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

Customer Segmentation using RFM Analysis

Python Package for RFM Analysis and Customer Segmentation

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

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.

Customer Segmentation, RFM analysis and price elasticity

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

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

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.

Examining the purchasing habits of customers and segmenting according to these habits.

This project focuses on customer segmentation using RFM (Recency, Frequency, Monetary) analysis, a proven method for identifying and categorizing customers based on their purchasing behavior.

Customer Segmentation using RFM analysis

Marketing analysis using RFM analaysis, unsupervised clustering for automatic customer segmentation and association rules

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 promotion.

RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behaviour based customer segmentation. It groups customers based on their transaction history in other terms– how recently (R), how often (F) and how much (M) did they buy.

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.

Data science project using the UCL Online Retail Dataset to perform customer segmentation based on spending behavior. Includes RFM analysis, K-Means clustering, customer profiling, and actionable business insights with visualizations.

I explored a sales dataset and generated various analytics and insights from customers' past purchase behavior. I used SQL to analyze sales revenue and create a customer segmentation analysis using the RFM technique.

Customer Segmentation based on RFM using K-Means & Hierarchical Clustering

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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.

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

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This project uses RFM (Recency, Frequency, and Monetary) segmentation to analyze customer behavior and provide insights for targeted marketing campaigns. By classifying customers based on their purchasing patterns, strategies can be tailored to improve customer retention, drive growth, and maximize the lifetime value of each customer.

Segmentation of customers in online retail databases using Python, including RFM analysis and clustering.

A production-scale customer segmentation system using RFM Analysis implemented in PostgreSQL and Python. The project analyzes 151K+ e-commerce records to segment 5,000 customers into 8 behavioral groups, enabling data-driven marketing strategies and identifying high-value and churn-risk customers.