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

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.

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

Usage-based churn: Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively.

The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.

Understanding customer behaviour during churn Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows different behaviour than the ‘good’ months. Also, it is crucial to...

This is a sample dataset of Telco Customer Churn. It's inspired by the original dataset of "Telco customer churn (11.1.3+)" from IBM Business Analytics Community. This sample dataset is being cleaned and aggregated from the original dataset. It would be good for telco customer churn analysis or prediction by the classification or regression model for experiment and learning purposes.

Column Description: * customerID: A unique ID that identifies each customer. * gender: The customer’s gender: Male (1), Female (0). * SeniorCitizen: Indicates if the customer is 65 or older: No (0), Yes (1). * Partner: Service contract is resold by the partner: No (0), Yes (1). * Dependents: Indicates if the customer lives with any dependents: No (0), Yes (1). * Tenure: Indicates the total amount of months that the customer has been with the company. * PhoneService: Indicates if the customer subscribes to home phone service with the company: No (0), Yes (1). * MultipleLines: Indicates if the customer subscribes to multiple telephone lines with the company: No (0), Yes (1). * InternetService: Indicates if the customer subscribes to Internet service with the company: No (0), DSL (1), Fiber optic (2). * OnlineSecurity: Indicates if the customer subscribes to an additional online security service provided by the company: No (0), Yes (1), NA (2). * OnlineBackup: Indicates if the customer subscribes to an additional online backup service provided by the company: No (0), Yes (1), NA (2). * DeviceProtection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: No (0), Yes (1), NA (2). * TechSupport: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: No (0), Yes (1), NA (2). * StreamingTV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * StreamingMovies: Indicates if the customer uses their Internet service to stream movies from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * Contract: Indicates the customer’s current contract type: Month-to-Month (0), One Year (1), Two Year (2). * PaperlessBilling: Indicates if the customer has chosen paperless billing: No (0), Yes (1). * PaymentMethod: Indicates how the customer pays their bill: Bank transfer - automatic (0), Credit card - automatic (1), Electronic cheque (2), Mailed cheque (3). * MonthlyCharges: Indicates the customer’s current total monthly charge for all their services from the company. * TotalCharges: Indicates the customer’s total charges. * Churn: Indicates if the customer churn or not: No (0), Yes (1).

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 contains information about customers of a telecommunications company. It includes various demographic, account, and service-related attributes. The dataset is primarily used to analyze customer behavior and predict churn, helping businesses retain customers by understanding the factors that lead to customer attrition. The data is ideal for machine learning projects focused on classification, customer segmentation, and retention strategies.

The Telco customer churn data contains information about a fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3. It indicates which customers have left, stayed, or signed up for their service. Multiple important demographics are included for each customer, as well as a Satisfaction Score, Churn Score, and Customer Lifetime Value (CLTV) index.

This dataset contains customer account information for a telecommunications company. It includes demographic details, services subscribed, billing information, and churn labels.

The dataset is widely used for customer retention modeling and churn prediction. It’s ideal for:

Machine learning classification projects.

Customer segmentation and lifetime value analysis.

Business case studies in predictive analytics.

Inspiration: Predict which customers are most likely to churn.

Understand customer behavior based on contract type, monthly charges, and service usage.

Apply supervised ML algorithms such as Logistic Regression, Decision Trees, Random Forests, and Neural Networks.

📌**Dataset Story** Telco churn data includes information about a fictitious telecom company that provided home phone and Internet services to 7,043 customers in California in the third quarter. It shows which customers left, stayed, or signed up for their service.

🆔**CustomerId:** Customer Id 👫**Gender:** Gender 👵**SeniorCitizen:** Whether the customer is elderly (1, 0) 👫**Partner:** Whether the customer has a partner (Yes, No) 👨‍👨‍👧‍👧**Dependents:** Whether the customer has dependents (Yes, No) 📜**Tenure:** Number of months the customer has been with the company ☎️**PhoneService:** Whether the customer has phone service (Yes, No) 📞**MultipleLines:** Whether the customer has more than one line (Yes, No, No phone service) 💻**InternetService:** Whether the customer has internet service provider (DSL, Fiber optic, No) ㊙️**OnlineSecurity:** Whether the customer has online security (Yes, No, No internet service) ◀️**OnlineBackup:** Whether the customer has online backup (Yes, No, No internet service) 🚫**DeviceProtection:** Whether the customer has device protection (Yes, No, No internet service) 🧢**TechSupport:** Whether the customer has technical support (Yes, No, No internet service) 📺**StreamingTV**: Whether the customer has streaming TV (Yes, No, No Internet service) 📽️**StreamingMovies:** Whether the customer streams movies (Yes, No, No internet service) 🗞️**Contract:** Whether the customer's contract term (Month-to-month, One year, Two years) 📰**PaperlessBilling:** Whether the customer has paperless billing (Yes, No) 💳**PaymentMethod:** Whether the customer's payment method (Electronic check, Postal check, Wire transfer (automatic), Credit card (automatic)) 🤑**MonthlyCharges:** The amount charged to the customer monthly 💰**TotalCharges:** The total amount charged to the customer ❌**Churn:** Whether the customer uses (Yes or No)

Telco Churn 7k

A 7,043-row customer-retention dataset drawn from a U.S. telecom provider. Each record profiles one account with 21 concise attributes and a Churn flag (Yes / No) indicating whether the customer left within the last month. The schema is:

customerID – unique subscriber identifier
gender – {Female, Male}
SeniorCitizen – {0, 1}
Partner, Dependents – {Yes, No}
tenure – months of service (0–72)
PhoneService, MultipleLines – {Yes, No, No phone service}… See the full description on the dataset page: https://huggingface.co/datasets/mnemoraorg/telco-churn-7k.

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.

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.

📝 Dataset Description This dataset contains information about customers of a telecommunications company, including their demographic details, account information, service subscriptions, and churn status. It is a modified version of the popular Telco Churn dataset, curated for exploratory data analysis, machine learning model development, and churn prediction tasks.

The dataset includes simulated missing values in some columns to reflect real-world data issues and support preprocessing and imputation tasks. This makes it especially useful for demonstrating data cleaning techniques and evaluating model robustness.

📂 Files Included telco_data_modified.csv: The main dataset with 21 columns and 7043 rows (some missing values are intentionally inserted).

📌 Features Column Name Description customerID Unique identifier for each customer gender Customer gender: Male/Female SeniorCitizen Indicates if the customer is a senior citizen (0 = No, 1 = Yes) Partner Whether the customer has a partner Dependents Whether the customer has dependents tenure Number of months the customer has stayed with the company PhoneService Whether the customer has phone service MultipleLines Whether the customer has multiple lines InternetService Customer's internet service provider (DSL, Fiber optic, No) OnlineSecurity Whether the customer has online security OnlineBackup Whether the customer has online backup DeviceProtection Whether the customer has device protection TechSupport Whether the customer has tech support StreamingTV Whether the customer has streaming TV StreamingMovies Whether the customer has streaming movies Contract Type of contract: Month-to-month, One year, Two year PaperlessBilling Whether the customer uses paperless billing PaymentMethod Payment method: (e.g., Electronic check, Mailed check, etc.) MonthlyCharges Monthly charges TotalCharges Total charges to date Churn Whether the customer has left the company (Yes/No)

🔍 Use Cases Binary classification: Predict customer churn

Data preprocessing and imputation exercises

Feature engineering and importance analysis

Customer segmentation and churn modeling

⚠️ Notes Missing values were intentionally inserted in the dataset to help simulate real-world conditions.

Some preprocessing may be required before modeling (e.g., converting categorical to numerical data, handling TotalCharges as numeric).

🏷️ Tags

telecom #churn #classification #customer-analytics #data-cleaning #feature-engineering

🙏 Acknowledgements This dataset is based on the original Telco Customer Churn dataset (initially provided by IBM). The current version has been modified for academic and practical exercises.

This record provides code and artifacts to reproduce the manuscript’s results. It includes source code (training/evaluation, post-hoc XAI), calibrated out-of-fold/test scores, cross-validation summaries, trained models, and all figures. No resampling is applied (class imbalance handled via class_weight="balanced"); PR-AUC is the primary selection metric; probabilities are isotonic-calibrated for decision-grade risk tiering. Raw IBM Telco Customer Churn data are not redistributed; obtain from Kaggle (dataset: blastchar/telco-customer-churn, file WA_Fn-UseC_-Telco-Customer-Churn.csv)

This deposit contains code and artifacts for a fully reproducible churn-prediction workflow on IBM’s Telco Customer Churn dataset. The pipeline emphasizes class-imbalance aware model selection (PR-AUC with class weights), probability calibration (isotonic; reported via Brier score and reliability curves), and transparent explainability (global SHAP + local LIME). Calibrated probabilities are mapped to operational risk tiers (Low <0.35, Medium 0.35–0.59, High ≥0.60) to support budgeted retention actions.

Contents: (i) training/evaluation scripts, (ii) a calibrated model artifact and evaluation figures (PR/ROC, gains, calibration), (iii) LIME figures and selection logs under a deterministic Plan-A protocol (Top-risk / Median-of-tier / Near-threshold per tier), (iv) audit reports and a Zenodo-ready README. Random seed is fixed to 42 for repeatability.

Data availability: we do not redistribute the raw IBM/Kaggle CSV. Users should download WA_Fn-UseC_-Telco-Customer-Churn.csv from the original source and place it in the project root as documented in the README.

The Telco Customer Experience Management (CEM) market is booming, projected to reach $2.5 billion by 2025 and grow at a CAGR of 7.7% through 2033. This report analyzes market drivers, trends, and key players, offering insights into regional growth and segmentation across OTT, banking, and retail. Discover the future of customer experience in the telecom sector.

The Telco Customer Churn Dataset contains information on customers of a telecommunications company. The objective of the dataset is to predict whether a customer will churn (leave) based on their attributes and usage behavior. It includes various customer features such as demographic details, account information, services subscribed to, and usage patterns. By leveraging machine learning techniques, this dataset enables the creation of predictive models to identify customers at risk of churn, which can help businesses implement retention strategies.

Key Features: CustomerID: Unique identifier for each customer. Demographics: Customer's age, gender, and other relevant details. Account Information: Tenure, contract type, payment methods, etc. Services: Information on the services each customer subscribes to (e.g., phone, internet, tech support). Usage Patterns: Call duration, data usage, and related usage statistics. Churn: The target variable indicating whether the customer has churned (1) or stayed (0).

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.

Telecom Churn Management Market Outlook

According to our latest research, the global telecom churn management market size reached USD 2.74 billion in 2024, reflecting robust momentum driven by the increasing demand for advanced customer retention strategies in the telecommunications sector. The market is projected to grow at a CAGR of 9.1% from 2025 to 2033, reaching an estimated value of USD 6.11 billion by 2033. This growth trajectory is attributed to the rising adoption of predictive analytics, AI-driven customer engagement tools, and the intensifying competition among telecom operators striving to minimize customer attrition and maximize lifetime value.

One of the primary growth factors propelling the telecom churn management market is the escalating competition in the telecommunications industry, which has led to a significant focus on customer retention. As service providers face saturated markets and price wars, the cost of acquiring new customers has soared, making retention efforts more economically viable. Advanced churn management solutions, powered by machine learning and big data analytics, enable telecom operators to proactively identify at-risk customers and intervene with personalized offers, thereby reducing churn rates. Additionally, the proliferation of mobile devices and the advent of 5G technology have heightened customer expectations for seamless connectivity and superior service quality, compelling telecom companies to invest in sophisticated churn management systems to maintain a competitive edge.

Another substantial growth driver is the integration of artificial intelligence and predictive analytics in churn management platforms. These technologies empower telecom operators to analyze vast volumes of customer data, uncover hidden patterns, and predict churn propensity with remarkable accuracy. By leveraging these insights, operators can design targeted retention campaigns, optimize pricing strategies, and enhance customer engagement, ultimately improving their bottom line. The growing emphasis on customer experience management, coupled with the increasing availability of cloud-based churn management solutions, has further accelerated market adoption, as operators seek scalable and cost-effective tools to address churn challenges across diverse customer segments.

Regulatory pressures and the evolving digital landscape also play a crucial role in shaping the telecom churn management market. Governments and regulatory bodies in various regions have mandated higher standards for customer data protection and transparency, prompting telecom operators to adopt advanced churn management platforms that comply with these requirements. Moreover, the rapid digital transformation across emerging economies has expanded the addressable market for churn management solutions, as telecom operators in these regions strive to differentiate themselves through superior customer service and innovative retention tactics. The convergence of these factors is expected to sustain the market's growth momentum throughout the forecast period.

AI for Churn Prediction in Telecom has emerged as a transformative force in the telecom industry, enabling operators to harness the power of artificial intelligence to anticipate customer behavior and reduce churn rates effectively. By analyzing vast datasets, AI algorithms can identify subtle patterns and trends that might be overlooked by traditional analytics methods. This capability allows telecom companies to proactively address potential issues before they lead to customer dissatisfaction and attrition. Furthermore, AI-driven insights can be used to personalize customer interactions, offering tailored solutions and promotions that resonate with individual preferences. As a result, telecom operators can enhance customer loyalty and retention, ultimately improving their competitive positioning in a crowded market.

From a regional perspective, North America currently dominates the telecom churn management market, owing to the presence of leading telecom operators, a mature technology ecosystem, and high customer churn rates. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by the rapid expansion of mobile and internet services, increasing smartphone penetration, and rising investments in digital infrast

The dataset provided is focused on customer churn within the telecommunications sector, a critical metric for businesses aiming to retain customers and understand turnover. It comprises 7042 entries, each representing an individual customer, with 9 variables that detail aspects of their service and contract.

Key variables include:

customerID: A unique identifier for each customer. tenure: The length of time (in months) a customer has been with the service, indicative of customer loyalty. PhoneService: Indicates whether the customer subscribes to phone service (Yes or No). Contract: The type of contract (Month-to-month, One year, or Two year) the customer has, affecting the likelihood of churn. PaperlessBilling: Whether the customer uses paperless billing (Yes or No), which could influence customer satisfaction and retention. PaymentMethod: The method by which customers pay their bills (e.g., Electronic check, Mailed check, Bank transfer, or Credit card), possibly affecting convenience and churn. MonthlyCharges: The amount charged to the customer each month. TotalCharges: The total amount charged to the customer over the course of their tenure. Churn: Whether the customer has left the service (Yes or No), the primary outcome variable for analyzing customer turnover.