A Comprehensive Literature Review on Business Analytics in Retail

This literature review examines the application of business analytics within the retail industry. It defines business analytics, differentiating it from business intelligence, and explores various types such as descriptive, predictive, and prescriptive analytics. The review traces the historical development of business analytics, emphasizing the impact of technological advancements, particularly in data storage and processing. It highlights the challenges faced by retailers, including managing customer data, understanding market trends, and optimizing operations. The review discusses the benefits of business analytics in addressing these challenges, such as improved decision-making and enhanced customer insights. Furthermore, it identifies several business analytics software tools, including BOARD, Tableau, Web Focus, and ThoughtSpot, and their specific functionalities in the retail context. The conclusion emphasizes the importance of selecting appropriate tools to leverage the full potential of business analytics for retailers to gain a competitive edge.

Running Head : LITERATURE REVIEW
Literature Review
Name of the Student
Name of the University
Author Note

1LITERATURE REVIEW
Abstract
There are different types of business
analytics such as the decision analytics
which supports the human decisions
with the visual analytics that is modelled
by the user for reflecting reasoning.
This literature review explores the
concept of business analytics , the
different types of business analytics
tools and its usage and benefits in the
retail industry. The review finds that
there are different business analytics
software tools BOARD, Tableau, Web
Focus, Thought Spot and many others.
The business no longer could use the
institution to predict the sales and have
to use the retail analytic tools for
reducing the risk of the human error

2LITERATURE REVIEW
Topic- Literature Review on the use of
business analysis in the retail industry
The business analytics can be defined as
the skills, the practices and the
technologies required for the continuous
exploration and investigation of the
previous business performances in order to
gain insight into the business planning in
future. The focus of the business analytics
is on the new insights and the
understanding of the business
performances founded on data and the
statistical models. As a contrast , the
business intelligence is concentrated on the
use of a consistent set of metrics to both
the past performances measurement and
future business planning guidance. The
business analytics makes a wide use of the
statistical analysis having included the
predictive modelling and the explanatory
modelling[1]. It also utilizes the fact
based management in order to drive the
decision making. Hence, it is closely
related to the management science.
Moreover, the analytics can also be used
as an input for the human decisions and
also support to drive the fully automated
decisions. The business intelligence is all
about the reporting, querying , alerts and
the online analytical processing. Taken for
example, the banks like the Capital One
make good use of the data analytics for
differentiating among then customers
founded on the credit risk and usage too.
There are different types of business
analytics such as the decision analytics
which supports the human decisions with
the visual analytics that is modelled by the
user for reflecting reasoning. There is
descriptive analytics which is helpful in
gaining deeper insight from the historical
data including clustering, scorecards and
reporting[2]. The predictive analysis on
the other hand is the predictive modelling
covering the use of the statistical data and
the machine learning techniques too.
Additionally, the prescriptive analytics

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recommends the decisions having used the
simulation and the optimization.
The history of the business analytics
would suggests that the use of the business
analytics in the business began years ago
when the exercises of the management
were out into place by Frederick Winslow
Taylor in the later half of the 19th century.
In the later parts of the 1960s, the business
analytics caught more command and more
attention too. Since that particular period,
the business analytics have evolved and
developed with the enterprise resource
planning or the ERP systems, a large range
of the software tools and the data
warehouses. The biggest evolution of the
business analytics is the introduction to the
computers. The analytics have reached the
highest level through this change. It has
to be note that the business analytics are
depended on the sufficient volumes of the
high quality data[3]. The major challenge
is with the integration of the data quality
having reconciled different systems.
Previously, the analytics was thought to be
an after the fact process which could
forecast the consumer behaviour through
the examination of the number of units
sold in the later part of the years.
However, this kind of the data
warehousing needed the more storage
space. Today, the data analytics is
becoming a popular tool which can easily
influence the customer interactions. It has
gained rapid popularity in different
industries and among them, it is most
evident in the retail industry. Data can be
considered as a key to unlock the sakes
potential but the retailers are over
burdened with so much data and out of the
continuous information streams, it
becomes quite impossible for them to
discover the customer behaviour. They
also become confused about how to align
the knowledge with the actions which
would drive more sales. Notably, business
analytics is not a new concept and the new
technologies have made the average
business users to analyze and understand
the data. Before using the business

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analytics in the organization, the
knowledge of the customers should be
there for better understanding. The
retailers must know their customers and at
the same time apply their existing
knowledge about the customer
preferences. It is likely to support them in
gaining the competitive advantage. When
observed closely, the customers do leave
their footprints at both the time of
purchasing or not purchasing from the
retailer. The footprints of the customers
can be found in the web site, purchase
making and staff questioning. These
footprints are useful in determining who
the customers are, what they are buying
and how often they shop. The retailer’s
goal is always to capture the data and
translate into the profit which is only
made possible by the business analytics
methods. The customer data captured by
the retail industry can be revealed due to
the workflows, the global markets, the
national infrastructure along with the
natural systems in the supply chain[5].
The business analytics in the retail
industry offers a reliable information
which can drive better decisions. In order
to stay competitive in the market, the
trends need to be known, the customers
need to be understood and everything
needs to be done in a timely manner. The
increased penetration of the analytics in
the retail industry has transformed the
analytics industries emerge like
mushrooms[4]. This increased adoption
can be said to be the result of the
advantages of business analytics in the
industry. The retail market is considered
to be a fickle market where the trends
change rapidly. Retail is all about the right
product, in the right prices sold to the right
person[6]. It is equally true that when the
customers don not find the business, they
cannot acknowledge the products and
develop a choice of it. The retailers are
facing several challenges which includes
the sustained margins of profits in the
markets which have been quite low since
the beginning. Hence, the retailers are

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expected to select the right combination of
the products for selling having selected the
appropriate suppliers and shipping options.
They must manage the customer
expectations, the inventory management
for the seasonal shifts in the demands
along with the prize optimization. The
business analytics help the retailers to keep
in terms with the market dynamics and
address the above mentioned challenges.
They also need to have a comprehensive
insight into the way the actual results
work when contrasted with the planned
numbers, he store location and the
revenues by product. The retailers are also
required to manage the operational costs
for ensuring that the costs of the products
are optimized. The buyers’ experience in
the retail industry does not look like it
looked 10 years ago[4]. It is not even the
same as it was in the previous year. The
reason can be the constant demand in the
buying trends. The result is that the
business no longer could use the institution
to predict the sales and have to use the
retail analytic tools for reducing the risk of
the human error. Hence the suitable
business analytics tools became the
scenario changer in the retail industry.
There are different business analytics
software tools BOARD, Tableau, Web
Focus, Thought Spot and many others.
BOARD can be referred to a tool of the
business intelligence which offers the
business analytics and the enterprise
performance management. The self-
service data discovery environment helps
to expand the toolset considerably. This
data discovery tool is used by the retail
industry to find customer information and
performs a high –level analysis too.
Tableau on the other hand is a data
visualization tool enables the non-
technical users to conduct the analysis of
the data without writing the code. The
visual query language is also known as the
VizQL which expresses the data
visually[6]. The users of this tool are
empowered to share their own customs
with fully interactive dashboards that run

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the gamut as far as the technical ability is
concerned. The dashboards are useful in
displaying the trends and the variations in
the data in the form of charts and graphs.
The Web Focus on the other hand is a
business analytics solution which offers
the retail analytical tools, reports ,
application and the visualizations in order
to guide the real-time decision making and
discovering the hidden trends. The
Thought Spot is an AI-driven analytics
platform which instantly presents the
search results because the best –fit
visualization of the data needs much
support from the IT. The Relational
search engine of ThoughtSpot allows the
use of the search to instantly build extra
accurate charts and the dashboards. On a
concluding note it can be said that the
decision making process in the retail
industry can be difficult during the
selection of the right business analytics
tool[3]. There are some of the business
analytics tools which may not offer
sufficient versatility and flexibility.
Undoubtedly, there are countless programs
which are there for the retailers and these
top –level tools are pushing the companies
to grow from the ordinary to the
extraordinary.

7LITERATURE REVIEW
Reference
1. Research.unsw.edu.au
(2019). Select Publications by
Professor Richard Timothy Vidgen
| UNSW Research. [online]
Research.unsw.edu.au. Available
at:
https://research.unsw.edu.au/people
/professor-richard-timothy-
vidgen/publications [Accessed 25
May 2019].
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"Business Intelligence as a
Competitive Advantage in the
Retail Industry -Big Data Analytics
News", Big Data Analytics News,
2019. [Online]. Available:
https://bigdataanalyticsnews.com/b
usiness-intelligence-competitive-
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29- May- 2019].
3. Imarticus.org, "10 MOST
POPULAR ANALYTICS TOOLS
IN BUSINESS", Imarticus, 2019.
[Online]. Available:
https://imarticus.org/10-most-
popular-analytics-tools-in-
business-data-analytics-blog/.
[Accessed: 29- May- 2019].
4. Dataversity.net, "A Brief History
of Analytics -
DATAVERSITY", DATAVERSITY
, 2019. [Online]. Available:
https://www.dataversity.net/brief-
history-analytics/. [Accessed: 29-
May- 2019].
5. GoogleBooks.co.in, "Business
Analytics for Managers", Google
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https://books.google.co.in/books?
hl=en&lr=&id=4BMlDQAAQBAJ
&oi=fnd&pg=PR11&dq=business
+analytics+&ots=_jyzkbekCF&sig
=YzQJDu29wkfC1loYI2w84gSdP
Pw#v=onepage&q=business
%20analytics&f=false. [Accessed:
29- May- 2019].
6. R. Vidgen, S. Shaw and D. Grant,
"Management challenges in
creating value from business

8LITERATURE REVIEW
analytics", Econpapers.repec.org,
2019. [Online]. Available:
https://econpapers.repec.org/RePEc
:eee:ejores:v:261:y:2017:i:2:p:626-
639. [Accessed: 29- May- 2019].

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Appendices