![]() As a result, it is critical to conduct research and use advanced DL approaches to customer data in order to accurately assess customer churn. There is considerable interest in using DL to assist companies in accurately forecasting customer churn from historical data. DL has been used successfully in a variety of fields, including stock price forecasting, personality recognition, disease prediction, text categorization, and others. However, these models do not provide an effective method for identifying clients who are likely to depart the organization.ĭeep learning (DL) is a new discipline of computer science that extracts patterns from past data and makes accurate predictions using feature embedding methods. 5 used KNN for classification in previous study. For machine learning classifiers, several studies rely on human feature engineering methods. Traditional churn prediction methods frequently have scaling concerns. Anticipating customer churn through data analysis has become critical for attracting and retaining customers, since it allows firms to anticipate probable reasons for customer turnover and take early actions to resolve them. When consumers leave, businesses incur significant costs, making customer retention critical for economic viability. Customer churn can also be caused by dissatisfaction with present offerings or unfulfilled demands 4. Customer attrition can also be caused by issues such as poor product quality or a perceived lack of security. Customers may transfer providers for a variety of reasons, including pricing, product delivery methods, and customer service encounters. Research motivationĬustomer churn has a substantial impact on enterprises, resulting in possible profits or losses and even the possibility of business closure 3. A corporation can take steps to retain existing customers, improve product or service quality, and avoid major losses by proactively addressing customer churn 3. Customer turnover must be predicted in order to design effective retention measures. ![]() Avoiding client churn has become a critical goal for every company looking to expand its revenue. The basic goal of customer churn prediction is to identify customers who are likely to leave the company. To keep current clients, a corporation needs to analyze data in the customer database to determine the reasons for their departure 2. It is also referred to as customer attrition, and it occurs when customers stop using a company's products or services. Furthermore, a large migration of unsatisfied consumers could have serious financial and reputational consequences for the organization 2.Ĭustomer churn is the period in which a company suffers considerable losses as a result of frequent customers leaving. A single bad encounter can result in the irreversible loss of a consumer. The telecommunications sector is very competitive, with multiple suppliers providing comparable services. Maintaining the happiness of existing consumers is critical in subscription-based product expansion. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%.Ĭustomers are critical to any company's success, thus every effort is made to assure their satisfaction 1. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process's accuracy. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers.
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