In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

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A Python-based project for analyzing customer churn using data visualization and machine learning models to predict churn probability. Employs libraries like Pandas, Scikit-learn, and Matplotlib for data preprocessing, model training, and insightful visualizations.

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

A detailed customer churn analysis for Telco Communication (Kaggle Dataset). IN PROGRESS (5/28)

Call Detail Record (CDR) is the information captured by the telecom companies during the Call, SMS and Internet activity. These information’s provides insights about the customer needs when it is used with customer demographics. Most of the telecom companies using call detail record information in the fraud detection by clustering the user profiles, Customer churn by usage activity and targeting the profitable customers by using RFM analysis.

A Python project analyzing customer churn using Exploratory Data Analysis (EDA). Insights into demographic, behavioral, and financial factors influencing churn using real-world bank dataset.

Telecom Customer Churn Analysis with Python

Customer Churn Analysis Experiments using Classical ML algorithms and Deep Neural Network

Application of K-means clustering. Prediction of customer churn using Multi-layer Perceptron ANN, Logistic Regression, SVM-RBF and Random Forest Classifier.

A sample Power BI report showcasing cohort analysis and customer churn in a retail setting.

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Causal Analysis of Customer Churn using DeepLearning

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This interactive web application leverages machine learning to predict whether a telecom customer is likely to churn. Users can input customer details for real-time predictions or upload a CSV file for batch analysis.

Implementation of Decision Tree Classifier, Esemble Learning, Association Rule Mining and Clustering models(Kmodes & Kprototypes) for Customer attrition analysis of telecommunication company to identify the cause and conditions of the churn.

Use Multiple Linear Regression, Python, Pandas, and Matplotlib to analyze the lifetime value and the key factors of the ‘Telco Customer Churn’ dataset.

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

Predict and prevent customer churn in the telecom industry with data-driven insights. This project explores customer behavior, builds predictive models, and offers recommendations to reduce attrition rates. Explore the code for analysis, model building, and more.

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Analyze customer churn data to identify trends and build predictive insights using Python.

Predict the customers who are likely to churn for an European bank using various models- KNN, logistic regression, decision tree and random forest

این پروژه به تحلیل داده‌های مرتبط با مشتریان بانکی می‌پردازد تا دلایل اصلی ترک آن‌ها را شناسایی کند. داده‌ها بر اساس سند نیازمندی‌های کسب‌وکار ارائه شده و از یک داشبورد ساخته‌شده با ابزار Power BI برای نمایش و تحلیل بصری استفاده شده است.

Completed an analysis of the "Customer 100000" dataset to explore customer profiles and subscription behaviors. Used Python for data cleaning, visualization, and machine learning to segment customers, predict churn, and provide insights for targeted marketing and retention strategies.

Predicting customer churn using scikit-learn