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Business Intelligence for the Evaluation of
Customer Satisfaction in E-Commerce
Websites- A Case Study
Priyadarshini P. 1 & K. T. Veeramanju 2
1 Research Scholar, Institute of Computer Science and Information Science, Srinivas
University, Mangalore – 575001, Karnataka India,
ORCIDID: 0000-0003-4658-3742; Email: priyadarshini.pnair@gmail.com
2 Research Professor, Institute of Computer Science and Information Science, Srinivas
University, Mangalore – 575001, Karnataka India,
ORCIDID: 0000-0002-7869-3914; Email: veeramanju.icis@srinivasuniversity.edu.in
Area/Section: Computer Science.
Type of the Paper: Research Analysis.
Type of Review: Peer Reviewed as per |C|O|P|E| guidance.
Indexed in: OpenAIRE.
DOI: https://doi.org/10.5281/zenodo.7456233
Google Scholar Citation: IJMTS
International Journal of Management, Technology, and Social Sciences (IJMTS)
A Refereed International Journal of Srinivas University, India.
CrossRef DOI: https://doi.org/10.47992/IJMTS.2581.6012.0243
Received on: 21/11/2022
Published on: 19/12/2022
© With Authors.
This work is licensed under a Creative Commons Attribution-Non-Commercial 4.0
International License subject to proper citation to the publication source of the work.
Disclaimer: The scholarly papers as reviewed and published by the Srinivas Publications
(S.P.), India are the views and opinions of their respective authors and are not the views or
opinions of the SP. The SP disclaims of any harm or loss caused due to the published content
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How to Cite this Paper:
Priyadarshini, P., & Veeramanju, K. T., (2022). Business Intelligence for the Evaluation of
Customer Satisfaction in E-Commerce Websites- A Case Study. International Journal of
Management, Technology, and Social Sciences (IJMTS), 7(2), 660-668. DOI:
https://doi.org/10.5281/zenodo.7456233
International Journal of Management, Technology, and Social
Sciences (IJMTS), ISSN: 2581-6012, Vol. 7, No. 2, December 2022
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Business Intelligence for the Evaluation of Customer
Satisfaction in E-Commerce Websites- A Case Study
Priyadarshini P. 1 & K. T. Veeramanju 2
1 Research Scholar, Institute of Computer Science and Information Science, Srinivas
University, Mangalore – 575001, Karnataka India,
ORCIDID: 0000-0003-4658-3742; Email: priyadarshini.pnair@gmail.com
2 Research Professor, Institute of Computer Science and Information Science, Srinivas
University, Mangalore – 575001, Karnataka India,
ORCIDID: 0000-0002-7869-3914; Email: veeramanju.icis@srinivasuniversity.edu.in
ABSTRACT
Background/Purpose: Advancement of technology has been proven in every field. To establish
a good business or any organization is very crucial nowadays. Businesses are moving
tremendously and competitors also high in number. It is very important to take a good decision
based on reviews and feedback of customer, etc. By combining the strength of technology and
business knowledge, Business Intelligence allows you to make good decisions that are fully
informed and stay one step ahead of the competition. With the help of business intelligence
tools businesses can use their data in a better way. Business intelligence incorporates Data
mining, Data Analytic, Data Visualization and Machine learning to help organization for the
analysis of data. This article provides the analysis of Business intelligence and machine
learning techniques used in e-commerce website and ABCD framework to inspect the key
factors.
Objective: Analyses the business intelligence technology and sentiment analysis on E-
commerce website.
Design/Methodology/Approach: The information and details for this case study is obtained
from different scholarly articles published in various journals and company websites.
Findings/Result: The study of this paper delivers the importance of customer behavior and
how it helps in growth of the industry using machine learning and business intelligence.
Originality/Value/Novelty: The result of this paper gives an explanation of business
intelligence and machine learning approach for customer feedback in e-commerce companies
and advantages of e-commerce websites.
Paper Type: Case study paper to study the advantages of business intelligence and machine
learning for customer satisfaction.
Keywords: Business intelligence, Sentiment Analysis, ABCD framework, Customer, Machine
learning.
1. INTRODUCTION :
The term "Business Intelligence" (BI) was first used by Richard Millar Devens in the year 1865.
Business intelligence is a technology that is used to gather data and analyzing it and finally transform
it into useful information. After 1988 Business intelligence technology developed by introducing
simplified BI tools. BI tools were easier and efficient [1]. Business Intelligence technically referred to
as the process of extracting transforming and analyzing the data for decision making [2].
Earlier, businesses and government agencies could learn from the structured data that was collected
through their internal business systems or organization’s data. But in this generation the concept big
data, to capture new economic opportunities brought about by advanced big data known as business
intelligence and analytics (BI&A). The advantages of data analytics enable businesses to make precise
and fast decisions by improving their understanding of their customers, market’s trend, and various
products [3]. Companies or organization can achieve their profit by integrating two areas business
intelligence and customer demands. ‘Customer’ is the main centric for designing marketing strategies
for a company. In this digital era deeper understanding of customer interest, new trends and behavior is
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very essential. And also how effectively a product can be delivered to a customer in a very short period
are also matters. Analyze how real-time high performance analytics, in particular, are enabling new
marketing opportunities and improving marketing return on investment to determine why so many
businesses are unable to reap the anticipated rewards from interactive marketing [4]. Digital marketing
is more flexible and affordable compared to traditional marketing strategies. More important than what
you sell is how you sell it. There are countless digital marketing companies or E Commerce companies,
but the marketing strategies define how to improve the business.
2. RELATED RESEARCH WORK :
Table 1: Literature review on Business Intelligence listed on the below table
S. No
Field of Research
Focus
Outcome
Reference
1
Business
Intelligence for
online marketing
Focus on a solution
for BI in commercial
field
Utilizing administrative
techniques and particular
data analysis tools, it
produces results that are
invaluable for enhancing
managerial decision-
making.
Azvine B.
et al.
(2006)
[5]
2
Concept of Business
Intelligence
Focused on the
function and
necessity of real-time
BI for companies
Examined the ideas behind
business intelligence (BI),
its elements, and
advantages, technological
needs, creating and
implementing BI.
Ranjan T.
(2009)
[6]
3
Issues of current
business intelligence
Technology that will
help real-time
business intelligence
become a reality,
Detailed automated
analytics and Intelligence
data analysis
Chen H. et
al. (2012)
[7]
4
E commerce with
Business
Intelligence
New research
direction and propose
for an architecture to
combine e commerce
and BI
The suggested architecture
allows for the
transformation of
consumer-generated raw
data from online retailers.
Ferreira T.
et al.
(2017)
[8]
5
Business
Intelligence and
Analytics
How to solve data
related problems in
business
Businesses and
organizations acquire
important insights from the
structured data gathered by
various enterprise systems
Gheorghe
S. et al.
(2017)
[9]
6
Business
Intelligence and
digital marketing
Business
intelligence's
importance and value
to digital marketing.
Business intelligence has
been shown to pave the way
for increased revenue.
Bhosale S.
et al.
(2020)
[10]
7
Current state of
Industry 4.0
business intelligence
(BI) technology's
current state
Business Intelligence, Big
data and IoT assist business
with decision making and
prediction.
Javera
Romeo et
al. (2021)
[11]
8
Data Visualization
Framework
Focused on IoT based
Data Visualization to
analyze various data
sources for BI in
Business enterprises.
The improved performance
with a quicker response
time
Shao C. et
al. (2022)
[12]
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3. OBJECTIVE OF THE STUDY :
(1) An overview on top e-commerce industries.
(2) To study and analyze Business Intelligence and Customer behavior.
(3) To study the different sentiment analysis approach for customer feedback and review.
(4) To study the impact of business intelligence on ecommerce websites using ABCD framework.
4. METHODOLOGY :
The study is based on information collected from various recourses like scholarly articles, conference
papers, websites and articles.
5. TOP E-COMMERCE INDUSTRIES :
Amazon: Amazon began as an online bookseller. Amazon is the undisputed e-commerce leader and
keeps expanding and getting creative, with around $470 billion in global sales. A client-centered
strategy is necessary for long-term progress. Amazon is achieving through reviews and feedbacks.
Product recommendation based on the search makes the customer more attractive to the website.
Decision making is completely based on what customer might want. Amazon offers user reviews and
question and answer section. Because customer take decision based on positive comments and ratings
[13].
Flipkart: Flipkart was established in 2007 as an online bookstore. Later, Flipkart had 261 million unit
sales, 100,000+ merchants, and 54 million active users. The company introduced Cash on Delivery, it
is an option where the customer could make the payment while receiving the product [14]
Snapdeal: Snapdeal is an Indian company, established in the year 2010. Snapdeal is one such platform
that makes online shopping a boom with its exclusive deals, coupons and discounts for consumers.
Around 15 million products are available for delivery. Timely notification is available on Snapdeal so
that customers will not miss out on any offers.
Myntra: It is a website for fashion and lifestyle needs. It has the broadest selection of brands and goods
on its site. The company was established in the year 2007, worth around 7.1 million. By permitting the
autonomous design of its fashion products, Myntra seems to have gone one step further. They
introduced T-shirts with entirely artificially created patterns. Two Myntra brands that produce clothing
designs using artificial intelligence are Moda Rappido and Here and Now [15].
6. BUSINESS INTELLIGENCE AND CUSTOMER BEHAVIOUR :
Business intelligence is adopted by most of the business organization to improve their growth. Internet
is becoming more widely available. As a result, there is a huge hike in the e-commerce business in all
the fields like banking, health, textile industry, food and grocery, etc. The majority of the world
population prefers online shopping and other e-commerce applications for their daily chores. A huge
amount of data is accumulated due to various activities on internet. In the case of any business
organization, either online or traditional, analyzing existing data or utilizing it for future prediction is
possible. Since the amount of data generated is greater, it is not possible to analyze manually. Here it
shows the importance of business intelligence and other technologies. Predicting the customer behavior
and understanding them to improve business is the advantage of business intelligence. By integrating
artificial intelligence and machine learning algorithms, BI tools make explicit past customer purchases.
AI uses this data, together with other supplemental information, to predict client demands. With the aid
of business information, developers can research their competitors before making a choice. BI
encourages decisions to be fully connected with the preferences of the entire firm rather than limiting
the marketer to only considering the product at hand [10]. Business intelligence can assist a company
in better understanding its consumers so that it can better serve them. For example, it can help the
company increase sales to exist customers and reduce the number of customers that leave the company,
which would increase sales and revenue [16]. Business Intelligence is effective because of the following
components:
(1) Online Analytical Processing (OLAP): Companies regularly assemble information to carry out
various analyses. To get a precise and trustworthy comprehension of the many parts of the data,
they have a pressing need to gather all the data in one location. They can quickly obtain the findings
of multi-dimensional analysis with the aid of OLAP.
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(2) Real Time Business Intelligence: When business data and operations need to be sorted and
analyzed at the point of collection, real-time business intelligence (RTBI) is used.
(3) Data Warehouse: Data warehouses are the main data storage system used in business intelligence.
In order to inform everything from routine choices to organizational-wide switches in focus,
business intelligence relies on complicated searches and comparing multiple sets of data [17]. The
following figure shows the concept of Business Intelligence.
Fig. 1: Understanding the concept of BI [6].
Business intelligence's (BI) primary tenet is to recognize customer behavior and forecast their
purchasing patterns in order to enhance both the business and the environment [18]. All businesses
throughout history have been significantly impacted by changes in customer behavior. It is been
important for any organization to concentrate more on customer behavior and adapting to new business
strategies. Customers will always purchase products that are convenient and appealing to them.
Companies always show interest in their customers' needs and keep a record of all customers.
7. SENTIMENT ANALYSIS APPROACHES FOR CUSTOMER FEEDBACK AND REVIEW
:
The integration of business intelligence, data analytics, and machine learning is used to boost sales by
tracking the search history of customers. E-commerce companies are using advanced machine learning
to suggest products and online ads to their customers. Intelligence technologies are used to promote
new products and offers to the relevant customers.
Amazon and Flipkart are the leading e-commerce companies, which use the best technologies to predict
their customers' buying patterns. Both Amazon and Flipkart collect customer feedback on a regular
basis and analyze it to find the best marketing strategies to boost their business. Users can review the
products on the major e-commerce sites like Flipkart, Amazon, and many others. Consumers will
research products before making purchases in order to gain a better grasp of how they operate. The
interpretation will consist of a very straightforward product that has been polarized into positive,
neutral, and negative Product checks. We might do this experiment using machine learning techniques.
Consumers who are aware of a product reaction participate in research known as sentiment analysis.
One can learn about a person's sentiments or opinions about a thing or a product using the computational
technique known as sentiment analysis. By examining and evaluating customer reviews in online
commerce, sentiment analysis or opinion mining can be valuable for determining customer behavior
[19] Sentiment analysis can be used to classify the text as negative, positive and neutral. The sentiment
analysis framework consists of the following stages:
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Fig 2: Stages of sentiment Analysis [20]
7.1 DATA COLLECTION:
Amazon and Flipkart are the most popular e-commerce websites where people buy different categories
of products like electronics, clothes, home décor, etc. And they give feedbacks and reviews. A user
must read product reviews in order to choose a helpful product, comprehend the product, and make a
purchasing decision. When purchasing online, one of the first things a consumer does is read customer
reviews of the item. A user places more trust in the opinions and experiences of other individuals. A
consumer typically bases their decision to purchase or return a goods only on evaluations. Thus, it is
evident to demonstrate the significance of reviews [20]. The dataset contains reviews and product
information from Amazon and Flipkart. Which includes reviews like ratings, text, and votes. And product
data like details, category, price, brand, and image. Important information are extracted from the dataset.
7.2 PRE-PROCESSING:
The procedure of cleaning and preparing the text for classification is known as pre-processing the data.
Online writings typically have a lot of background noise and useless information such as HTML tags,
scripts, and commercials. Noise will be removed with the help of preprocessing, so that classification
procedure will be easier. Preprocessing includes online text cleaning, white space removal, extending
abbreviations, stemming, stop word removal and feature selection [21].
7.3 FEATURE EXTRACTION:
Feature extraction is the process of extracting information from unstructured data and eliminating all
irrelevant aspects [19]. Customers that shop online look for products based on specific attributes. For e-
tailers and marketplaces, the ability to quickly and easily identify the ideal product can be a key
difference in the market that influences sales and conversions. Various methods are used in feature
extraction. Feature pattern mining with association rule, finding similar patterns allows us to group items
with a high degree of correlation and quickly spot shared traits and correlations. Term Document Matrix,
in Term Document Matrix text data will be converted into mathematical matrices. In which the rows
represents the sentences and columns represents the word. From the matrix it is easy to get the occurrence
of each word. The importance of word can be selected with the help of TF-IDF. The TF-IDF assists in
counting the word's occurrences and concentrates on the term's relevance. Researchers can exclude terms
with low scores by isolating the most frequent words.
7.4 CLASSIFICATION ALGORITHMS :
LOGISTIC REGRESSION: Logistic regression is a machine learning algorithm, and it is under the
category of supervised learning techniques. An approach for predictive analysis used for categorization
issues is logistic regression. The idea of probability serves as its foundation. It can be used to classify
positive and negative comments from the feedback. From the input of term frequency integer vectors,
the function logistic regression () can be used to forecast the precision of sentiment categorization [22].
NAIVE BAYERS: It operates according to the Bayes theorem's definition of conditional probability.
Basically, the Bayes theorem determines the conditional probability of an event occurring based on
DATA
COLLECTION
PRE-PROCESSING
FEATURE
EXTRACTION
CLASSIFICATION
ANALYSIS OF
THE MODEL
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knowledge of possible conditions that may be connected to the event. Customer buying patterns can be
predicted with the help of Naïve bayes in e-commerce websites.
RANDOM FOREST: A popular ensemble of decision trees used in classification is called Random
Forest. Random Forest frequently displayed a strong performance that surpasses that of many other
classification methods. RF must build numerous separate decision trees to function as a decision tree
ensemble. Each tree is constructed with a bootstrap sample set of the initial training data in mind to
achieve that. This entails building a new set sampling from the previous set's replacement instances until
the size of the original training data is obtained. A random tree is created using one of the training data's
bootstrap sample sets [23].
8. THE IMPACT OF BUSINESS INTELLIGENCE ON E-COMMERCE WEBSITE USING
ABCD FRAMEWORK :
E-commerce has made doing business more affordable. The benefit of an e-commerce website is that it
can reach more customers, regardless of time or distance constraints. Better decision-making is the goal
of business intelligence. Business intelligence makes it possible for web designers and users to
understand the insights of e-commerce online apps. Any business model's ability to be successful
depends on how a company determines its place in the value chain and how it generates profits. As a
result, enterprises are currently challenged with finding viable business models that will increase
customer value and create money. Advantages, Benefits, Constraints, and Disadvantages are
abbreviated as ABCD [24]. The most effective way to learn new lessons needed to recognise,
comprehend, and resolve issues that arise during the process of managing and leading firms is said to
be through company analysis. ABCD analysis can be used as a research methodology in company
analysis [25]. ABCD frameworks can be used efficiently in various fields of research [26-29].
ADVANTAGES: There has been a surge in electronic commerce, sometimes known as e-commerce,
as a result of increased Internet usage. The challenge of such e-commerce companies is to find relevant
information from a huge amount of data. Leading e-commerce websites like Amazon and Flipkart are
accumulating large amounts of customer data like feedback, reviews, product descriptions, etc. Finding
a positive response is very essential to taking any decision. The combination of business intelligence
and machine learning techniques makes it easier. The combination of e-commerce and business
intelligence are a very potent combination that aids website owners in a variety of tasks, including
defining consumer-targeted marketing campaigns, identifying market trends, locating buying habits,
and predicting customer behavior [8].
BENEFITS: Financial stability of the industry can be improved by applying Business Intelligence
techniques. It is possible by tracking customers and their interest. Suggesting products based on
customer interest can be attained with the help of business intelligence. It is possible to find out which
product and service combinations clients are most likely to buy, and when. Additionally, it can enhance
customer satisfaction by enabling prompt and appropriate responses to client concerns and priorities
[6].
CONSTRAINTS: For many firms, the expense of setting up a sizable data warehouse to support a BI
system is still prohibitive [30]. Data utilization and capacity to store more data can be achieved by
integrating BI with the cloud storage concept.
DISADVANTAGES: Filtering data from a large dataset is difficult. Gathering data with respect to
time and resources is tedious. It is possible to manipulate data in a way that distorts the truth. The daily
operations of the modern organization involve the extraction of enormous amounts of data from a wide
range of databases and business applications, both on-premise and in the cloud. This makes data
integration extremely challenging for users.
9. CONCLUSION :
There are many e-commerce websites like Amazon, Flipkart, Snapdeal, etc. As the number of users
increased due to the wide availability of the internet, the companies started attracting their customers in
various ways. Business intelligence and machine learning make it easier. Almost all businesses use
business intelligence because of its decision-making capabilities. Growth of the industry is completely
based on what customers might want and their buying patterns.
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