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).

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

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.

Analysis for Customer Segmentation

Customer Segmentation using RFM Analysis

Python Package for RFM Analysis and Customer Segmentation

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

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

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.

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.

Using Kohonen self organising maps in R for customer segmentation and analysis.

This repo contains unsupervised models including the Latent Dirichlet Allocation (LDA) model applied to a corpus of research papers and a clustering analysis applied to customer segmentation.

Customer Segmentation, RFM analysis and price elasticity

Leveraging on Unsupervised Learning Techniques (K-Means and Hierarchical Clustering Implementation) to Perform Market Basket Analysis: Implementing Customer Segmentation Concepts to score a customer based on their behaviors and purchasing data

This code performs customer segmentation using RFM (Recency, Frequency, Monetary) analysis. It creates an RFM dataframe, determines optimal clusters with k-means, and orders clusters from worst to best. Customers are segmented into high, mid, and low value groups and visualized in a 3D plot for better marketing strategies.

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

By means of this project I am trying to create a value-based customer segmentation model using RFM(Recency, Frequency, Monetary) analysis in python using pandas, numpy and matplotlib

A collection of SQL-based data analytics projects demonstrating techniques for data extraction, transformation, analysis, and visualization. These projects showcase practical SQL queries and insights across various domains, including sales, customer segmentation, and employee attrition analysis.

Segmenting consumer based buying behavior and applying 80/20 rule to identify top customers/products/geographic locations driving 80% of total $ sales

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Data Analysis with Python - Customer Segmentation ( RFM Analysis) - Power BI Dashboard - Tableau Dashboard

I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predictive analysis). Used deep learning to enhance my model.

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.

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Repository for customer segmentation using KMeans clustering, utilizing techniques for data analysis and cluster identification. Includes dataset from Kaggle and open-source tools.

Customer Segmentation Recommendation is a Machine Learning based project which analysis user data and preferences to provide product recommendations on a micro-segment level and also helps the marketing team in making better marketing-business strategy by analysing its customers based on their buying behaviour.

This is a data analysis project with price prediction with Random Forest Algorithm of cars in Indian market and also consist of customer segmentation with the help of Clustering analysis with KNN algorithm

Advanced SQL analytics project extending prior EDA work. Includes change-over-time, cumulative trends, performance benchmarking, segmentation, part-to-whole analysis, and customer/product analytical reporting using window functions and real-world data warehouse logic.

Pickl.AI’s Datathon - 4

The objective is to predict insurance policy renewals for existing customers using predictive modeling. To segment existing customers for better targeting through focused marketing strategies & Minimize the fraudulent claims..