Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.

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.

The motive of the project is to predict the Customer LifeTime Value of a Four Wheeler Insurance Company and it is implemented by satisfying all MLR Assumptions. All the basic Exploratory Data Analysis and Data preprocessing, end to end Data Science life cycle has been implemented in this project.

Customer lifetime value analysis in python. Using Beta Geometric Negative Binomial Distribution and Gamma-Gamma methods

Customer Lifetime Value, Returns Predictions, Recommender system and sales analysis on UC Irvine online sales dataset.

Marketing analytics with Python, churn prediction, AB testing, RFMT segmentation, cohort analysis, customer lifetime value

Clustering and predicting customer lifetime value with machine learning and RFM analysis.

Customers knowledge, supply chain movement and sales forecasting, Customer Lifetime value, churn and survival analysis

Analysis of customer Lifetime Value from transaction level data. This analysis helps in targeted marketing by identification of high value customers

Analysis and Prediction of Customer Lifetime Value using R.The insights were then compiled into a report using R markdown.

This project analyzes online retail transaction data to identify distinct customer segments using RFM (Recency, Frequency, Monetary) analysis and calculates Customer Lifetime Value (CLV) using Predictive CLV models.

Explore the world of data-driven customer analysis and lifetime value estimation. This project dives into customer segmentation, geographic analysis, time series insights, stock trends, and product descriptions. Join us on our journey of data exploration and optimization.

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Python scripts for Customer Lifetime Value (CLTV) calculation and segmentation for retail analytics.

Customer Lifetime Value, Churn Rate and Survival Analysis

Customer lifetime value and market basket analysis for Arc'

This project predicts Customer Lifetime Value (CLV) for e-commerce. It aims at forecasting the revenue a business can expect from a customer over time. I did an explatory analysis. From Linear Regression to Neural Networks, explore how different models perform in predicting CLV.

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

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Customer Segmentation with RFM Analysis and Customer Lifetime Value Prediction (with BG-NBD and Gamma-Gamma Model)

Amazon Customer Lifetime Value (CLV) Analysis This project demonstrates an end-to-end analytics workflow for analyzing Customer Lifetime Value (CLV) using: Python – MySQL. Power BI . Azure (Azurite)

Data analysis project identifying key drivers for customer retention and lifetime value (LTV) in e-commerce using Python and SQL.

Predicting Customer Lifetime Value (CLV) using machine learning techniques based on purchase history, frequency, recency, and demographics. Includes customer segmentation using RFM analysis and clustering.

In this project, I employed survival analysis models to examine how the probability of customer churn evolves over time and to calculate customer lifetime value (LTV). Additionally, I implemented a Random Forest model to predict whether a customer is likely to churn.

This is my Dissertation that I completed as part of my MSc Statistics in UCD 2019. I completed my Dissertation with Idiro Analytics and used their data to develop a probabilistic model to predict the customer lifetime value of the customers of their largest client. I proposed this research project myself and completed all research and analysis over a 9-month period. I was awarded an A grade for the project and Idiro Analytics have implemented the model to identify the highest value customers and to predict which customers are at risk of churning.

The report is about evaluate and predict customer’s lifetime value and their reaction to renewal insurance offer from company when the current contracts expire based on their demographics and buying behaviors. This dataset was collected from IBM Watson analytics that published on marketing website. The dataset has 9134 customer records and 24 variables which contains customers personal information, auto information, aggregate information about their behavior since policy inception, and the outcome variables Customer Lifetime Value and Response Yes/No based on their acceptance or not. All customers were engaging auto insurance with company and their policies were going to expire between Jan 1 to Feb 28, 2011. Using Watson Analytics data, we can analyze the most profitable customers and develop targeted actions for retention programs. Based on all that information, the objective of this project is to figure which factors affect customers’ value and their decisions on accepting renewal offer, from that determine relevant valuable customers and build appropriate marketing campaign. This report includes data exploration analysis, data cleaning, building and selecting model, model predictive performance, and recommendations from results.

Gain the Python skills you need to make better data-driven marketing decisions. In this track, you’ll learn how to analyze campaign performance, measure customer engagement, and predict customer churn. Working with real-world data, including retail transactions, you'll discover how to analyze social media data, extract insights from text data, and gain market basket analysis skills that will help you better understand your customers. You’ll also use statistical models and machine learning to forecast customer lifetime value. Through hands-on activities, you’ll use popular packages such as pandas, Matplotlib, tweepy, NLTK, seaborn, NumPy, SciPy, and scikit-learn to help you improve your company’s marketing strategy. By the end of the track, you'll be ready to navigate the world of marketing using Python.

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