Customer data prediction and analysis in e-commerce using machine learning (original) (raw)
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Customer Churn Prediction on E-Commerce Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
For E-commerce businesses to produce successful marketing plans and customer retention tactics, client churn vaticination is pivotal. In order to handle the longitudinal timeframes and multiple data variables of B2Ce-commerce consumers' buying habits, the authors of this study present a loss vaticination model that integrates k-means client segmentation with support vector machine (SVM) vaticination. guests are divided into three groups according to the approach, which also defines the main customer groupings. In order to anticipate client development, the study analyses the efficacity of logistic retrogression and SVM vaticination. The findings show that client segmentation greatly increases each indicator's capability to read values, emphasizing the significance of k-means clustering segmentation. also, it's demonstrated that SVM vaticination is more accurate than logistic retrogression vaticination. The conclusions of this study have important ramifications for client relationship operation.
E-Business Churn Prediction Model Using Machine Learning
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Businesses need to keep their clients in the present competitive environment in order to remain in the market. To achieve this, they must anticipate customer attrition and take proactive steps to keep clients. In this research, we offer a model for predicting customer churn based on machine learning that can forecast the probability of consumers leaving with accuracy. To anticipate customer turnover, we employ a variety of machine learning techniques, including logistic regression, random forest, and support vector machines. To assess the effectiveness of our methodology, we additionally employ a number of assessment measures. Our findings show that the suggested model works better than the current models and can aid companies in keeping consumers. Keywords : Machine learning, Logistic Regression, Random Forest, and Customer Churn Customer retention, classification, e-business churn forecast, accuracy, precision, recall, F1-score, Log loss, ROC AUC, calibration loss, cost matrix gain
Performance Analysis of Machine Learning Algorithms in Customer Churn Prediction
2018
Customer attrition is termed by several industrialists and e-commerce professionals to recognize the customers, who are about to change their service from the existing company or end their period of subscription. In recent years, companies such as e-commerce, telecommunication and insurance sectors are facing tremendous pressure due to financial disintermediation and marketing and the gradual increase in the competitiveness tends to provide better service with lesser cost. So, early prediction of the behaviour of the clients plays an important role in the real-time market and can help to retain the loyal customers. In this research, a survey on different data mining techniques and machine learning algorithms along with the challenges of customer attrition prediction in the motor insurance sector are depicted. The survey on the application of the various machine learning algorithm for churn prediction is mainly observed in telecommunication sector and Support Vector Machine (SVM), Ar...
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
In current days, the customers are getting more attracted towards the quality of service (QoS) provided by the organizations. However, the current era is evidencing higher competition in providing technologically advanced QoS to the customers. Nevertheless, efficient customer relationship management systems can be advantageous for the organization for gaining more customers, maintaining customer relationships and improve customer retention by adding more profit to the organizational business. Furthermore, the machine learning models such as support vector machine Random Forest algorithms can add more value to the customer retention strategies.
Customer Churn Prediction Using Machine Learning Algorithms
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Customer churn is a serious problem in the telecommunications industry and occurs more often. Customer churn is the percentage of customers that stopped using your company's product or service during a certain time frame. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms will be use in this paper are Decision Tree, Support Vector Machine, Random Forest. Also, the performance of support vector were better than other algorithms.
Customer Churn Prediction Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1-score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE, SMOTEENN, and Random under Sampler to balance it. Our study shows that machine learning can anticipate client attrition. Ensemble learners perform better than single base learners, and a balanced training dataset should increase classifier performance. I.
Machine Learning Approaches to Predict Customer Churn in Telecommunications Industry
IRJET, 2023
This project aims to develop machine learning models for predicting customer churn in the telecommunications industry. The project will analyze various customer behavior and demographic data, such as tenure, payment method,monthly charges, total charges, etc to identify patterns and build predictive models. The project will use advanced techniques, such as logistic regression, decision trees, support vector machine and random forests, to predict customer churn accurately. The study will help the telecommunications industry to understand the reasons behind customer churn and implement effective strategies to reduce customer churn rates. The results of this project can be useful for improving customer retention and enhancing the overall customer experience in the industry.
Journal of Systems and Information Technology, 2017
Purpose This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model. Findings According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution. Practical implications Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers. Originality/value The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using…
2020
Customer acquisition and retention is a key concern for several industries and is particularly acute in fiercely competitive and fast growth businesses. Retaining a loyal customer is far more important than acquiring a new one, thus making customer churn one of the critical concerns for big corporations. Finding factors triggering customer churn is vital to implement necessary remediation to pre-empt and cut back this churn. This research focuses on implementing machine learning (ML) algorithms to identify potential churn customers, categorise them based upon usage patterns, and visualize the analysis results. Results show that Extra Trees Classifier, XGBoosting Algorithm and Support Vector Machine have the best churn modelling performance, particularly for 80:20 dataset distribution with average AUC scores of 0.843, 0.787 and 0.735 respectively and low false negatives. The research demonstrates that ML algorithms can successfully predict potential customer churn and help in devisin...
Dynamic Churn Prediction Using Machine Learning Algorithms on Telecommunication
IJETMS, 2023
Any organization's ability to increase revenue and profit depends heavily on its customers. So, it is crucial for organizational managers to keep a single effective customer relationship management system by choosing the target consumers and maintaining effective relationships with them. This would help them to increase customer satisfaction. Customers are becoming more drawn to the quality of service (QoS) offered by businesses in the present. Yet, the present day shows greater rivalry in offering clients technologically cutting-edge QoS. Yet, effective customer relationship management systems can help the organization attract new clients, preserve client connections, and enhance client retention by generating more revenue for the company's operations. Also, the client retention methods can benefit greatly from the use of machine learning models like support vector machines and Random Forest algorithms. One essential machine learning approach that effectively analyses the data for churn prediction is support vector machine learning. The telecom industries have extensively embraced machine learning as an efficient application of artificial intelligence in assessing and mitigating customer turnover.