Customer Churn Prediction

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the customer churn. The overall accuracy of the model, Receiver Operating Characteristic curve and Area Under the Receiver Operating Characteristic Curve is used as the evaluation metrics for this research to identify the best classifier. A comparative study on the most popular supervised machine learning methods – Logistic Regression, Random Forest, Support Vector Machine (SVM) and Neural Network were applied to customer churning prediction in a CU context. In the first phase of our experiments, the various feature selection techniques were studied. In the second phase of our study, all models were applied on the imbalance dataset and results were evaluated. SMOTE technique is used to balance the data and then the same models were applied on the balanced dataset and results were evaluated and compared. The best over-all classifier was Random Forest with accuracy almost 97%, precision 91% and recall as 98%

Churn prediction modeling comparison in the retail energy market

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceMachine Learning algorithms are used in diverse business cases and different markets. This project has the goal of applying different training models with the purpose of predicting customer churn in a retail energy provider. Following CRISP-DM methodology, the dataset was analyzed, prepared and results were evaluated in order to achieve the best method of forecasting the likelihood of churning in an existent customer base. That information is essential in company’s business planning to maintain and increase its portfolio

Unsupervised learning models-based CRM anomaly detection using GPU

Deep learning models have improved several business intelligence tools like Customer relationship Management(CRM) systems. However, those models have increased the need for advanced computational capacity and infrastructure. Modern accelerators are starting to have floating-point precision arithmetic problems generated by highly streamlined systems, powered by the need to process an ever-increasing volume of data and increasingly complex models to attend to the necessity to identify customer data that allow consolidating products or services. We focus on CRM anomalies detection using GPU(Graphics Processor Unit) because they are a relevant source of money drain for organizations and directly affect the relationship between clients and suppliers. Our results present the combination of deep learning models with a computational structure that could access by organizations, but with a combination that reduces the number of features that achieve answers to CRM system.#AnáliticaDeDatosLos modelos de aprendizaje profundo han mejorado varias herramientas de inteligencia empresarial, como los sistemas de gestión de relaciones con el cliente (CRM). Sin embargo, esos modelos han aumentado la necesidad de infraestructura y capacidad computacional avanzada. Los aceleradores modernos están comenzando a tener problemas aritméticos de precisión de punto flotante generados por sistemas altamente optimizados, impulsados ​​por la necesidad de procesar un volumen cada vez mayor de datos y modelos cada vez más complejos para atender la necesidad de identificar datos de clientes que permitan consolidar productos o servicios. . Nos enfocamos en la detección de anomalías de CRM utilizando GPU (Graphics Processor Unit) porque son una fuente relevante de drenaje de dinero para las organizaciones y afectan directamente la relación entre clientes y proveedores. Nuestros resultados presentan la combinación de modelos de aprendizaje profundo con una estructura computacional a la que podrían acceder las organizaciones, pero con una combinación que reduce la cantidad de funcionalidades que logran respuestas al sistema CRM

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business

The purpose of this thesis is to analyze the behavior of customers within the Online Food Delivery industry, through which it is proposed to develop a prediction model that allows detecting, based on valuable active customers, those who will leave the services of Alpha Corporation in the near future. Firstly, valuable customers are defined as those consumers who have made at least 8 orders in the last 12 months. In this way, considering the historical behavior of said users, as well as applying Feature Engineering techniques, a first approach is proposed based on the implementation of a Random Forest algorithm and, later, a boosting algorithm: XGBoost. Once the performance of each of the models developed is analyzed, and potential churners are identified, different marketing suggestions are proposed in order to retain said customers. Retention strategies will be based on how Alpha Corporation works, as well as on the output of the predictive model. Other development alternatives will also be discussed: a clustering model based on potential churners or an unstructured data model to analyze the emotions of those users according to the NPS surveys. The aim of these proposals is to complement the prediction to design more specific retention marketing strategies

Predicting Customer Retention of an App-Based Business Using Supervised Machine Learning

Identification of retainable customers is very essential for the functioning and growth of any business. An effective identification of retainable customers can help the business to identify the reasons of retention and plan their marketing strategies accordingly. This research is aimed at developing a machine learning model that can precisely predict the retainable customers from the total customer data of an e-learning business. Building predictive models that can efficiently classify imbalanced data is a major challenge in data mining and machine learning. Most of the machine learning algorithms deliver a suboptimal performance when introduced to an imbalanced dataset. A variety of algorithm level (cost sensitive learning, one class learning, ensemble methods ) and data level methods (sampling, feature selection) are widely used to address the class imbalance in the retention prediction problems. This research employs a quantitative and inductive approach to build a supervised machine learning model that addresses the class imbalance problem and efficiently predict the customer retention. The retention Precision is used as the evaluation metrics for this research. The research evaluates the performance of different sampling methods (Random Under – Sampling, Random Over – Sampling, SMOTE) on different single and ensemble machine learning models. The results show that Random Under-Sampling used along with XGBoost classifier yields the best precision in identifying the retention class. The best model evolved in the research was also used to predict retainable customers from the recent unknown customer data, and could attain a retention precision of 57.5%

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining