Predicting which set of the customers are gong to churn out from the organization by looking into some of the important attributes and applying Machine Learning and Deep Learning on it.

Customer churn Modelling

Using data: Customer's invoice file. Introductions: Customer Lifetime Value(CLTV) "Customer Lifetime Value is a monetary value that represents the amount of revenue or profit a customer will give the company over the period of the relationship". CLTV demonstrates the implications of acquiring long-term customers compare to short-term customers. Customer lifetime value (CLV) can help you to answers the most important questions about sales to every company: How to Identify the most profitable customers? How can a company offer the best product and make the most money? How to segment profitable customers? How much budget need to spend to acquire customers? CLTV indicates the total revenue from the customer during the entire relationship. CLTV helps companies to focus on those potential customers who can bring in more revenue in the future. CLTV = ((Average Order Value x Purchase Frequency)/Churn Rate) x Profit margin. Please check the below step for how to calculate CLTV. Algorithm: Step1: Calculate CLTV. Calculate the average order value of customers: Average order value = Total money spent / total number of transactions Calculate Purchase Frequency: Purchase Frequency = Total Number of Orders / Total Number of Customers Calculate Repeat rate and Churn rate: Repeat rate = How many customers have numbers of transactions more than one / total numbers of customers Churn rate = 1 - repeat rate Calculate the profit margin: Profit margin is the commonly used profitability ratio. It represents how much percentage of total sales has earned as the gain. Let's assume our business has approx 5% profit on the total sale. Profit margin = Total money spent on each customer * 0.05 Calculate customer lifetime value: Customer value = (Average Order Value * Purchase Frequency) / Churn rate Customer lifetime value = Customer value * Profit margin Step2: Predictive modelling. Build a regression model for existing customers. Take recent six-month data as independent variables and total revenue over existing time( here taking 2 years) as a dependent variable and build a regression model on this data. Pros and Cons of CLTV: CLTV helps you to design an effective business plan and also provide a chance to scale your business. CLTV draw meaningful customer segments these segment can help you to identify the needs of the different-different segment. Customer Lifetime Value is a tool, not a strategy. CLTV can figure out the most profitable customers, but how you are going to make a profit from them, it depends on your strategy. Generally, CLTV models are confused and misused. Obsession with CLTV may create blinders. Companies only focus on finding the best customer group and focusing on them and repeat the business, but it’s also important to give attention to other customers.

Companies use different marketing strategies for different customer segments. In order to do so companies use marketing analytics for Analysing Business Metrics, Customer Segmentation, Customer Lifetime Value Prediction, Churn Prediction, Predicting Next Purchase Day, Predicting Sales, Market Response Prediction, Uplift Modelling & A/B Testing Design and Execution.

Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay

Analysis on bank account data to predict customer churn

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Perbandingan kinerja algoritma machine learning pada data churn modelling

This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, by applying the below steps of a Data Science Project Life-Cycle 1. Data Exploration, Analysis and Visualisations 2. Data Pre-processing 3. Data Preparation for the Modelling 4. Model Training 5. Model Validation 6. Optimized Model Selection based on Various Performance Metrics 7. Deploying the Best Optimized Model into Unseen Test Data 8. Evaluating the Optimized Model’s Performance Metrics The business case of determining the churn status of bank customers are explored, trained and validated on 7 different classification algorithms/models as listed below and the best optimized model is selected based on the accuracy metrics. 1. Decision Tree Classifier - CART (Classification and Regression Tree) Algorithm 2. Decision Tree Classifier - IDE (Iterative Dichotomiser) Algorithm 3. Ensemble Random Forest Classifier Algorithm 4. Ensemble Adaptive Boosting Classifier Algorithm 5. Ensemble Hist Gradient Boosting Classifier Algorithm 6. Ensemble Extreme Gradient Boosting (XGBoost) Classifier Algorithm 7. Support Vector Machine (SVM) Classifier Algorithm

Churn Modelling with Bank Customer Prediction using ANN: Utilizing Artificial Neural Networks for predicting customer churn in banking scenarios.

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Artificial Neural Networks

Coursework project implementing ANN models for datasets like Breast Cancer, Car Purchasing, Churn Modelling, and Iris.

Customer Churn Modelling

Churn modelling for bank customers using Artificial Neural Network

- The project is based on a bank dataset where we analyzed each feature. To understand why customers are leaving.

Churn prediction using ANN

Churn’s prediction could be a great asset in the business strategy for retention applying before the exit of customers.

I built an Artificial Neural Network in predicting churn rate in a banking system. Keras, Scikit learn, Pandas, and Numpy libraries were used.

Artificial Neural Network (ChurnModeling)

Artificial Neural Network (ChurnModeling)

A churn model is a mathematical representation of how churn impacts your business. Churn calculations are built on existing data (the number of customers who left your service during a given time period). A predictive churn model extrapolates on this data to show future potential churn rates.

Predictive Analytics : Project on customer data from a store that answers : 1.Segmentation - Can we find the Holiday Shopper or Discount Freak clusters among customers using latent class or k-means analysis? 2.Price Sensitivity - What effect does increasing price have on the revenue generated by a segment? 3.Market Basket - Which products can be sold together? 4.Churn - When will a customer leave us? 5.Survival Modelling - Can we predict the lifetime value of a customer? and much more using predictive analytics in SAS

A business intelligence ANN based model made for Churn-Modelling

Customer Churn Modelling using Multi-Layer Perceptron

Churn_Modelling Using Deep Learning (Implemented ANN)

Churn Modelling using XGBoost

Churn Modelling using ANN

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