Dataset

This dataset was created by R. Joseph Manoj, PhD

Contents

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).

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.

Dataset

This dataset was created by kavinp0301

Released under Apache 2.0

Contents

Dataset

This dataset was created by Al Amin

Contents

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.

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

This dataset was created by Arjun Selvamurugan

Contents

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

Dataset

This dataset was created by ASMIT BANDYOPADHYAY

Released under Apache 2.0

Contents

Dataset

This dataset was created by Abraz Laskar

Contents

The predictions of classifier C for samples of dataset X.

The Big Data and Machine Learning (BDML) in Telecom market is experiencing robust growth, driven by the explosive increase in mobile data traffic, the rise of 5G networks, and the increasing need for personalized customer experiences. The market, valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching an estimated $60 billion by 2033. This expansion is fueled by several key factors. Telecom operators are leveraging BDML for network optimization, predictive maintenance, fraud detection, customer churn prediction, and personalized service offerings. The adoption of descriptive, predictive, and prescriptive analytics across various applications, including processing, storage, and analysis of vast datasets, is a significant driver. Furthermore, advancements in machine learning algorithms and feature engineering techniques are empowering telecom companies to extract deeper insights from their data, leading to significant efficiency gains and improved revenue streams. The increasing availability of cloud-based BDML solutions is also fostering wider adoption, particularly among smaller operators. However, challenges remain. Data security and privacy concerns, the need for skilled data scientists and engineers, and the high initial investment costs associated with implementing BDML solutions can hinder market growth. Despite these restraints, the strategic advantages offered by BDML are undeniable, making its adoption crucial for telecom companies aiming to stay competitive in a rapidly evolving landscape. Segments like predictive analytics and machine learning for network optimization are expected to experience the most significant growth during the forecast period, driven by the increasing complexity of telecom networks and the demand for proactive network management. Geographic regions such as North America and Asia Pacific, with their advanced technological infrastructure and substantial investments in 5G, are anticipated to lead the market, followed by Europe and other regions.

Literature review of papers on churn prediction in telecommunication.

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.

CRM Analytics Market size was valued at USD 8.94 Billion in 2024 and is projected to reach USD 20.95 Billion by 2031, growing at a CAGR of 11.23 % during the forecast period 2024-2031.

Global CRM Analytics Market Drivers

1. Decision Making Based on Data
Data is becoming a more important factor for businesses to consider when making strategic decisions. Organisations can use CRM analytics to examine enormous volumes of customer data and find trends, patterns, and insights that can guide corporate strategy. Businesses can improve business outcomes by using this data-driven strategy to help them make well-informed decisions regarding customer service, sales, and marketing. The market for CRM analytics is mostly driven by companies’ transition to a data-centric culture.

2. Machine learning and AI advancements
The way companies handle customer connections is being completely transformed by the incorporation of AI and ML technology into CRM systems. Deeper insights into consumer behaviour and preferences can be obtained by using AI and ML algorithms to process massive datasets more correctly and effectively than with conventional techniques. Predictive analytics, which helps companies foresee customer demands and trends, is made possible by these technologies. This enables proactive rather than reactive customer relationship management. Thus, the market for CRM analytics is being driven ahead by the ongoing developments in AI and ML.

3. Spread of Personal Information
An abundance of consumer data produced by social media, internet, mobile apps, and Internet of Things devices has resulted from the digital transformation of many businesses. Businesses face both opportunities and challenges as a result of this massive amount of data. In order to compile and analyse this data and derive actionable insights, CRM analytics tools are crucial. The need for advanced CRM analytics systems that can manage large, complex data sets and deliver useful insights is being driven by the growth in both the volume and variety of customer data.

4. Demanding Tailored Customer Experiences
Contemporary customers demand individualised services that are catered to their own tastes and habits. Businesses can segment their customer base and comprehend the particular requirements of various customer groups with the help of CRM analytics. Businesses can use these insights to provide individualised product recommendations, focused marketing efforts, and unique customer support encounters. As companies work to increase customer pleasure and loyalty, the increased expectation for personalisation is a major factor driving the adoption of CRM analytics.

5. Pay attention to client retention and loyalty
Getting new clients is frequently more expensive than keeping the ones you already have. Consequently, enterprises are directing their attention towards enhancing customer retention and cultivating enduring loyalty. CRM analytics offers insightful information about potential churn risks, customer engagement, and satisfaction. Businesses can lower customer churn and maintain customer engagement by implementing successful retention measures, such loyalty programmes and personalised messaging, by recognising these aspects. CRM analytics solutions are in high demand because of the emphasis placed on customer loyalty and retention.

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Emotion Analytics Market Size 2024-2028

The emotion analytics market size is forecast to increase by USD 4.44 billion, at a CAGR of 18.2% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for data-driven customer behavior management in various sectors, including IT and telecommunications. Emotion recognition technology, which includes tone of voice analysis and correlation engines, is increasingly being used in public safety and contact centers to enhance situational awareness and improve response times. In the automotive industry, emotion analytics is being utilized to provide personalized user experiences and optimize sales performance. Advanced audio mining algorithms and artificial intelligence are being employed to extract insights from labeled emotion data obtained from various sources, including wearable gadgets and audio recordings. The accuracy and reliability of emotion analytics solutions are key drivers of market growth, making them valuable tools for businesses seeking to improve customer engagement and satisfaction.

What will be the Size of the Market During the Forecast Period?

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The market is witnessing significant growth as businesses increasingly recognize the value of understanding and responding to human emotions. Emotion analytics is a branch of artificial intelligence that focuses on the identification, analysis, and interpretation of emotions from various data sources. This market is poised to revolutionize industries by providing valuable insights into customer and employee behavior, public safety, and fraud detection. Behavioral analytics is a crucial application of emotion analytics. By analyzing voice tone, facial expressions, and text data, businesses can gain a deeper understanding of their customers' emotions and intentions. This information can be used to optimize sales performance, improve customer experience, and enhance brand reputation management. Another key application of emotion analytics is in risk management. By analyzing emotions in real-time, businesses can identify potential threats and take preventative measures. For instance, in public safety analytics, emotion analytics can be used to detect anomalous behavior and alert authorities.
In fraud detection, it can help identify suspicious transactions based on the emotional tone of the communication. Emotion analytics also plays a vital role in human-computer interaction. Conversational AI, a subset of emotion analytics, enables machines to understand and respond to human emotions. This technology can be used to improve customer service, automate contact centers, and enhance the overall customer experience. predictive analytics is another area where emotion analytics is making a significant impact. By analyzing historical data and identifying patterns, emotion analytics can predict customer churn, agent performance, and product perception. This information can help businesses take proactive measures to retain customers and improve employee performance. Cloud-based technology is driving the adoption of emotion analytics in various industries. Emotion analytics software, which can be integrated with existing systems, offers real-time insights and easy scalability. Sentiment analysis tools, a popular application of emotion analytics, help businesses measure customer satisfaction and monitor brand reputation.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Customer experience management
  Sales and marketing management
  Competitive intelligence
  Public safety and law enforcement
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

By Application Insights

The customer experience management segment is estimated to witness significant growth during the forecast period.

In today's business landscape, understanding user experience and emotions plays a pivotal role in gaining customer satisfaction and loyalty. Emotion analytics, a growing field, enables companies to diagnose and respond to customers' emotions using advanced machine learning algorithms. This data is collected through various sources such as virtual reality (VR), wearables, and user feedback. Emotion recognition technology, which includes facial recognition and haptic touch, is a significant component of emotion analytics. By analyzing customers' emotional responses, businesses can tailor their interactions, creating a more engaging experience. For instance, this technology can be used to gauge viewers