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.

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

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.

We are going to apply machine learning on unlabeled retail data to create at least 5 customer segments.

Customer Segmentation for an online retail store based on Recency - Frequency - Monetary value (RFM) model, using unsupervised K-means clustering

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.

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.

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

Mall Customers Clustering Project Unleash the power of K-means clustering to decode mall customer behavior! From data exploration to 3D visualizations, we navigate through demographics and spending patterns. Join our journey into customer segmentation for strategic insights. Let's redefine retail analytics! 🛍️📈

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.

Customer segmentation project using RFM and K-Means.

The objective of this project is to perform customer segmentation using unsupervised machine learning algorithms to identify distinct groups of customers based on their buying behavior.

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Problem Statement :This dataset is created for to segregate the regular customer to improvise our advertisement stratergy by Indian walmarts. The dataset contains various features that are Capture during the selling and buying sratergy in walmarts. The predicted output obtained from the above algorithm gives a fair idea about the who are the loyal customers.

Online Retail dataset customer segmentation with RFM

Customer segmentation (or market segmentation) are techniques to split customers into clusters based on similarities to get a sense of their behavior. In this notebook, we are going to analyze patterns in the Online Retail Data Set from the UCI Machine Learning Repository.

Retail customer segmentation using Machine Learning and Streamlit.

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.

🛍️ ARIA - World's First Multimodal Retail AI Platform powered by BigQuery AI. Real-time fraud detection, predictive analytics, customer segmentation & supply chain optimization.

End-to-end Power BI retail analytics dashboard delivering revenue, profitability, customer segmentation, and return risk insights using star schema modeling and DAX Measures

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Customer Segmentation and Targeting for Retail Industry

Implementation of a d3.js Visual Analytics dashboard for Sales Analysis and Customer Segmentation in Retail

AI Starter Kit for Customer Segmentation for Online Retail using Intel® Extension for Scikit-learn*

This project analyzes real-world e-commerce transaction data to uncover customer behavior patterns and segment users using RFM analysis and unsupervised machine learning (KMeans Clustering). It also provides product insights, sales trends, and business-ready summaries.

In this project, I performed an unsupervised learning clustering task using K-Means on unlabeled training data to segment and profile customers for a retail store. After segmenting the clients, a loyalty program called "Prosperous" was developed based on the profile of our best customers.

In this notebook, the purchases made by around 4000 customers in a period of one year (12/02/2010 - 12/09/2012) is analysed. The customers are further segregated into segments based on Recency, Frequency and Monetary Value (RFM) parameters which can be used to perform target marketing

I used the online retail dataset from the UCI Machine Learning Repository for exploratory data analysis and customer segmentation using RFM Analysis K-Means Clustering.

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