The Customer log dataset is a 12.5 GB JSON file and it contains 18 columns and 26,259,199 records. There are 12 string columns and 6 numeric columns, which may also contain null or NaN values. The columns include userId, artist, auth, firstName, gender, itemInSession, lastName, length, level, location, method, page, registration, sessionId, song,status, ts and userAgent. As evident from the column names, the dataset contains various user-related information, such as user identifiers, demographic details (firstName, lastName, gender), interaction details (artist, song, length, itemInSession, sessionId, registration, lastinteraction) and technical details (userAgent, method, page, location, status, level, auth).

Dataset

This dataset was created by R. Joseph Manoj, PhD

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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.

In the second quarter of 2024, Verizon's wireless retail churn rate was 1.63 percent. This was a marginal increase on the same period in 2024, but short of the 1.73 percent churn rate reported for the final quarter of 2023.

Dataset

This dataset was created by Sercan Yeşilöz

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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 ---

Dataset

This dataset was created by Prasanth Raj

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The market size of the Customer Churn Software Market is categorized based on Type (Cloud Based, Web Based) and Application (Telecommunications, Banking and Finance, Retail and E-commerce, Healthcare, Insurance, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This report provides insights into the market size and forecasts the value of the market, expressed in USD million, across these defined segments.

T-Mobile reported a prepaid customer churn rate of 2.75 percent in the United States in the first quarter of 2024. This was a decrease in comparison to the last two quarters of 2023. The company's prepaid churn rate has fallen over recent years, having peaked at over five percent in the final quarter of 2014.

Dataset

This dataset was created by Hajarkhagd

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Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making.

Dataset

This dataset was created by pavani k

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Analysis of ‘Bank Turnover Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling on 28 January 2022.

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

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

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.

Literature review of papers on churn prediction in telecommunication.

Rogers Communications reported a monthly wireless postpaid churn rate of 1.1 percent during the first quarter of 2024. The firm's prepaid churn rate for the same period was 3.9 percent, the lowest rate since the fourth quarter of 2017.

Churn prediction aims to detect customers intended to leave a service provider. Retaining one customer costs an organization from 5 to 10 times than gaining a new one. Predictive models can provide correct identification of possible churners in the near future in order to provide a retention solution. This paper presents a new prediction model based on Data Mining (DM) techniques. The proposed model is composed of six steps which are; identify problem domain, data selection, investigate data set, classification, clustering and knowledge usage. A data set with 23 attributes and 5000 instances is used. 4000 instances used for training the model and 1000 instances used as a testing set. The predicted churners are clustered into 3 categories in case of using in a retention strategy. The data mining techniques used in this paper are Decision Tree, Support Vector Machine and Neural Network throughout an open source software name WEKA.

Dataset to train and test a churn classifier model for a ecommerce company.

Dataset

This dataset was created by Bhavika Saini

Released under Apache 2.0

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Dataset

This dataset was created by Mohammed Ikramuddin

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