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.

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An Excel-based sales and customer analysis dashboard designed for a fashion e-commerce store. The project visualizes key business insights including monthly sales trends, product category performance, customer demographics, retention analysis, and regional market reach.

Introduction In ecommerce companies like online retails, customer segmentation is necessary in order to understand customers behaviors. It leverages aqcuired customer data like the one we have in our case, transactions data in order to divide customers into groups. Our goal in this Notebook is to cluster our customers to get insights in: Increasing revenue (Knowing customers who present most of our revenue) Increasing customer retention Discovering Trends and patterns Defining customers at risk We will do RFM Analysis as a first step and then combine RFM with predictive algorithms (k-means). RFM Analysis answers these questions: Who are our best customers? Who has the potential to be converted in more profitable customers? Which customers we must retain? Which group of customers is most likely to respond to our current campaign? More about RFM here.

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.

This project uses Python and Jupyter Notebook to perform time-based Cohorts Analysis to assess and compare retention, order items quantity and order revenue of different customer cohorts.

This repo is a code demo that implements a custom Customer Retention Analysis class with a number of helpful methods/functions to generate customer churn insights frequently used for marketing analytics to understand the growth and change of your customer base (new vs retained vs lost) .

This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention

CustomerExpenseChurn-py is a Python-based analytics tool that combines expense analysis, customer feedback sentiment, and machine-learning churn prediction to generate insights, visualizations, and retention recommendations from data.

Telecom Customer Churn Analysis using Power BI This project analyzes telecom customer data to identify key factors influencing customer churn. It includes interactive Power BI dashboards highlighting churn trends, customer behavior, and actionable insights to improve retention and business performance.

This repository contains a comprehensive analysis of customer behavior in an online retail setting using the RFM (Recency, Frequency, Monetary) model. The goal is to segment customers based on their purchasing patterns and provide actionable insights to improve marketing strategies and enhance customer retention.

Welcome to the new era. One of the biggest challenges when studying the technical skills of data science is understanding how those skills and concepts translate into real jobs, like growth marketing. The main idea is to demonstrate how with Python skills you can make the best marketing decisions based on data. In this project, through Python, using packages such as pandas, I perform an analysis of marketing campaigns using machine learning, taking into account the different metrics such as CTR, conversion rate, or retention rate of each social network, to learn how to analyze campaign performance, measure customer engagement, and predict customer churn, to improve company's marketing strategy.

customer retention analysis - python - data exploration, random forest & logistic regression

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

cohort analysis on customer retention rate of a product company

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.

Advanced Customer Behavior Prediction & Service Discontinuation Analysis | ML-driven customer retention modeling with original behavioral pattern recognition, ensemble algorithms, and comprehensive performance evaluation framework for business intelligence applications.

To analyze bank customer churn behavior, identify key attrition factors, and develop predictive models to forecast churn probabilities, aiding proactive customer retention strategies using advanced data analysis and machine learning techniques.

After Covid-19, with the help of advances in the technology online shopping have become a part of daily life and it is expected to grow more all around the world. Accordingly Customer behavior are becoming more and more complex with the passage of time. With increasing competitor in the market, Retailers tries their best to hold their customer because attracting new customers cost several times then retaining the existing customer. For this purpose, Retailer analysis their customers purchase so that they can provide better service and maximize their profit margins. In this work, EDA of e-Retail data has been performed, using RFM analysis to identify the categorical segmentation of customers and Time Series Analysis with ARIMA Model to identify trends and clustering and classification models are implemented to identify the customers who are likely to churn. Furthermore, will also analyze top factors that influence user retention.

Telecom Customer Churn Analysis Dashboard built in Power BI to identify key churn drivers and customer behavior patterns. The report analyzes service usage, contract types, payment methods, and demographics to highlight high-risk segments and support data-driven retention strategies.

INN Hotels faces high booking cancellations, impacting revenue. Using data analysis, key factors like lead time, booking channel, guest loyalty, and special requests were identified. A Decision Tree model with 88% accuracy predicts cancellations, helping the hotel optimize pricing, policies, and customer retention.

Cohort Analysis (Retention over User and Product Lifetime) & Customer Segmentation with RFM ( RFM is an acronym of recency, frequency and monetary)

Jupyter notebook for churn analysis on bank customer retention data

this project represents the analysis exploratory and the creation of machine learning model to solve the problem of churn classification and Customer retention in the field of telecommunication.

Using data analysis to predict if customers are likely to leave the bank, empowering proactive retention efforts.

This project predicts Telecom customer churn by combining customer data and emotional analysis from voice interactions.Our goal is to identify at-risk customers and enhance customer retention strategies by understanding both behavioural patterns and emotional tone.

This repository showcases a Python project using Machine learning Algorithms/ Models on Churn Analysis in the telecommunication industry. By leveraging Machine Learning techniques, I strive to help telecom providers reduce churn rates, improve customer retention, and make data-driven decisions.

We’ll use a telecommunications data for predicting customer churn. This is a historical customer data where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it’s less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company. This data set provides info to help you predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs. The data set includes information about: Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents

Dive into the world of customer retention with this GitHub repository, Utilizing the power of tools like Power BI and Python libraries such as Numpy, Seaborn, and Tidyverse, we explore the factors driving customer churn and pinpoint their impact areas.