Dataset

This dataset was created by Mohammed Assad

Contents

The data set includes the data of a Telecom company wherein we are to check if a customer would churn or not. Based on the different features it is to be predicted whether the customer would churn or not.

Dataset

This dataset was created by Deepanshu Saini

Contents

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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.

Dataset

This dataset was created by Adnan Amin

Contents

Dataset

This dataset was created by Sam kay

Released under CC0: Public Domain

Contents

"Telecom Customer Churn Prediction Dataset" is a synthetic dataset designed to simulate customer data for a telecommunications company. This dataset is created for the purpose of predicting customer churn, which refers to the phenomenon of customers discontinuing their services with the company. The dataset contains a variety of features that capture different aspects of customer behavior and characteristics.

The dataset includes information such as customer age, gender, contract type, monthly charges, total amount spent, number of devices connected, and the number of customer support calls made. The key focus of this dataset is the binary target variable "Churn," which indicates whether a customer has churned (1) or not (0). This variable is essential for training and evaluating predictive models aimed at identifying customers who are likely to leave the service.

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.

❓ Business Problem

TelcoConnect is experiencing significant customer churn, particularly among new subscribers. This churn results in substantial revenue loss and increased customer acquisition costs. The Marketing and Customer Success teams need to understand why customers are churning and who is most likely to churn, so they can implement targeted retention strategies.

📋 Objective

1. Identify key factors contributing to customer churn.
2. Build a predictive model to identify customers at high risk of churning.
3. Provide actionable recommendations to the Marketing and Customer Success teams to reduce churn.

This dataset contains customer information from a telecommunications company, aimed at predicting customer churn. The dataset includes demographic, service usage, and account information, along with the churn status of each customer. Each row represents a customer, and each column contains specific attributes related to customer demographics, services, account information, and whether or not the customer churned.

Column Descriptions: - customerID: A unique identifier for each customer. Used to distinguish between individual customers. - gender:

The gender of the customer (Male/Female). Helps analyze any gender-related patterns in customer behavior or churn. - SeniorCitizen:

Indicates if the customer is a senior citizen (1 for Yes, 0 for No). Used to assess if age plays a role in customer churn. - Partner:

Whether the customer has a partner (Yes/No). May influence customer retention due to family or household dynamics. - Dependents:

Whether the customer has dependents (Yes/No). Having dependents may impact the likelihood of churn based on service needs. - tenure:

The number of months the customer has stayed with the company. Key indicator for analyzing loyalty and churn tendencies over time. - PhoneService:

Whether the customer has a phone service (Yes/No). Helps assess if phone services impact churn or customer satisfaction. - MultipleLines:

Whether the customer has multiple lines (Yes/No or No phone service). Used to determine if having more services correlates with churn risk. - InternetService:

Type of internet service subscribed (DSL, Fiber optic, No). Internet service type could affect churn based on service quality. - OnlineSecurity:

Whether the customer has an online security add-on (Yes/No). Used to evaluate if security services influence customer retention. - OnlineBackup:

Whether the customer has an online backup add-on (Yes/No). Similar to OnlineSecurity, this service could impact churn. - DeviceProtection:

Whether the customer has a device protection plan (Yes/No). May indicate whether customers opt for value-added services. - TechSupport:

Whether the customer has opted for tech support (Yes/No). Quality and access to tech support might affect customer loyalty. - StreamingTV:

Whether the customer subscribes to a TV streaming service (Yes/No). Examines if entertainment services reduce the likelihood of churn. - StreamingMovies:

Whether the customer subscribes to a movie streaming service (Yes/No). Similar to StreamingTV, this service might influence retention. - Contract:

The type of contract the customer has (Month-to-month, One year, Two year). Longer contracts may lead to lower churn compared to month-to-month agreements. - PaperlessBilling:

Whether the customer has opted for paperless billing (Yes/No). May indicate digital engagement levels, which could impact churn. - PaymentMethod:

The customer’s payment method (e.g., Electronic check, Mailed check). Used to identify if specific payment methods relate to higher churn. - MonthlyCharges:

The amount the customer is charged monthly for their services. Higher charges might contribute to churn, especially for price-sensitive customers. - TotalCharges:

The total amount the customer has been billed during their tenure. Represents overall revenue per customer and helps analyze lifetime value. - Churn:

Whether the customer has churned (Yes/No). The target variable for prediction, indicating if a customer left the company.

Dataset

This dataset was created by Sourabh Pattanshetty

Contents

Dataset

This dataset was created by prsh_ch

Released under Apache 2.0

Contents

Customer churn refers to the phenomenon where customers discontinue their relationship or subscription with a company or service provider. It represents the rate at which customers stop using a company's products or services within a specific period. Churn is an important metric for businesses as it directly impacts revenue, growth, and customer retention.

In the context of the Churn dataset, the churn label indicates whether a customer has churned or not. A churned customer is one who has decided to discontinue their subscription or usage of the company's services. On the other hand, a non-churned customer is one who continues to remain engaged and retains their relationship with the company.

Understanding customer churn is crucial for businesses to identify patterns, factors, and indicators that contribute to customer attrition. By analyzing churn behavior and its associated features, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer turnover. Predictive modeling techniques can also be applied to forecast and proactively address potential churn, enabling companies to take proactive measures to retain at-risk custos.

1.Age Distribution and Churn Rate:

What is the distribution of ages among your customers? Is there a relationship between age and churn rate?

2.Gender Analysis:

What is the gender distribution of your customers? Is there any noticeable difference in churn rates between genders?

3.Tenure and Churn:

How long, on average, have your customers been with your service (tenure)? Is there any pattern between tenure and churn?

4.Usage Frequency:

How frequently do customers use your service, on average? Does usage frequency affect churn rates?

5.Support Calls and Churn:

What is the average number of support calls made by customers? Is there any correlation between support calls and churn?

6.Payment Delay:

What is the typical payment delay among customers? Does payment delay influence churn behavior?

7.Subscription Type and Contract Length:

What are the different subscription types and their proportions? Do customers with different subscription types have different churn rates? How does contract length relate to churn?

8.Total Spend and Churn:

What is the average total spend of customers? Is there any correlation between total spend and churn?

9.Last Interaction:

How recently did customers interact with your service? Is there any connection between the recency of the last interaction and churn?

Dataset

This dataset was created by Rajveer Tanwani

Contents

Dataset

This dataset was created by Sparsh Gupta

Contents

Dataset

This dataset was created by PangKW

Released under CC0: Public Domain

Contents

This dataset is being shared for the first time for public research after extensive research performed. See the following publications for more information.

• Jafari-Marandi, R., Denton, J., Idris, A., Smith, B. K., & Keramati, A. Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry. Neural Computing and Applications, 1-34.
• Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in the telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994-1012.
• Keramati, A., & Ardabili, S. M. (2011). Churn analysis for an Iranian mobile operator. Telecommunications Policy, 35(4), 344-356.

This dataset is perfect for practicing prescriptive analysis such as predictive prescription or predictive decision making. The reason is that the dataset has the attribute of customer value which allows for creating False Positive (FP) and False Negative(FN) costs in case of misclassification. In standard classification tasks, it is assumed that FPs and FNs are the same, which is not the case for many cases. Furthermore, even if it is recognized that FPs and FNs are indeed different, their different balances per each data object are not understood or taken into consideration. This dataset gives you the opportunity to create a model that recognizes these complexities. For further information about the balance of FPs and FNs see the first mentioned publication. Also, you can find more information about each attribute on one of the publications.

Telecom Churn Insights: Unraveling Customer Behavior in Telecommunication Industry

Description:

Explore the dynamic world of telecommunications with the Telecom Churn Insights dataset, a comprehensive collection of customer interactions, patterns, and churn behaviors meticulously curated for in-depth analysis. This dataset offers a unique opportunity to delve into the intricate landscape of customer relationships within the telecom industry.

Overview:

Telecom Churn Insights provides a rich and diverse dataset encompassing a wide array of customer attributes, usage patterns, and service preferences. With an extensive collection of features including customer demographics, call records, data usage, customer feedback, and subscription details, this dataset empowers data enthusiasts, analysts, and machine learning practitioners to unravel profound insights into customer behavior.

Key Features:

Customer Demographics: Dive into the demographics of telecom service subscribers, understanding their age, gender, location, and other essential details.

Usage Patterns: Analyze usage patterns related to calls, messages, data consumption, and service utilization, providing a deep understanding of customer engagement.

Service Preferences: Gain insights into the specific services customers prefer, such as international calling, data packages, value-added services, and more.

Churn Prediction: Leverage the churn labels to explore predictive analytics, identifying key indicators and patterns that signal potential customer churn.

Customer Feedback: Delve into customer feedback and survey responses, understanding customer satisfaction levels and areas for improvement.

Benefits:

Customer Retention Strategies: Identify factors contributing to customer churn, enabling the development of targeted retention strategies and personalized customer engagement initiatives.

**Predictive Analytics: **Utilize advanced machine learning techniques to build accurate churn prediction models, allowing telecom providers to proactively retain customers.

**Product Development: **Tailor services and products based on customer preferences, ensuring the delivery of offerings that meet customer demands.

Business Intelligence: Derive actionable insights through exploratory data analysis, enabling data-driven decision-making and strategic planning for business growth.

Enhanced Customer Experience: By understanding customer behavior, preferences, and pain points, telecom companies can enhance customer experience, driving customer loyalty and satisfaction.

Ideal For:

• Data Analysts and Scientists -Machine Learning Practitioners -Telecom Industry Researchers -Business Intelligence Professionals -Marketing and Customer Engagement Teams

Embark on a transformative data journey with the Telecom Churn Insights dataset, where every data point tells a story, and every insight drives strategic decisions in the dynamic telecom landscape. Uncover patterns, predict churn, and revolutionize customer experiences with this invaluable resource for telecom analytics.

Customer Churn Analysis

Customer churn, also known as customer attrition, is when a customer essentially stops being a customer- ie, they choose to stop using your products or services. Customer Churn is one of the most important and challenging problems for businesses such as Credit Card companies, cable service providers, SASS and telecommunication companies worldwide.

What is Churn Analysis? Customer churn analysis is the process of using your churn data to understand:

Which customers are leaving? Why are they leaving? What can you do to reduce churn? As you may have guessed, churn analysis goes beyond just looking at your customer churn rate. It’s about discovering the underlying causes behind your numbers.

Ultimately, successful churn analysis will give you the valuable insights you need to start reducing your business’s customer attrition rate.