NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R > E-Commerce Customer Behavior Analysis

E-Commerce Customer Behavior Analysis

Oreste RUKUNDO
Posted on Dec 19, 2024

Introduction

This project aims to help businesses better understand what their customers want and what incentivizes them to place an order. By leveraging this information, business owners can offer the products, services, and associated incentives that will not only attract customers but also convert them into loyal buyers. Furthermore, the insights gained from this analysis are based on segmentation into different categories of customers, which considers purchasing patterns, membership type, and demographic information, such as gender and location. To achieve this, I used R for data cleaning and developing the visualizations, while R Shiny was utilized to deploy the analysis in an interactive and accessible format.

App link: https://ixud1l-rukundo-oreste.shinyapps.io/R_shinny_app1/ 

 

Dataset overview

This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Specifically, each entry corresponds to a unique customer and offers a detailed breakdown of their interactions and transactions. Moreover, the information is intended to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, thereby aiding businesses in making data-driven decisions to enhance the customer experience. In terms of structure, the dataset contains eleven columns: Customer ID, Gender, Age, City, Membership type, Total spend, Items purchased, Average rating, Discount applied, Days since last purchase, and Satisfaction level. Together, these columns provide a holistic overview of customer profiles and behaviors, enabling businesses to identify trends, tailor their strategies, and ultimately improve overall customer satisfaction.

Link: https://www.kaggle.com/datasets/uom190346a/e-commerce-customer-behavior-dataset

Fig.1 below is a sample of the dataset description.

Fig.1

Segmentation by Membership 

Memberships are classified by spending into Bronze, Silver, and Gold for this customer set.The higher the amount the customer spends,the higher the membership level,as identified by the difference in metal values, illustrated below in Fig. 2

Fig.2

The Impact of Discounts on Different Segments

Membership 

The average amount customers spend when a discount is applied indicates that Gold and Silver membersare significantly more likely to take advantage of discounts compared to Bronze members.

Given this trend, strategies can be developed specifically to enhance retention among these membership types.For example, businesses could design targeted campaigns or exclusive discount programs to maintain their loyalty.Moreover, understanding this behavior allows companies to allocate their resources more effectively towardinitiatives that drive engagement and long-term satisfaction.

Discount and Gender Fig.3

Fig.3

Regarding target demographics, the data indicates that females are more likely to take advantage of discounts than males.Therefore, understanding this trend can help businesses plan targeted marketing efforts more effectively.On the other hand, for male customers, businesses may need to explore alternative strategies to appeal tothose who require incentives beyond discounts to motivate their purchases.For example, offering loyalty rewards,exclusive products, or personalized services could be more impactful.Ultimately, such tailored approaches can help businesses better engage both demographics and drive overall customer satisfaction.

Mapping  City Differences 

Fig.4

City preference and performance: Customers in Houston represent the lowest average ratings, which may partly be attributed to the economic conditions and customer sentiments. Although the cost of living in Houston is lower than in New York, some Houston customers may still rate businesses poorly if they feel that the offers and services lack personalization or if their specific concerns are not being adequately addressed. To address this challenge, companies can win greater approval and secure future business in these regions by catering to local preferences and responding to regional economic fluctuations. In doing so, they can continue to deliver good value for their customers. Businesses should actively seek feedback from Houston customers to understand what matters most to them and what they expect in terms of value, quality, and service.

By aligning their products, services, and pricing with the specific values and expectations of Houston customers, businesses can foster customer satisfaction and achieve long-term success in the region. In contrast, the highest ratings come from customers in San Francisco, with New York customers close behind. This business can aim to further win over these customers by offering products and services tailored to these geographic regions or through exceptional customer service, as evidenced by the higher ratings it enjoys. Each city has its unique cultural vibe, which often includes a celebration of emerging trends that are common to both San Francisco and New York. Consequently, it is unsurprising that customers in these cities are among the most satisfied.

 

Future Work

Machine learning (ML) applications for e-commerce customer behavior offer a range of powerful tools for analyzing and enhancing customer interactions.(Learn more about how machine learning enhances e-commerce customer engagement.) In particular, ML has the potential to transform e-commerce by providing deeper insights into customer behavior,

enhancing personalization, and optimizing operational efficiencies. For instance, an ML-based classification tool can help businesses understand more precisely how memberships are classified into different types (categories) based on customer behavior. Additionally, it can reveal how different genders and membership types respond to various discount levels. Moreover, ML can analyze how purchasing preferences vary by city or region, thereby enabling businesses to tailor their strategies to meet specific customer needs and preferences more effectively.

 

Quick Links: 

GitHub Repository

Google slides

LinkedIn profile  

Attribution: Featured image on Freepik

About the Author:

Oreste RUKUNDO developed this R Shiny App as one of his projects for the New York City Data Science Academy program. Notably, R Shiny App offers a user-friendly interface with interactive data visualization and rapid prototyping. As a result, it is very well-suited for analyzing customer behavior in an e-commerce business. Furthermore, its interactivity and flexibility allow users to explore complex datasets with ease, thereby enabling deeper insights into customer preferences and trends.

 

About Author

Oreste RUKUNDO

A data scientist with a background in Mathematics and Statistics. Typically, I have a strong foundation in quantitative analysis, problem-solving, and statistical modeling. Skilled in using mathematical techniques and statistical methods to analyze complex datasets and extract meaningful...
View all posts by Oreste RUKUNDO >

Leave a Comment

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application