Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed.

In todays competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and high dimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGN COREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pre-trained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal co learning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers.

In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. The longer a player is playing the game, the higher will be the chances that he or she will generate a revenue within the game. Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime. In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures. The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data.

Deep Learning is not a so new research area, but if we think about its applications with the development of computing power and data availability, one could say that this practice is in a sustainable growth route, since 2012. The applications are endless and very diverse, which can include, for instance, Automating Driving, Health Checks, Home System Devices like Alexa, or even to study the universe, in which a deep learning algorithm was used to get the first image of a black hole. Nonetheless, there are a lot of expectations involved in Deep Learning and we could say that there are some myths also. This article aims to show some deep learning techniques, presenting some code of a practical example, in a not-so-large dataset. We applied some deep learning techniques in a sample of 2,711 clients of an investment advisory company.

An artificial neural network is a computing system that is inspired by biological neural networks that constitute the human brain. ANNs are based on a collection of nodes or units which are are called neurons and they model after the neurons in a biological brain. An artificial neuron receives a signal and then processes it and pass the signal to other neurons connected to it. The signal at the connection is a real number and the output from the neuron is computed by some non-linear function of the sum of the inputs. The inputs are accompanied by weights assigned respectively, the weight of an input increases or decreases the strength of the signal at connection.

Customer churn is essential in business as getting new customers requires more efforts and investment. This business logic is also applicable for retaining other business stake holders like employees, suppliers, investors etc. Whether it is getting new clients, hiring new employees, sourcing new suppliers or getting new investors, they all involve additional costs and investments. This article is about training a deep neural network to predict purchase behavior of customers for an application selling digital books. The image on the left describes the architecture of the model.

It's a telecommunications company that provides home phone and internet services to residents in the USA. The company noticed that their customers have been churning for a while. And this has impacted their customer base and business revenue, hence they need a plan to retain their customers. What do they mean by Customer Churn or Churned Customers? People who stopped using their home phone and internet services are known as churned customers.

One of the most famous and useful case studies of churn prediction is in the telecom industry. It is important for telecom companies to analyze all relevant customer data and develop a robust and accurate Churn Prediction model to retain customers and to form strategies for reducing customer attrition rates. In this project, Telco Customer Churn Dataset which is available at Kaggle is used. Two numerical columns: 1. MonthlyCharges: The amount charged to the customer monthly 2. TotalCharges: The total amount charged to the customer Eighteen categorical columns: 1. CustomerID: Customer ID unique for each customer 2. gender: Whether the customer is a male or a female 3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) 4. Partner: Whether the customer has a partner or not (Yes, No) 5. Dependents: Whether the customer has dependents or not (Yes, No) 6. Tenure: Number of months the customer has stayed with the company 7. PhoneService: Whether the customer has a phone service or not (Yes, No) 8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service) 9. InternetService: Customer's internet service provider (DSL, Fiber optic, No) 10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service) 11.

Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an aggregation procedure of such sequential data. However, recurrent neural networks provide an alternative approach by which time-varying features can be readily used for modeling. This paper assesses the performance of neural networks for churn modeling using recency, frequency, and monetary value data from a financial services provider. Results show that RFM variables in combination with LSTM neural networks have larger top-decile lift and expected maximum profit metrics than regularized logistic regression models with commonly-used demographic variables. Moreover, we show that using the fitted probabilities from the LSTM as feature in the logistic regression increases the out-of-sample performance of the latter by 25 percent compared to a model with only static features.

Customer churn, also known as customer attrition, occurs when customers stop doing business with a company. The companies are interested in identifying segments of these customers because the price for acquiring a new customer is usually higher than retaining the old one. For example, if Netflix knew a segment of customers who were at risk of churning they could proactively engage them with special offers instead of simply losing them. In this post, we will create a simple customer churn prediction model using Telco Customer Churn dataset. We chose a decision tree to model churned customers, pandas for data crunching and matplotlib for visualizations.