Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER
BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA
SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8

PROPOSAL
The modern consumer behavior perspective is just the
same as the traditional consumer behavior
perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has
been conducted and presented on the behavior of consumers and
how the data obtained is important in solving real-world
problems. In analyzing consumer behavior in this paper, we will
embrace data mining techniques. Each data mining technique
has its pros and cons. For this reason, we will choose the best
technique to mine our database. The main objective is
identifying psychological conditions that affect customer’s
behavior at the time of purchase and the key data mining tool
that is convenient for each method of purchase. Furthermore,
there is an association rule that is employed in customer mining
from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER
BEHAVIOURS.
Analyzing consumer behavior is important as the data
obtained is converted to a format that is statistical and a
technical technique is used to analyses the data (Stoll, 2018).
Business enterprises also use the knowledge of consumer
behavior in the following ways:
I. Determining the psychology of consumers in terms of their
feeling, reasoning, and thinking and how best they can choose
between the alternatives.
II. Businesses also determine how the business environment
affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the
time of purchasing their goods and services.

IV. Companies also find out how customer motivation affects
customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing
strategies based on the available data that they will
gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the
same as the traditional consumer behavior perspective. The
patterns used by consumers in the day to day lives are also
applicable in the online context. Koufaris (2002) in his article
argues that online consumer behaviors are similar to traditional
behaviors. However, online consumers have additional
advantages as besides being customers, they easily access the
information about the goods and services they want. The
contents of our datasets pertaining the consumer behaviors can
be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a
constant generator whom his or her data is treated in diverse
contexts as unstructured, contemporary and behavioral. The
volume of the customer data and how rapidly the data is
generated affects how the data is generated and interpreted. The
large volumes of generated data have caused data scientists to
look for a format of making the data easy to be interpreted by
common people (Roll, 2019). Large volumes of data are
complex and there is a need for sophisticated techniques to aid
data management and visualization. Unprecedented volumes of
data have caused data to be more complex. Big data analytics is
inevitable in the present world to aid in business decision
making.
Clickstream data is among the key types of consumer
behavioral data. This type of data is collected by the server
from the electronic record of the website (Nimalendran, 2017).
This type of data concerns the number of clicks that customers
make when visiting the website. This data gives an insight of
the navigated pages. Text data is the second format that caters
to the naturalist of the emails, webpages and other posts of
social media.

Using online consumer data is through path analysis. This
is the navigational path based on website insights. The data
analytics in this sense gives the follow-up events up to the point
the customer decides to buy the goods and services. The
prediction of the customers’ behavior aims, interests and
knowledge can be determined in this case. The ability of the
business to use customer data in making decisions is explored in
the discipline of customer analytics.
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA
SET.
In the implementation of the consumer behavior data set, we
embrace customer behavior modeling in coming up with the
mathematical representation of the common behaviors that we
observed from the groups of customers that we observed. These
representations are important in that it will give us a prediction
of how customer’s behavior will be given the same
circumstances. In the present competitive world, no business
can survive without predictive analytics. Since we are living in
a competitive world, the survival of the businesses will greatly
depend on predictive analytics. The main aim of business
analytics is to work on the profitability of the business.
ML application in data algorithms requires one to
understand the type of data he or she will be dealing with. If a
data scientist is a novice, there will be a high possibility that
the ML model will not succeed. Explanatory Data Analysis
(EDA) is mandatory and it is performed following three visual
methods. The three visual methods of EDA include text
analysis, volumetric analysis, and time pattern analysis. In
volumetric analysis, customer behavior is categorically done
based on the numerical features of data (Garvy, 2015). This

visual method is important in the analysis of bi-variate and
variate data. Another visual method is the time pattern analysis
which is based on the format of time and date. In principle, text
analysis is embraced in many companies as it incorporates all
text data characteristics. This text analysis is like N-gram and
word cloud.
CUSTOMER BEHAVIOR DATA MINING
TECHNIQUESAssociation Mining
There is a correlation amid the sets of data in this technique.
Catalog design and loss leader analysis are the results obtained
from the transactional records of this data
technique.Transaction study unit
Market basket analysis is what a researcher will get from the
association of rule mining. This technique is keen on the
customer habits of purchase depending on the available items of
purchase. This information is important to retailers as it will aid
in their ability to coming up with good marketing strategies. For
instance, a trip to the supermarket to buy greens will need that
the customer also buys onions and tomatoes as they will go
hand in hand. This helps retailers utilize their shelf space well
and hence improve their sales.CONCLUSION
In the current world, an ordinary shopper is viewed as a steady
generator whom their information is treated in differing settings
as unstructured, contemporary and conduct. The volume of the
client information and how quickly the information is produced
influences how the information is created and deciphered. The
huge volumes of produced information have made information
researchers search for an arrangement of making the
information simple to be deciphered by average folks. Huge
volumes of information are perplexing and there is a
requirement for modern strategies to help information the
executives and representation. Extraordinary volumes of

information have made information increasingly mind-boggling.
Enormous information investigation is unavoidable in the
current world to help in organizations dynamic
REFERENCES
Garvy, G. (2015): “Rivals and interlopers in the history of the
New York security market,” Journal of Political Economy,
52(2), 128-143.
George, T.J., G. Kaul and M. Nimalendran (2017): “Estimation
of bid-asks spreads and its components: A new approach,”
Review of Financial Studies, 4(4), 623-656.
Roll, R. (2019): “A simple implicit measure of the effective
bid-ask spread in an efficient market,” Journal of Finance,
39(4), 1127-1139.
Stoll, H.R. (2018): “Inferring the components of the bid-ask
spread: Theory and empirical tests,” Journal of Finance, 44(1),
115-134.
Project Description
CIS 4321 Spring 2020
Dr. Batarseh
In this project, you experience the full cycle of the data mining
process. Below, I explain the different stages of the
project.Project Objectives
At the conclusion of this project assignment, participants should
be able to:
· Write a project proposal
· Identify a dataset to mine
· Mine a dataset and write-up the insights gathered from the
results
Requirements
For the final project in CIS4321 , you are going to mine a
dataset and define a project scope, implementation and analysis.

The dataset should be interesting, non-trivial and should have at
least 6 attributes and on the order of 1000s (or more) instances.
Some examples include data related to business, consumer
behaviors, social-network information, etc. You could select a
business problem that can be addressed through data mining.
The following links are some sites to public datasets.
· www.data.gov
· www.kaggle.com/datasets
· www.archive.ics.uci.edu/ml/datasets.html
· www.aws.amazon.com/datasets
· www.data.worldbank.org
· www.pewinternet.org/datasets
· www.labrosa.ee.columbia.edu/millionsong
· www.sports-reference.com
· www.wunderground.com/history
· www.yelp.com/academic_dataset
· www.developer.bestbuy.com/apis
Project Proposal (Due April 20th)
Formally write up your proposed project. Your write-up should
address each below point individually, It should be single
spaced, grammatically correct, and submitted to Blackboard by
the deadline. Include in your project the following:
1. Project name (descriptive and concise).
2. Significance of the project
3. Dataset description
a. Describe the contents of the dataset.
b. Link to where it can be located
c. Dataset format
d. Provide a description of the attributes and target variable.
4. Implementation
a. What type of pre-processing, EDA and modeling you
anticipate using?
5. Results
a. Why are the results useful?
b. Who would be interested in the results?
Dataset Mining

Your project should deliver on the functionality described in
your project proposal. As part of this, you will need to perform
data preprocessing (as needed), exploratory analysis of the
dataset (including visualizations), modeling and testing and
evaluation. You should also consider feature selection to help
improve the predictive power (accuracy) of you approach.
Technical Report (Integrated in Jupyter Notebook).
You need to write a technical report describing your approach
and findings. Your report must be written in Jupyter Notebook
and interleaved with your python code. The report should be
organized, clear, concise and easy to understand and follow.
Your notebook should have the following sections at a minimum
(in the order given below):
1. Introduction: This section must briefly describe the dataset
you used and the data mining task you implemented. Briefly
describe your findings.
2. Data Analysis: This section must provide details about the
dataset. You must include:
a. Information about the dataset itself, e.g., the attributes and
attribute types, the number of instances, and the attribute being
used as the label.
b. Relevant summary statistics about the dataset.
c. Data visualizations highlighting important/interesting aspects
of your dataset. Visualizations may include frequency
distributions, comparisons of attributes (scatterplot, multiple
frequency diagrams), box and whisker plots, etc. The goal is not
to include all possible diagrams, but instead to select and
highlight diagrams that provide insight about the dataset itself.
d. Note that this section must describe the above (in paragraph
form) and not just provide diagrams and statistics. Also, each
figure included must have a figure caption (Figure number and
textual description) that is referenced from the text (e.g.,
“Figure 2 shows a frequency diagram for ...”). You should
provide you source code using Jupyter Notebook and files.
3. Modeling Results: This section should describe the modeling
approach you developed and its performance. Explain what

techniques you used, briefly how you designed and implemented
model, how you tested the predictive ability, and how well it
performs.
4. Conclusion: Provide a conclusion of your project, including a
short summary of the dataset you used and any of its inherent
challenges, the modeling approach you developed and any ideas
you have on ways to improve its performance
Project Submission
Submit your project to blackboard by the due date, no late
submissions will be accepted.
You should submit a well-documented Jupyter Notebook and
dataset files. Submit both .ipynb and .pdf files, name your files
First_Lastname_FinalProject.ipynb.
Grading Guidelines
This assignment is worth 100 points + 10 points bonus. Your
assignment will be evaluated based on a successful compilation
and adherence to the program requirements. We will grade
according to the following criteria:
· 15 pts for project proposal
· 50 pts for implementation
· 25 pts for relevance/originality of project
· 25 pts for technical rigor and complexity
· 35 pts for technical reporting in a Jupyter Notebook