuracy (e.g., balance-to-salary ratio).

This dataset is perfect for beginners and professionals alike to explore customer churn prediction, develop insights, and create impactful business solutions.

The Customer Churn Analysis Software market is experiencing robust growth, driven by the increasing need for businesses to understand and mitigate customer attrition. The market's expansion is fueled by several factors, including the rising adoption of cloud-based solutions, the proliferation of big data analytics, and the growing demand for predictive analytics capabilities to proactively identify at-risk customers. Businesses across diverse sectors, including SaaS, e-commerce, and telecommunications, are increasingly leveraging these sophisticated tools to gain actionable insights into customer behavior, personalize their offerings, and improve customer retention strategies. This market is characterized by a competitive landscape with both established players like Adobe and Google, and specialized niche providers such as Infer and Churnly Technologies Limited. The integration of AI and machine learning capabilities within these platforms is a prominent trend, enabling more accurate prediction models and automated interventions to reduce churn. While the initial investment in such software can be a restraint for some smaller businesses, the long-term return on investment, in terms of improved customer retention and reduced acquisition costs, is a compelling driver for market growth. The forecast period (2025-2033) is expected to witness significant expansion, building upon the historical growth from 2019-2024. Assuming a conservative CAGR (let's estimate it at 15% based on industry trends), and a 2025 market size of $5 billion (a reasonable estimate given the presence of major players and the importance of the sector), the market is projected to reach approximately $17 billion by 2033. This expansion will be propelled by continuous technological advancements, the growing adoption of subscription-based business models, and a heightened focus on customer experience management across industries. Regional variations will likely exist, with North America and Europe leading the market initially due to higher adoption rates and technological infrastructure, but emerging markets in Asia-Pacific are expected to show significant growth in the later years of the forecast period. The competitive landscape will remain dynamic, with mergers, acquisitions, and the emergence of innovative solutions shaping the future of customer churn analysis software.

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

This dataset contains synthetic data simulating customer behavior for a Netflix-like video streaming service. It includes 5,000 records with 14 carefully engineered features designed for churn prediction modeling, business insights, and customer segmentation.

The dataset is ideal for:

Machine learning classification tasks (churn vs. non-churn)

Exploratory data analysis (EDA)

Customer behavior modeling in OTT platforms

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 ---

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.

Analysis of ‘Customer Churn Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sercanyesiloz/customer-churn-dataset on 30 September 2021.

--- No further description of dataset provided by original source ---

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

If you found the dataset useful, your upvote will help others discover it. Thanks for your support!

This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

The Customer Churn Software market is experiencing robust growth, driven by the increasing need for businesses to retain customers and improve profitability. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the increasing availability of sophisticated analytics and AI-powered prediction models enabling proactive churn management, and the growing focus on delivering personalized customer experiences to enhance loyalty. Major players like IBM, Adobe, Salesforce, and Microsoft are actively shaping the market through continuous innovation and strategic acquisitions, contributing to a competitive landscape that fosters further growth. However, the market also faces certain restraints. The high initial investment costs associated with implementing sophisticated churn prediction software can be a barrier for smaller businesses. Furthermore, the complexity of integrating these solutions with existing CRM and data management systems can pose challenges, requiring significant expertise and resources. Despite these challenges, the long-term benefits of reduced customer churn significantly outweigh the initial investment, driving market expansion. The segmentation within the market is diverse, encompassing solutions catering to specific industry verticals and customer sizes, allowing for targeted solutions addressing unique churn drivers within each sector. The increasing prevalence of subscription-based business models further fuels the demand for effective churn management tools.

Customer Churn Analysis Software Market size was valued at USD 1.9 Billion in 2024 and is projected to reach USD 8.4 Billion by 2032, growing at a CAGR of 19.80% during the forecast period 2026-2032.Global Customer Churn Analysis Software Market DriversThe market drivers for the Customer Churn Analysis Software Market can be influenced by various factors. These may include:Customer Retention Methods: As obtaining new consumers is becoming more expensive, greater emphasis is placed on retaining existing ones. Churn analysis software is used to forecast and reduce turnover, resulting in increased customer lifetime value.An Increase in the Usage of Predictive Analytics and AI Technologies: To examine big data sets, churn prediction technologies now incorporate artificial intelligence and machine learning. Their application is allowing for more accurate churn forecasting and targeted actions.

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 ---

AI-Powered Customer Churn Prediction Market Outlook

According to our latest research, the AI-powered customer churn prediction market size reached USD 1.58 billion globally in 2024, with a robust CAGR of 19.7% expected from 2025 to 2033. Driven by rapid digital transformation and the increasing need for predictive analytics across sectors, the market is forecasted to attain a value of USD 7.57 billion by 2033. The growth of this market is primarily attributed to the escalating adoption of AI and machine learning technologies by enterprises seeking to reduce customer attrition, optimize retention strategies, and enhance overall customer lifetime value, as per the latest industry research.

One of the fundamental growth drivers for the AI-powered customer churn prediction market is the proliferation of customer data and the imperative need for businesses to leverage this data to drive actionable insights. With the advent of digital touchpoints, organizations are now able to collect vast amounts of structured and unstructured data from various customer interactions. This data, when processed using advanced AI and machine learning algorithms, empowers companies to predict potential churn with high accuracy. As a result, businesses across industries such as telecommunications, BFSI, retail, and healthcare are increasingly investing in AI-powered churn prediction solutions to proactively identify at-risk customers and implement targeted retention strategies, thereby reducing revenue loss and improving profitability.

Another significant factor fueling market expansion is the growing emphasis on customer experience and personalization. In today's hyper-competitive landscape, retaining existing customers has become more cost-effective than acquiring new ones. AI-powered churn prediction tools enable organizations to segment their customer base, understand behavior patterns, and tailor interventions for individual customers. This level of personalization not only helps in reducing churn rates but also enhances customer satisfaction and loyalty. The integration of AI-driven insights into CRM systems and marketing automation platforms further streamlines the process, making it easier for businesses to act on predictions in real time. Moreover, the rising adoption of cloud-based solutions has made these technologies more accessible to small and medium enterprises (SMEs), broadening the market’s reach.

The surge in demand for scalable, real-time analytics platforms is also contributing to market growth. Enterprises are increasingly seeking AI-powered solutions that can integrate seamlessly with their existing IT infrastructure, deliver instant insights, and scale as their data grows. The shift towards cloud deployment models has accelerated this trend, offering cost-effective, flexible, and easily deployable churn prediction solutions. Additionally, advancements in natural language processing (NLP), deep learning, and big data analytics are further enhancing the accuracy and reliability of churn prediction models. As organizations strive to stay ahead of the competition by minimizing customer attrition, the demand for sophisticated, AI-driven predictive analytics tools continues to rise.

Regionally, North America holds the largest market share, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of AI technologies, presence of major technology vendors, and a strong focus on customer-centric strategies among enterprises in the region. Europe is also witnessing significant growth, driven by stringent regulations around data protection and a growing emphasis on customer retention in industries like BFSI and retail. The Asia Pacific region is expected to exhibit the highest CAGR during the forecast period, fueled by rapid digitalization, increasing investments in AI, and the expansion of e-commerce and telecommunications sectors. Latin America and the Middle East & Africa are also experiencing gradual adoption, primarily in financial services and telecommunications.

Component Analysis

The component segment of the AI-powered customer churn prediction market is categorized into software and services. The software segment dominates the market, accounting for the largest share in 2024, owing to the widespread deployment of advanced AI and machine learning platforms

"It's necessary to implement an effective customer retention strategy through data analysis. The main goal is to predict the probability of customer churn for the next month, identify key customer profiles, and develop specific recommendations to improve customer retention and satisfaction. This will enable optimizing the customer experience and strengthening their loyalty."

This dataset is for ABC Multistate bank with following columns:

1. customer_id, unused variable.
2. credit_score, used as input.
3. country, used as input.
4. gender, used as input.
5. age, used as input.
6. tenure, used as input.
7. balance, used as input.
8. products_number, used as input.
9. credit_card, used as input.
10. active_member, used as input.
11. estimated_salary, used as input.
12. churn, used as the target. 1 if the client has left the bank during some period or 0 if he/she has not.

Aim is to Predict the Customer Churn for ABC Bank.

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

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

Context

"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Inspiration

To explore this type of models and learn more about the subject.

New version from IBM: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

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