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.

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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.

📝 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.

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 21 November 2021.

--- 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 ---

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.

Dataset

This dataset was created by wakibia

Contents

Dataset

This dataset was created by John Wilken Christoper

Contents

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.

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

The Telco Customer Experience Management (CEM) market is experiencing robust growth, projected to reach $2,522 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.7% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of digital channels by telecom companies necessitates sophisticated CEM solutions to ensure seamless and personalized customer interactions across various touchpoints, from online portals and mobile apps to social media and in-person interactions. Rising customer expectations for immediate issue resolution and proactive support are also driving demand for advanced analytics and AI-powered CEM tools that allow telcos to anticipate and address customer needs before they escalate into complaints. Furthermore, the growing competition within the telecom industry is pushing companies to invest heavily in improving customer loyalty and reducing churn through enhanced CEM strategies. Segmentation reveals strong demand from both large enterprises and small companies across diverse sectors including OTT, banking, and retail, reflecting the broad applicability of effective CEM solutions. The North American market currently holds a significant share, driven by early adoption of advanced technologies and a high concentration of telecom companies. However, rapid technological advancements and increasing digital penetration in regions like Asia Pacific and Europe are expected to fuel significant growth in these markets over the forecast period. While the market faces challenges such as high implementation costs and the need for specialized expertise, the strategic benefits of improved customer satisfaction, reduced operational costs, and increased revenue generation outweigh these constraints. Key players like Nuance, mPhasis, Tieto, Wipro, Tech Mahindra, IBM, Huawei, ChatterPlug, ClickFox, and InMoment are actively shaping the market landscape through innovation and strategic partnerships, further accelerating growth within the Telco CEM sector.

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.

📌**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)

Big Data Analytics In Telecom Market Outlook

The global market size for Big Data Analytics in the Telecom sector was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 50 billion by 2032, exhibiting a robust CAGR of 20% during the forecast period. This impressive growth trajectory is fueled by the increasing demand for advanced analytics to optimize operations, enhance customer experience, and improve network management. The telecom sector's continuous expansion and the proliferation of connected devices are also significant contributors to this market's rapid growth.

One of the primary growth factors for this market is the exponential increase in data generation. With the advent of 5G technology, the volume of data transmitted over networks has surged, necessitating sophisticated analytics to manage and utilize this data effectively. Telecom companies are increasingly relying on big data analytics to derive actionable insights from vast datasets, which can lead to improved decision-making and strategic planning. Moreover, the integration of IoT devices and services has further amplified data traffic, making analytics indispensable for telecom operators.

Another crucial driver is the need for enhanced customer experience. Telecom operators are leveraging big data analytics to gain deeper insights into customer behavior, preferences, and pain points. This data-driven approach allows for personalized marketing strategies, better customer service, and reduced churn rates. By analyzing customer data, telecom companies can identify trends and patterns that help in developing targeted campaigns and offers, thereby increasing customer loyalty and satisfaction.

Operational efficiency is also a significant factor propelling the growth of big data analytics in the telecom market. Telecom operators are under constant pressure to improve their network performance and reduce operational costs. Big data analytics enables real-time monitoring and predictive maintenance of network infrastructure, leading to fewer outages and improved service quality. Additionally, analytics helps in optimizing resource allocation and enhancing the overall efficiency of telecom operations.

Regionally, North America holds a substantial share of the big data analytics in telecom market, driven by the presence of leading telecom companies and advanced technology infrastructure. Additionally, the Asia Pacific region is expected to witness the fastest growth rate due to the rapid digital transformation and increasing adoption of advanced analytics solutions in emerging economies like China and India. European countries are also making significant investments in big data analytics to enhance their telecom services, contributing to the market's growth.

Component Analysis

In the context of components, the Big Data Analytics in Telecom market is segmented into software, hardware, and services. The software segment is anticipated to dominate the market, as telecom operators increasingly invest in advanced analytics platforms and tools. The software solutions facilitate the processing and analysis of large datasets, enabling telecom companies to gain valuable insights and improve decision-making processes. Moreover, the software segment includes various sub-categories such as data management, data mining, and predictive analytics, each contributing significantly to market growth.

The hardware segment, although smaller compared to software, plays a critical role in the overall ecosystem. This segment includes servers, storage systems, and other hardware components necessary for data processing and storage. As data volumes continue to grow, the demand for robust and scalable hardware solutions is also on the rise. Telecom companies are investing in high-performance hardware to ensure seamless data management and analytics capabilities. The hardware segment is essential for supporting the infrastructure needed for big data analytics.

On the services front, the market is witnessing substantial growth due to the increasing need for consulting, integration, and maintenance services. Telecom operators often require expert guidance and support to implement and manage big data analytics solutions effectively. Service providers offer a range of services, including system integration, data migration, and ongoing support, which are crucial for the success

The global Telco Customer Experience Management (CEM) market is experiencing robust growth, driven by the increasing demand for seamless and personalized customer interactions in the competitive telecommunications industry. The market's value, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% between 2025 and 2033. This growth is fueled by several key factors. Firstly, the rising adoption of digital channels and the increasing expectation of omnichannel customer service are pushing Telcos to invest heavily in CEM solutions. Secondly, the increasing availability of advanced technologies such as artificial intelligence (AI), machine learning (ML), and big data analytics allows for sophisticated customer behavior analysis and personalized service delivery, significantly improving customer satisfaction and loyalty. Finally, the growing focus on proactive customer service and preventative measures to avoid churn are contributing to the market's expansion. Key players such as Nuance, mPhasis, Tieto, Wipro, Tech Mahindra, IBM, Huawei, ChatterPlug, ClickFox, and InMoment are actively shaping the market landscape through innovative solutions and strategic partnerships. Competition is intense, pushing companies to differentiate themselves through superior analytics, integration capabilities, and customer-centric approaches. The market segmentation within the Telco CEM space is likely complex, encompassing solutions for various customer touchpoints (voice, digital channels, social media), customer lifecycle stages (acquisition, retention, churn management), and specific services (billing, technical support, customer care). While regional data is currently unavailable, we can expect variations in market penetration based on factors such as digital infrastructure maturity, regulatory landscapes, and consumer technological adoption rates. North America and Europe are expected to hold significant market shares, though the Asia-Pacific region is anticipated to witness substantial growth in the coming years, driven by increasing smartphone penetration and rising internet usage. Restraints on market growth might include the high initial investment costs associated with implementing comprehensive CEM solutions, integration challenges with existing legacy systems, and the need for skilled professionals to manage and analyze the vast amounts of data generated. However, the long-term benefits in terms of improved customer satisfaction, reduced churn, and increased operational efficiency are expected to outweigh these challenges.

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.

Telco Customer Experience Management Market Outlook

As of 2023, the global telco customer experience management market size is estimated to be approximately USD 3.5 billion and is projected to grow to USD 7.8 billion by 2032, reflecting a robust CAGR of 9.2% over the forecast period. This remarkable growth is primarily driven by the increasing demand for optimizing customer interactions across various touchpoints, coupled with the rapid digital transformation witnessed in the telecommunications sector. The industry's focus on enhancing customer satisfaction and loyalty by leveraging advanced technologies such as AI, big data analytics, and automation is a key factor propelling the market forward.

The shift towards digitalization is a major growth driver in the telco customer experience management market. With the advent of new technologies, telecommunication companies are increasingly aiming to provide personalized and efficient customer service. The use of artificial intelligence and machine learning allows companies to analyze vast amounts of customer data to predict behavior, understand preferences, and tailor services accordingly. Additionally, the integration of big data analytics helps identify potential issues and improve service delivery, thereby enhancing overall customer satisfaction. This technological advancement is central to the market's expansion.

Another significant growth factor is the increasing competition within the telecommunications industry. As the market becomes saturated, companies are striving to differentiate themselves by offering superior customer experiences. This is achieved through strategic investments in customer experience management solutions that streamline processes and enhance efficiency. By focusing on the customer journey and addressing issues such as service quality, response time, and personalized interactions, telcos aim to retain customers and reduce churn rates. The competitive landscape thus acts as a catalyst for companies to innovate and improve their customer experience strategies.

Furthermore, regulatory compliance and customer-centric policies are driving the demand for customer experience management in the telecom sector. With stringent regulations in place, telecommunication companies are compelled to focus on transparency and customer satisfaction. This has led to the adoption of robust CEM solutions that not only ensure compliance but also foster trust and loyalty among customers. Moreover, as regulatory bodies push for improved customer services and data protection, telcos are investing in advanced systems to meet these requirements effectively, thereby fueling market growth.

From a regional perspective, North America is expected to lead the telco customer experience management market due to the early adoption of advanced technologies and the presence of leading market players. The region's well-established telecommunications infrastructure further supports the implementation of sophisticated CEM solutions. Meanwhile, Asia Pacific is anticipated to witness the highest growth rate owing to the rapid expansion of the telecom industry and increasing internet penetration. The growing middle class in countries like China and India, coupled with their increasing demand for enhanced customer services, contributes significantly to the regional market's expansion.

Component Analysis

The telco customer experience management market, when dissected by component, comprises both solutions and services. Solutions, which include software platforms designed to enhance customer interactions, play a pivotal role in the market. These platforms offer comprehensive capabilities, such as real-time analytics, customer journey mapping, and feedback management, enabling telecom companies to gain deep insights into customer behaviors and preferences. With the increasing demand for personalized and seamless customer experiences, the solutions segment is anticipated to witness substantial growth. Moreover, the adoption of AI-driven solutions that automate customer service processes is on the rise, further boosting this segment.

On the other hand, the services segment is also crucial as it encompasses consulting, training, support, and maintenance services that facilitate the effective deployment and utilization of CEM solutions. As the market evolves, the demand for professional services that assist telecom operators in transforming their customer experience strategies is growing. This demand is driven by the need for expert guidance in integrating complex solutions into existing systems. Additionally, manage

This dataset provides detailed call log records linked to customer churn events, including call metadata, customer demographics, churn reasons, and resolution outcomes. It enables comprehensive analysis of why customers leave, how call center interactions influence churn, and supports the development of targeted retention strategies. The dataset is ideal for churn prediction modeling, root cause analysis, and customer experience optimization.

The global Telco Customer Experience Management market is expected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX%. The market growth is primarily driven by the increasing adoption of customer experience management solutions by telecommunication companies to improve customer satisfaction and loyalty, reduce churn, and increase revenue. Additionally, the growing demand for personalized and omnichannel customer experiences, the proliferation of mobile devices and the internet of things (IoT), and the need for real-time customer support are further fueling the market growth. The market is segmented based on application into customer relationship management (CRM), customer service management, customer experience analytics, and others. The CRM segment is expected to hold the largest market share during the forecast period, owing to the growing need for managing customer relationships effectively. Based on type, the market is divided into on-premises and cloud-based. The cloud-based segment is expected to witness the highest growth rate during the forecast period, due to the increasing adoption of cloud-based solutions by telecommunication companies for their flexibility, scalability, and cost-effectiveness. The key players in the Telco Customer Experience Management market include Nuance, mPhasis, Tieto, Wipro, Tech Mahindra, IBM, Huawei, ChatterPlug, ClickFox, and InMoment.

Dataset

This dataset was created by Ramasubramanian M

Released under MIT

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