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.

Three business analytics case studies were undertaken, encompassing market basket analysis, customer segmentation, and campaign management. SAS Visual Data Mining and Machine Learning on SAS Viya was utilized to explore data and provide insights. A comprehensive report addressing both technical and business aspects was delivered.

I have always been curious on how marketing campaigns play an essential part of how businesses promote their interests.

In any Retail and E-Commerce industry one of the key elements in shaping the business strategy of a firm would be the understanding of customer behaviour. More specifically, understanding their customers based on different business metrics: how much they spend (revenue), how often they spend (frequency), are they new or existing customer, what are their favourite products, etc. Such understanding would in turn help direct marketing, sales, account management and product teams to support better the customer and improve the product offering in turn improving the business. RFM model proves to be one such way of understanding the customers. RFM analysis is a data-driven customer behaviour segmentation technique where RFM stands for Recency, Frequency, and Monetary value. The idea is to segment customers based on their last purchase (Recency), how often they’ve purchased in the past (Frequency), and how much they have spent (Monetary). All three of these measures have proven to be effective predictors of a customer’s which is also been used to perform better campaign’s which in turn helps to increase the business ROI.

Customer segmentation, regression modeling, and campaign response analysis using Radiant and Tableau.

This project focuses on **customer behavior analysis** using SQL. We explore **segmentation, churn prediction, marketing campaign effectiveness, and sales performance** to help businesses make data-driven decisions.

RFM customer segmentation and marketing channel performance analysis using the UCI Online Retail II dataset. Segments 3,745 customers into 8 behavioural groups and identifies where marketing budget should go.

Customer segmentation and marketing campaign analysis using SQL

Customer-Segmentation-and-marketing-campaigns-analysis-main with Exploratory Data Analysis by using Power Bi & Python

The Marketing Department of a global retail store is running a customer appreciation campaign on the occasion of Christmas and New Year. They need to deploy appropriate marketing program for each customer group and exploit potential customers to become loyal customers. This Python project provided a segmentation analysis base on RFM Model.

Exploratory analysis, customer segmentation and cluster profile analysis based on responses received from a marketing campaign

Unsupervised learning model (K-Means) for customer segmentation using RFM analysis to drive targeted marketing campaigns.

Customer segmentation and marketing campaign analysis on 42k+ transactions. Identifies high-value customers and optimizes targeting using Python and Tableau.

Analyzing Marketing Analytics Data on Purchase Behavior and Campaign Responses - Customer Segmentation, Data Visualization, Regression Analysis, Random Forest

End-to-end Retail Sales & Customer Behavior Analysis using SQL on a star schema dataset. Includes revenue insights, customer segmentation, store performance, and campaign ROI analysis.

This project showcases **Marketing Campaign Analysis** using Power BI with a sample financial dataset. The dashboard provides insights into campaign performance, customer segmentation, and financial outcomes.

Comprehensive SQL analysis on Blinkit’s hyperlocal commerce data — including customer segmentation, RFM, churn analysis, delivery delays, and campaign performance, all built in PostgreSQL.

This is a PowerBI dashboard and DAX model for analyzing Mave Marketing's Campaign Performance. It Includes Customer Segmentation, Product spend analysis, Channel comparison, and key insights on campaign effectiveness and customer behavior.

Predictive modeling and customer segmentation analysis to optimize direct mailing campaigns for North-Point Software. Includes Classification, Regression, and Clustering models.

End-to-end marketing data science project with campaign analysis, customer segmentation, and conversion prediction, deployed using Docker, GitHub Actions, and EC2.

Recommendation Engine for Books E-Commerce Website. Built a customer segmentation k-means algorithm and recommended marketing campaigns for each customer segment. The analysis was used to retain existing high-ticket customers and increase sales for reluctant customers

This project performs a comprehensive marketing analysis for an e-commerce business using data-driven techniques. It involves customer segmentation, sales trend analysis, RFM analysis, and campaign performance evaluation to derive actionable insights that enhance marketing strategies and improve customer targeting.

Developed a credit card campaign optimization project using A/B testing and statistical analysis. Performed exploratory data analysis, customer segmentation, and experimental design to evaluate campaign effectiveness using hypothesis testing, confidence intervals, and structured data-driven decision-making approaches.

This project analyzes customer data to understand **purchasing behavior, campaign effectiveness, and market segmentation. The report covers **data preprocessing, exploratory data analysis (EDA), and insights visualization** to identify trends in customer demographics and spending habits.

The objective of customer segmentation analysis is to divide a customer base into distinct groups based on shared characteristics, enabling targeted marketing strategies. This approach helps businesses tailor products, services, and campaigns to meet specific needs, improving customer satisfaction and profitability.

In the retail industry, understanding diverse customer needs poses challenges that affect marketing campaign effectiveness. Developing a segmentation model using clustering has addressed issues in model development, cluster characteristic analysis, and strategy recommendations.

This project focuses on deriving marketing insights through customer segmentation, campaign analysis, and BI dashboarding. The project uses a real-world dataset from Kaggle I have conducted data mining, clustering, and business intelligence to support data-driven marketing strategies.

Customer segmentation is a pivotal task for business analytics. Customer segmentation is the process of splitting customers into different groups with similar characteristics for potential business value proposition. Many companies find that segmenting their customers enable them to communicate, engage with their customers more effectively. Future Bank is conducting an analysis on the existing customer profiles and the marketing campaign data to identify the target customers who are mostly likely to subscribe long-term deposits. As a member of the data analytics team, I am tasked to analyse historical data and develop predictive models for marketing purposes. I have used SAS Enterprise Miner and Rstudio to perform the analysis.

End-to-end EDA of banking data with SQL-Jupyter integration, data cleaning, and visualizations using pandas, seaborn, matplotlib, and plotly. Key insights on customer behavior and campaign response. Includes a Power BI dashboard for KPI tracking and segmentation analysis.

## Deep Lea Project Description Booking cancellations in hospitality industry have risen due to Online Travel Agencies (OTA) making it as the main selling point in their marketing campaign. The increase makes it harder for hotels to accurately forecast, leading to non-optimized occupancy and revenue lost. This causes direct financial consequences as well as operational problems. In order to solve this problem, we will use a real-life hotel booking dataset to create a customer segmentation analysis in order to gain insights about the customers (and hopefully reasons why they cancel their reservation). We will then build a classification model (including the newly created customer clusters) to predict whether or not a booking will be canceled with the highest accuracy possible. #### Dataset Description Our goal is to build a model able to classify a booking as canceled or not canceled. The dataset provides data from real bookings scheduled to arrive between July, 1st 2015 and August, 31st 2017 from two hotels in Portugal (a resort in the Algarve region (H1) and a hotel in the city of Lisbon (H2)). Booking data from both hotels share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. For a detailed list and description of those variables refer to the data dictionary.