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.

The Customer Churn Software market is experiencing robust growth, driven by the increasing need for businesses across diverse sectors to improve customer retention and enhance profitability. The market's expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting a wider range of businesses. Secondly, advancements in AI and machine learning are enabling more sophisticated churn prediction and proactive customer engagement strategies. The telecommunications, banking and finance, and retail and e-commerce sectors are currently leading the adoption, leveraging the software to identify at-risk customers and implement targeted retention programs. However, factors such as high implementation costs, integration challenges with existing systems, and the need for skilled personnel to manage the software can act as restraints on market growth. We project a substantial market expansion in the coming years, with a steady compound annual growth rate (CAGR) contributing to a significant increase in market value. The competitive landscape is dynamic, with established players like IBM, Salesforce, and Microsoft competing alongside specialized churn management solution providers. This competition fosters innovation and drives the development of more advanced features and functionalities. Looking ahead, the market will witness further consolidation through mergers and acquisitions, as larger companies seek to expand their market share. The increasing emphasis on data privacy and security regulations will also shape market dynamics, with vendors focusing on compliant solutions. The market is expected to witness the rise of niche solutions tailored to specific industry segments, providing customized functionalities. The geographic distribution of the market is expected to remain concentrated in North America and Europe initially, with significant growth potential in emerging markets like Asia Pacific and the Middle East & Africa, fueled by increasing digitalization and adoption of sophisticated business analytics. The continued evolution of AI and machine learning algorithms will be crucial in improving the accuracy and efficiency of churn prediction models, further enhancing the value proposition of Customer Churn Software. This convergence of technological advancement, regulatory compliance, and industry-specific needs will shape the future trajectory of the Customer Churn Software market.