Customer Segmentation Anaylsis
Identifies the parts of the Germany population that best describe the core customer base of the Arvato company. Uses a supervised model to predict which individuals are most likely to convert into becoming customers for the company.
Customer Segmentation Report for Arvato Financial Services. Analyzing demographics data for customers of a mail-order sales company in Germany and identifying most suitable parts of general population whom are most likely to be converted to customers through marketing campaigns.
Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
This project shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation and DBSCAN.
Conducted customer sales segmentation and affinity analysis on chip sales to identify groups to target for advertisements and promotions.
This project develops a customer segmentation process related to a mail-order company in Germany.
Customer segmentation in Instacart. K-means, RFM Analysis.
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.
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.
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 is a project for the course "Machine Learning" - Master's degree in Data Science, University Milano-Bicocca
It is highly related to the Customer Segmentation problem, so with RFM Analysis itself as well
This notebook provides some skills to perform financial analysis on economical data.
This is a Customer Segmentation model made in Python
Data Science - Capstone Project
A dataset of Customer Profile going into a Mall Reference: https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python
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. Futu…
The purpose of this project was to perform customer segmentation on mall customers using sklearn Kmeans algorithm. Exploratory data analysis was first performed on the dataset to understand the data. Silhouette analysis was then used to determine the best number of clusters using age, annual income and spending score assigned to customers based …
Customer Segmentaton using RFM analysis
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