Big Data Analytics and Its Applications in E-Commerce

This research paper examines what is meant by Big Data in business operations. The study examines Analytics of Big Data along with its uses within e-commerce. The presentation addresses concerns in Big Data regarding e-commerce along with how e-commerce businesses can utilize Big Data in efficient manner.

Running Head: BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 1
Big Data Analytics and Its Applications in E-Commerce
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BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 2
Abstract
The application of Big Data as well as for analytic is assisting e-commerce operation stay
a step in front of other businesses in the trade. Big data in most instances is the massive
gathering of information that a specified organization can utilize in determining the appropriate
products, cost, and ads that are appropriate to expand their profits. Besides, Big Data is just a
gathering of different sets of data that are tough to process through technique of conventional in
computing. Big data in most business operations is not merely data, but it has changed to be
entire theme that comprise of a variety of devices, frameworks, and procedures. Big Data refers
to the idea of using complex sets of data to drive focus, decision-making process, and direction
within the organization. Such operations are attained by executing applicable approaches for
aiming the precise and deep perceptive of skills obtained through analyzing the data of an
organization. Therefore, this research paper examines what is meant by Big Data in business
operations. The study examines Analytics of Big Data along with its uses within e-commerce.
The presentation addresses concerns in Big Data regarding e-commerce along with how e-
commerce businesses can utilize Big Data in efficient manner. Besides, this report illustrates the
impression of exclusive elements that distinguish Big Data from ancient sets of data. Usage of
analytics of Big Data within e-commerce alongside several tools that create examination of client
information possible is examined. In conclusion, this research paper will focus on the
identification of different challenges these vendors of e-commerce encounter while executing
Big Data Analytics.
Table of Contents

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 3
Abstract............................................................................................................................................2
Introduction......................................................................................................................................3
Definition of Big Data and its Analytics.........................................................................................4
Analytics of Big data within an e-commerce setting.......................................................................7
Techniques of Analytic of Big Data along with its use within e-commerce...................................7
Increase shopper examination......................................................................................................8
Improving customer service.........................................................................................................9
Easier as well as protect payments.............................................................................................10
Social Media Analytic alongside E-commerce..............................................................................10
Conclusion.....................................................................................................................................11
References......................................................................................................................................12
Introduction
Over last few decades, capacity of international society to swap as well as general
information on business operations has increased. The increment had been reported to change

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 4
from 0.3 Exabyte in early days of 1986 (twenty percent digitized) to sixty-five Extra bytes in
2007 that stand to be 99.9 percent (Zeide, 2017). For example, a company such as Wal-Mart and
its transactional database were approximated to comprise of at least 2.5 petabytes of data linked
to operations of customers. Besides, advancement in data and their usage has been fed by the
presence of affordable computing techniques along with ubiquity of the internet. Presently, every
business operation in different e-commerce segment is achieved electronically. The presence of
vital data allows individuals to exchange data over the internet as well as take part in buying and
promotion through internet (Saeed, 2018). Moreover, vendors of e-commerce have taken merit
of use of online websites to market products along with services. The-commerce uses data to
improve its revenue and understanding of brand. Besides, utilization of big data in operations of
businesses through societal media platforms has absolutely affected e-commerce in relation to
revenue along with brand appreciation.
With presence of the advent of analytics of Big Data, there is the presence of more
notified or information-driven approaches for businesses to interact with the client. Hence,
vendors of e-commerce can examine great data or Big Data produced from interchange of
electronic data to attain an appropriate understanding of the behavior of clients (Kirkpatrick,
2013). Such exclusive insight can be used to enhance client service, offer an efficient guide to
business approach, and offer democratized operations to different clients. Several big retailers
understand worth of big data and provide the client with many choices when conducting
shopping on their sites (Hare, 2014). All such penchants are all produced from Big Data.

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Definition of Big Data and its Analytics
In modern era, there lacks a unified description for ‘Big Data.’ the most conventional
description of Big Data in a wider range is in terms of three traits. These characteristics comprise
of volume, variety, and velocity also known as 3 V’s (Cercone, 2015). For instance, the first V
which is Variety is a term that relates to the varied nature that comprises of both sets of data that
are structured as well as unstructured in business operations. The second V which is Velocity
describes the speed at which information is confined during business operations (Hare, 2014).
Additionally, Volume describes the data range found in Exabyte, Petabytes, together with
Terabytes.
Big Data performs play an essential purpose if operation of business is prepared online to
achieve different demands of clients and their penchant. However, big data is not just a phrase
that stands out as data volume, but it also describes the velocity of information (Kirkpatrick,
2013). The volume of Big Data is in the range of various Terabytes to Petabytes. The volume of
such data presents the most immediate challenge to the conventional structure of IT.
Additionally, in e-commerce, big data often refers to the sector that treats ways to examine,
analyze, systematically extract information from or otherwise deal with setoff different data that
are either too complex or large. All such data are dealt with y traditional application software
responsible for the processing of data (Saeed, 2018). Furthermore, data with many cases or rows
tend to provide greater statistical authority, but data with higher complexity may lead to a higher
rate of false discovery.
In e-commerce, Big Data consists of two distinct categories of important data. These two
categories comprise of structured together with unstructured information. Structured data in e-
commerce remain to be the regular information that comprises of names and addresses of the

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 6
business sector. On the other side, unstructured data is always gathered from different places that
comprise of platforms of social media (Sangeetha &Kannimuthu, 2016). Apart from social media
other places where unstructured bi data are collected from in e-commerce comprise of clicks,
tweets, and videos posted within online sites that relate to societal media. All the data obtained in
the above different places are what is important to e-commerce operations that can use the
services of customers. However, due to different characteristics of Big Data in expression of V’s,
it is nearly impossible to efficiently manage as well as analyze Big Data by use of the different
traditional database (Verma, 2017). Nevertheless, using special devices together with
technologies that comprise of Hadoop Distributed file system, big data can be managed in
efficient way. Big Data can efficiently be capable to manage different data. For instance, its
management process can comprise of process, storage, real-time analysis of different operations
in the business. Furthermore, when algorithms of mining of special data are initiated to analytical
structure of Big Data, an individual involved in e-commerce businesses can then be able to
derive insight from data.

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Figure 1 - Structure of Big Data as presented by Verma (2017)
Analytics of Big data within an e-commerce setting
Most e-commerce corporations are some of the first organizations of Big Data Analytics
implementers due to their desire to keep being on summit of their business operations. In most
instances, e-commerce businesses, damage Big Data that are categorized as structured along with
unstructured information (Zheng, 2015). Besides, structured data concentrates on demographic
information that comprises of name, address, sex, date of birth, age, and choices. On the other
side, unstructured Analytics of Big Data utilized by e-commerce businesses comprise of likes,
clicks, tweets, links, and voices. In analytic setting of Big Data, major contest is to focus on
dealing with both kinds of information to produce meaningful imminent to improve interactions.
For instance, Al-Sakran (2014) recorded that description of Big Data incorporates several scopes
in business actions. Such dimensions within e-commerce comprise of better extent of data, a

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 8
fresh set of information and examination, concurrent data, data driven by new technology, social
media data, and a large amount of data.
Techniques of Analytic of Big Data along with its use within e-commerce
There are several ways on how e-commerce uses Big Data in its operations. Analytics of
Big Data is utilized by e-commerce to develop better customer understanding in business
operations. The customer in e-commerce business has to emerge to expect better action from e-
commerce operations and this data can be used to maintain consumers contented (Hussain &
Roy, 2016). Besides, prognostic analytics of Big Data is utilized by e-commerce to forecast what
targeted customers will buy during operations. For instance, a company such as Amazon and
Walmart uses this better than any individual business operator with science and not relying on
the ability of the present market. Additionally, personalization comprises of application of Big
Data to help in process of personalizing different transaction emails as well as increasing
conversion rates (Ali, Qadir, Rasool, & Crowcroft, 2016). Idea o pricing of different products
within the e-commerce business can then be changed constantly.
The constant change in pricing of various services or goods can help e-commerce
businesses to maintain competition by use of real-time analytics. Big Data Analytic this offer
insight for e-commerce business in modern society (Kauffman, Srivastava, & Vayghan, 2012).
Hence, various owners of e-commerce can use information from Big Data and utilize it to
examine different business drifts. Such examinations will aid business operators to gain more
clients as well as streamline operations for success. Some of the ways that Analytics of Big Data
can be used in operations is to increase consumer analysis, improving client service, easier and
more assured different expenses made through online sites (Pal, Li, & Atkinson, 2018). Other

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 9
uses of Analytics of Big Data within e-commerce comprise of need to sustained advances within
mobile commerce and effective reality progressions within retail society.
Increase shopper examination
The need to understand different behaviors of shoppers remain to be an essential aspect
for the success of any e-commerce business. Big Data Analytic is an essential aspect of process.
The idea offers vital and much-needed data on trends, spike demands, together with customer
preference (Singh, 2018). Hence, business owners that deal with e-commerce can utilize that
information in ensuring that common products are readily present. For instance, f any customer
visits the business site to search for different products that the corporation does not offer. Big
data will help the management to learn about those searches mad. The idea of understanding the
need of consumers through Analytics of Big Data can aid e-commerce trade operators to seize
new opportunities. It has been predicted by Lokhande & Khare (2015) in their study that, big
data analytics will advance to illuminate essential behaviors of customer snap patterns, such as
the popular duration for shopping as well as spikes in searches for different products.
Improving customer service
There have been several reports in regard to unhappy customers and poor customer in the
operations of the e-commerce business. The situation has been alarming in the business
community. For example, Dhar, (2014) recorded that about ninety percent of unhappy clients
are not willing to do business with any corporations. Their unwillingness arises if they have had
a deprived client experience. Concentrating on client service in every operation of e-commerce
businesses is crucial to their success in the present competitive business setting. Big Data

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 10
Analytic helps the management of such firms with unhappy customers to understand them and
how to improve their operations. It makes the interaction between business operators and
customers to get better. The use of Analytics of Big Data is essential in ensuring that business
operators are able to identify issues that affect their operations before they can resolve them in an
efficient manner (Tromp, Pechenizkiy, & Gaber, 2017). They also help the management to find
different answers to competes that affect their operations. Additionally, application of Big Data
within e-commerce offers best metrics needed to view how faster customers are capable to
compete for their operations. Hence, implementation of Analytics of Big Data in every trade
operations has continued to offer the best approaches to track experiences of client service, but
also to insert more analytic monitoring (You, Ogiela, & Hwang, 2015). The idea has greatly
helped e-commerce businesses to effectively identify prospect issues and resolve them prior to
the customer gets involved in operations.
Easier as well as protect payments
Big Data Analytic plays important purpose in increasing business transactions and
payments to be more secured. For instance, big data usage improves how a company makes
payments in several ways (Najafabadi, Villanustre, Wald, & Muharemagic, 2015). For instance,
Analytics of Big Data incorporates all diverse payment purposes made into a single federal
platform. Not only does big data usage helps with ease of use for clients, but it also aids in
reducing instances of scam dangers. The superior analytics obtained by Big Data are influential
and instinctive sufficient to be capable of discovering instances of fraud in real moment (Hare,
2014). The idea helps in offering a proactive solution for identifying dangers in operations.

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 11
Social Media Analytic alongside E-commerce
Application of Social media analytic comprises of the gathering of different information
from various social media platforms or sites. These platforms may include Facebook, blogs,
Instagram, Twitter, Google plus, WhatsApp, and Snapchat among other platforms (Emmert-
Streib, Yli-Harja, & Dehmer, 2018). All the gathered data from such sites are then evaluated to
gain insights and knowledge on how operations of e-commerce can be improved. Hence, societal
media can be grouped as big data has it stands traits of the three V’s. For example, in daily
business operations in the e-commerce, there are over three million updates on social media
platforms. Sites of societal media are not only referring to systems of consistent individuals,
although they are a virtual society (Verma, 2017). They offer the best site where individuals
interact, exchange ideas, as well as share different opinions in relation to business operations.
Hence, such activities offered by the usage of social media platforms can be capable of
influencing perceptions of e-commerce business customer son a specific brand.
As a result of over-relying on social media usage by the present society, a lot of e-
commerce retailers are accepting analytics of social media for several motives. Some of the
reasons for the adoption of Analytics of Big Data within E-commerce include need to drive
customer traffic (Zheng, 2015). The other reason is the need to gain competitive merit as well as
business values during operations. The need to boost retention alongside loyalty of customers
remains to be the other major concern that makes e-commerce to adopt use of Analytics of Big
Data. There is need to enhance daily sales along with revenues, enhancing the satisfaction of the
customer, and most essential to develop an awareness of the brand are some of the reasons as to
why Big Data Analytics is useful in improving e-commerce (Dhar, 2014). Al these ideas are
essential in creating an improved reputation of brand and business operators.

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 12
Conclusion
From the above assessment of Analytics of Big Data, it is apparent that analytics hold
many pledges for providing insight into the business. Big Data also holds the insight of analyzing
the behavior of consumers that come with unique challenges. The huge obstacle in the use of Big
Data within e-commerce business remain as staffing as well as training that is needed for the
program to be efficiently implemented in operations. The privacy constraint is also another form
of constraint that affects the manner Big Data is functional in business operations among e-
commerce. Majority of business operators and consumers o e-commerce products are concerned
about how their personal identity is used. Therefore, Bi Data have been utilized by different e-
commerce businesses to increase their efficiencies and how they gather sensory data being used
in manufacturing contexts.

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References
Ali, A., Qadir, J., Rasool, R., & Crowcroft, J. (2016). Big data for development: applications and
techniques. Big Data Analytics, 1(1). doi: 10.1186/s41044-016-0002-4
Al-Sakran, H. (2014). B2C E-Commerce Fact-Based Negotiation Using Big Data Analytics and
Agent-Based Technologies. International Journal Of Advanced Computer Science And
Applications, 5(12). doi: 10.14569/ijacsa.2014.051204
Cercone, F'IEEE, N. (2015). What's the big deal about big data?. Big Data And Information
Analytics, 1(1), 31-79. doi: 10.3934/bdia.2016.1.31
Dhar, V. (2014). Big Data and Predictive Analytics in Health Care. Big Data, 2(3), 113-116. doi:
10.1089/big.2014.1525
Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2018). Data Analytics Applications for
Streaming Data From Social Media: What to Predict?. Frontiers In Big Data, 1. doi:
10.3389/fdata.2018.00002
Hare, J. (2014). Bring it on, Big Data: Beyond the Hype. Big Data, 2(2), 73-75. doi:
10.1089/big.2014.1520
Hussain, A., & Roy, A. (2016). The emerging era of Big Data Analytics. Big Data Analytics,
1(1). doi: 10.1186/s41044-016-0004-2

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 14
Kauffman, R., Srivastava, J., & Vayghan, J. (2012). Business and data analytics: New
innovations for the management of e-commerce. Electronic Commerce Research And
Applications, 11(2), 85-88. doi: 10.1016/j.elerap.2012.01.001
Kirkpatrick, R. (2013). Big Data for Development. Big Data, 1(1), 3-4. doi:
10.1089/big.2012.1502
Lokhande, S., & Khare, N. (2015). An Outlook on Big Data and Big Data Analytics.
International Journal Of Computer Applications, 124(11), 37-41. doi:
10.5120/ijca2015905658
Najafabadi, M., Villanustre, F., Wald, R., & Muharemagic, E. (2015). Deep learning applications
and challenges in big data analytics. Journal Of Big Data, 2(1). doi: 10.1186/s40537-014-
0007-7
Pal, G., Li, G., & Atkinson, K. (2018). Multi-Agent Big-Data Lambda Architecture Model for E-
Commerce Analytics. Data, 3(4), 58. doi: 10.3390/data3040058
Saeed, F. (2018). Towards quantifying psychiatric diagnosis using machine learning algorithms
and big fMRI data. Big Data Analytics, 3(1). doi: 10.1186/s41044-018-0033-0
Sangeetha, S., & Kannimuthu, S.. (2016). Survey on Big Data Analytics and its Applications.
International Journal Of Computer Applications, 153(12), 9-12. doi:
10.5120/ijca2016912137
Singh, A. (2018). Customized biomedical informatics. Big Data Analytics, 3(1). doi:
10.1186/s41044-018-0030-3

BIG DATA ANALYTICS AND ITS APPLICATIONS IN E-COMMERCE 15
Tromp, E., Pechenizkiy, M., & Gaber, M. (2017). Expressive modeling for trusted big data
analytics: techniques and applications in sentiment analysis. Big Data Analytics, 2(1).
doi: 10.1186/s41044-016-0018-9
Verma, S. (2017). Big Data and Advance Analytics: Architecture, Techniques, Applications, and
Challenges. International Journal Of Business Analytics, 4(4), 21-47. doi:
10.4018/ijban.2017100102
You, I., Ogiela, M., & Hwang, M. (2015). Intelligent technologies and applications for big data
analytics. Software: Practice And Experience, 45(8), 1019-1021. doi: 10.1002/spe.2331
Zeide, E. (2017). The Structural Consequences of Big Data-Driven Education. Big Data, 5(2),
164-172. doi: 10.1089/big.2016.0061
Zheng, Z. (2015). Introduction to Big Data Analytics and the Special Issue on Big Data Methods
and Applications. Journal Of Management Analytics, 2(4), 281-284. doi:
10.1080/23270012.2015.1116414