Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).

A customer segmentation project can be approached in multiple ways. In this repository, we will explore advanced techniques for defining clusters and analyzing the results.

This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

Modern PHP 8.3+ ecommerce platform built on Symfony, Doctrine DBAL, Laminas, 100% vanilla JS. Drop-in replacement for Magento 1 projects with full compatibility. Complete toolchain: Composer/PHPStan plugins, language packs. Enterprise features: automated email marketing, customer segmentation, dynamic categories, passkey/2FA auth and so much more.

The objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.

Analysis for Customer Segmentation

Customer Segmentation using RFM Analysis

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Customer segmentation using k-means clustering in python

PyCon SG 2016 - Customer Segmentation in Python

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Customer & Purchase Analytics using Segmentation, Targeting, Positioning, Marketing Mix, Price Elasticity

Analyzing and transforming a UK-based retail dataset (2010-2011) into a customer-centric format for customer segmentation using K-means clustering. Implementing a personalized recommendation system to enhance marketing strategies and boost sales.

This project is based on Unsupervised Learning

Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment

Python Package for RFM Analysis and Customer Segmentation

Udacity Data Science Nanodegree Capstone

Analyse customer segmentation, sentiment on product review, and built a product recommender system

Customer Segmentation Analysis with RFM, using Python and Power BI.

Unsupervised Clustering on Online Retail Dataset

Quick EDA on a data set to determine what segments there are.

Machine Learning Case study on customer segmentation and prediction of groups.

Customer-base segmentation over e-commerce sales data

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.

Learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing customer transactions from an online retailer.

Customer Personality Analysis Using Clustering

RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.

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