This dataset is for ABC Multistate bank with following columns:

1. customer_id, unused variable.
2. credit_score, used as input.
3. country, used as input.
4. gender, used as input.
5. age, used as input.
6. tenure, used as input.
7. balance, used as input.
8. products_number, used as input.
9. credit_card, used as input.
10. active_member, used as input.
11. estimated_salary, used as input.
12. churn, used as the target. 1 if the client has left the bank during some period or 0 if he/she has not.

Aim is to Predict the Customer Churn for ABC Bank.

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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.

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.

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.

The Customer Churn Software market is rapidly evolving as businesses increasingly recognize the critical importance of retaining existing customers in a competitive landscape. Customer churn, the phenomenon where customers cease to engage with a business or terminate their subscriptions, poses a significant challeng

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.

This is the full version of the KDD Cup 2009 dataset

This Year's Challenge

Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).

The most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is an evaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.

The challenge is to beat the in-house system developed by Orange Labs. It is an opportunity to prove that you can deal with a very large database, including heterogeneous noisy data (numerical and categorical variables), and unbalanced class distributions. Time efficiency is often a crucial point. Therefore part of the competition will be time-constrained to test the ability of the participants to deliver solutions quickly.

Task Description

The task is to estimate the churn, appetency and up-selling probability of customers, hence there are three target values to be predicted. The challenge is staged in phases to test the rapidity with which each team is able to produce results. A large number of variables (15,000) is made available for prediction. However, to engage participants having access to less computing power, a smaller version of the dataset with only 230 variables will be made available in the second part of the challenge.

• Churn (wikipedia definition): Churn rate is also sometimes called attrition rate. It is one of two primary factors that determine the steady-state level of customers a business will support. In its broadest sense, churn rate is a measure of the number of individuals or items moving into or out of a collection over a specific period of time. The term is used in many contexts, but is most widely applied in business with respect to a contractual customer base. For instance, it is an important factor for any business with a subscriber-based service model, including mobile telephone networks and pay TV operators. The term is also used to refer to participant turnover in peer-to-peer networks.

• Appetency: In our context, the appetency is the propensity to buy a service or a product.

• Up-selling (wikipedia definition): Up-selling is a sales technique whereby a salesman attempts to have the customer purchase more expensive items, upgrades, or other add-ons in an attempt to make a more profitable sale. Up-selling usually involves marketing more profitable services or products, but up-selling can also be simply exposing the customer to other options he or she may not have considered previously. Up-selling can imply selling something additional, or selling something that is more profitable or otherwise preferable for the seller instead of the original sale.

The training set contains 50,000 examples. The first predictive 14,740 variables are numerical and the last 260 predictive variables are categorical. The last target variable is binary (-1,1).

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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

AI-Powered Chatbots and Virtual Assistants: Chatbots and virtual assistants use AI to provide customer support and interact with users through natural language processing. They can handle a wide range of queries, freeing up human agents for more complex tasks.Predictive Analytics: Predictive analytics uses AI to analyze data and identify patterns. In the telecommunication industry, predictive analytics is used for fraud detection, network optimization, and customer churn prediction.Network Optimization: AI-powered solutions can optimize network performance by analyzing network data and identifying areas for improvement. This can lead to reduced downtime, improved bandwidth utilization, and enhanced customer experience. Recent developments include: June 2023: Amdocs, an American software and services provider to communications and media organizations, announced Amdocs amAIz, a telco generative AI platform. This creative approach combines huge language AI models with open-source technologies and carrier-grade architecture. By doing this, Amdocs amAIz gives international telecom service providers a strong platform on which to build in order to fully utilize the enormous potential of generative AI., February 2023: Bharti Airtel, an Indian telecommunications service provider, said that it had built an AI solution in conjunction with NVIDIA to improve the customer experience for its contact center from all inbound calls., September 2022: Amazon Web capabilities (AWS), an IT service management company, and SK Telecom, a telecommunications corporation, partnered to build a new set of computer vision capabilities. Through this relationship, the process of developing, deploying, and growing computer vision applications is made simpler and more efficient, which eventually increases productivity, lowers costs, and improves facility safety for customers as well as equipment maintenance.. Potential restraints include: Lack of reliable network infrastructure need for high-speed connectivity, Security & Privacy Concerns.

Global Customer Success Platforms Market size was valued at USD 17.92 Billion in 2024 and is projected to reach USD 93.50 Billion by 2031 growing at a CAGR of 25.30% from 2024 to 2031.

The Customer Success Platforms market is driven by several key factors, including the growing emphasis on customer retention and expansion, which is critical for subscription-based businesses and SaaS companies. Companies are increasingly investing in these platforms to enhance customer experience, reduce churn, and increase lifetime value. The rise of digital transformation initiatives, coupled with the need for real-time customer insights and personalized engagements, further accelerates the adoption of these platforms. Additionally, the integration of AI and machine learning in Customer Success Platforms is providing advanced analytics and predictive capabilities, making these solutions more effective and attractive to organizations. As businesses continue to shift toward customer-centric models, the demand for robust Customer Success Platforms is expected to grow significantly.

According to Cognitive Market Research, the global Artificial Intelligence in Marketing Market size is USD 12.7 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 23.8% from 2024 to 2031.

Market Dynamics of Artificial Intelligence in Marketing Market

Key Drivers for Artificial Intelligence in Marketing Market



  Increasing demand for predictive analysis - AI can predict consumer behavior, such as purchasing habits and churn rates. This enables marketers to anticipate customer requirements and preferences, allowing them to solve concerns and provide relevant solutions ahead of time. AI allows marketers to provide highly tailored information and offers to individual customers based on their interests, purchasing history, and behavior. Personalization improves consumer engagement, contentment, and loyalty, resulting in more conversions and revenue. As a result, the market is growing due to increased demand for personalization and predictive analytics.


  Rapid adoption of artificial intelligence in the healthcare Application




Key Restraints for Artificial Intelligence in Marketing Market



  Cost and data privacy issues


  Maintaining data privacy and security concerns

Introduction of the Artificial Intelligence in Marketing Market

Artificial intelligence (AI) in marketing is the incorporation of advanced algorithms and machine learning techniques into various marketing processes and tactics. This cutting-edge technology lets businesses to use data-driven insights, automate repetitive operations, and provide personalized experiences to their target audience, resulting in higher customer engagement, efficiency, and ROI. AI's applicability in marketing is diverse, ranging from monitoring consumer behavior and predicting trends to optimizing ad campaigns and improving customer service. The growing usage of artificial intelligence and machine learning to provide social networking platform acceptance, tailored consumer experiences, and the growth of e-commerce are the main drivers driving the market's development.

Telecom Analytics Market size was valued at USD 5.06 Billion in 2024 and is projected to reach USD 14.64 Billion by 2031, growing at a CAGR of 14.20% from 2024 to 2031.

The telecom analytics market is driven by the growing demand for data-driven insights to enhance customer experience, optimize network performance, and improve operational efficiency in an increasingly competitive telecom landscape. The surge in mobile data usage, fueled by the proliferation of smartphones and high-speed internet, has created vast amounts of data, prompting telecom operators to adopt advanced analytics solutions. Telecom analytics help in fraud detection, churn prediction, and revenue assurance, enabling companies to make more informed decisions. The integration of AI, machine learning, and big data technologies further enhances the capabilities of analytics tools, allowing for real-time decision-making and predictive analysis. Additionally, regulatory requirements for compliance and the increasing need to monetize network infrastructure drive the adoption of telecom analytics solutions. The shift toward 5G and IoT also presents new opportunities for telecom analytics in managing complex and data-intensive networks.

The global customer relationship management (CRM) software market is forecast to grow to 57 billion U.S. dollars in size in 2025. This is a projected increase of almost four billion U.S. dollars from 2021, at a compound annual growth rate (CAGR) of 2.1 percent.

Customer relationship management

Customer relationship management, or CRM, is the technology used to analyze and manage a company’s interaction with customers or potential customers. The goal of CRM is to improve companies’ relationships with customers, therefore increasing customer retention rates and ultimately driving sales growth. According to a 2018 survey, only 16 percent of U.S. organizations considered their company’s delivery of real-time customer interactions across touch points and devices as effective, showing that it is important for organizations to make use of the CRM technology. Scaling customer-centered decisions and actions across function in the business is the main driver behind organizations’ increased investment in real-time customer analytics in the United States.

CRM vendors

Salesforce is the leader in the CRM applications market, with a share of 16.8 percent in 2018. Other contenders in the market include Oracle and SAP, with 5.7 and 5.6 percent market shares respectively in 2018.

The size and share of the market is categorized based on Application (Churn Analysis, Customer Segmentation, Revenue Forecasting, KPI Monitoring) and Product (Cloud-Based, On-Premises, Hybrid) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).