Research on a small e-commerce clothing company called Natalie’s. We studied factors on which the yearly amount spent by consumers depend upon. We also developed a clustering model for the customer segmentation. Various regression models were also developed to predict the yearly amount spent.

1. An Empirical Study on Customer Consumption,
Loyalty and Retention on a B2C E-commerce site
Isaac Lin, Chun Keat Lum, Monika Mishra, Pratik Parmar, Anthony Martinez| Professor Ming Wang| California State University, Los Angeles
Problem / Question
To verify whether customer loyalty is important
for an e-commerce business to thrive. To
develop a model for customer segmentation and
prediction of the yearly amount spent.
Research Questions
• Which states out of the 50 US states account for the highest yearly
amount spent ?
• Is there any correlation between Yearly Amount spent with the
Average session length, Time on App, Time on Website and Length
of Membership ?
• What is the predicted value of the Yearly Amount Spent ?
• How can the customers be segmented for different promotional
strategies?
Project Overview
Our research is based on a dataset from a small e-commerce clothing
company called Natalie’s. We studied various factors on which the
yearly amount spent by consumers depend upon. It was found that
the amount spent is directly proportional to the length of membership.
For retaining a customer, their segmentation is important for targeted
marketing. We developed a clustering model for the same. The
business needs to be future-ready to change business policies by
predicting business performance. For that, we developed the
regression models to predict the yearly amount spent.
Variables / Research
Controlled variables
• Email
• Address
Independent variable
• Average Session
Length
• Time on App
• Time on Website
• Length of
Membership
Dependent variable
• Yearly Amount
Spent
Platform Specification
SAP BusinessObjects Predictive
Analytics
R Console
Version 3.2 R version – 3.6.0
Expert Analytics
Microsoft Azure Machine Learning
Studio
Databricks
10 GB Memory Apache Spark 2.4.0
1 Node Python Version 3
6 GB Memory, 0.88 cores
Procedure
•Dataset acquired
from the Kaggle
site.
•Address column
was split into
multiple columns to
derive state name
Step 1
Research
Questions were
developed.
Correlation
analysis was
performed
Step 2
Customer
segmentation was
done.
Regression model
s developed for
predicting yearly
amount spent
Step 3
Regression models
were compared.
Conclusions were
made.
Step 4
Data / Observations
• South Carolina spends the most $6820. It is then followed by
Delaware with a spending of $6844.98 and Missouri with a
spending of $6402.57.
• There is a strong correlation between Yearly Amount Spent and the
length of membership.
• There is a weak correlation of Yearly Amount Spent with the
Average Session Length, Time on App and the Time on Website.
• Linear Regression Model developed on Azure platform is the best
model to predict the Yearly Amount Spent by the consumers.
• R-K algorithm helped in the segmentation of consumers into five
groups for targeted promotions.
Results
Conclusion
• Yearly amount spend by a customer is directly proportional to the
length of membership.
• Customer retention plays an important part in contributing to
company’s revenue.
• Segmentation of customers can be done through the clustering
algorithm for targeted marketing.
• Prediction of Yearly Amount spent is important for the business to
thrive. This can be done through regression algorithm.
Works Cited
• Fernandes, L. (2013). Fraud in electronic payment transactions: threats
and countermeasures. Asia Pacific Journal of Marketing & Management
Review, 2(3), 23-32.
• Smith, B. (2002). The effectiveness of marketing strategy making
processes: A critical literature review and a research agenda. Journal of
Targeting, Measurement and Analysis for Marketing, 11, 273-290.
• Zhang, G., Chen, X., & Zhou, F. (2009). Research on the relationship
between customer value of e-business and customer retention: an
empirical study in china. International Conference on Industrial
Engineering and Engineering Management. 2202-2206.
2018 Stanford Global WiDS Conference, CSULA
Correlation between Yearly Amount Spent and Length of Membership
Actual vs Predicted Value of Yearly Amount Spent – Linear Regression