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Customer Segmentation with Machine Learning

on items that are frequently bought in bulk, such as pens and notepads for office supplies, is likely to make bulk buyers log in to the online store and place purchase orders, but it might not be attractive for luxury product buyers. By identifying customer segments based on their behavioral patterns and using customized marketing campaigns, you can optimize your marketing channels.

In this article, you’ll use an online retail dataset that contains all the transactions that occurred between Jan. 12th 2010 and Sep. 12th 2011 for a UK-based online retail store to build models for customer segmentation. This dataset is available in the UCI Machine Learning Repository and can be downloaded from 

http://archive.ics.uci.edu/ml/datasets/online+retail#. The full code for this data analysis can be found at https://github.com/yoonhwang/c-sharp-machine-learning/blob/master/ch.6/DataAnalyzer.cs.

Data analysis for the online retail dataset

It is now time to look into the dataset. You can follow http://archive.ics.uci.edu/ml/datasets/online+retail#

, click on the Data Folder link in the top-left corner, and download the Online Retail.xlsx file. You can save the file Learn how to build models for customer segmentation in this tutorial by Yoon Hyup Hwang, a seasoned data scientist with expertise in predictive modeling, machine learning, statistical analysis, and data engineering.

Whether you’re trying to send marketing emails to your customers or simply want to better understand your customers and their behaviors on your online store, you will want to analyze and identify different types and segments of your customers.

Depending on the behavioral patterns, your marketing campaigns should vary. For example, sending out emails with promotions on luxury items is likely to provoke luxury product buyers to log in to the online store and purchase certain items, but such an email campaign is not going to work well for bulk buyers.

 On the other hand, sending out emails with promotions as a CSV format and load it into a Deedle data frame.

Handling missing values

Since you’ll be aggregating the transaction data for each customer, you need to check whether there are any missing values in the Customer ID column. The following screenshot shows a few records with no Customer ID:

                                          

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