Data Analytics for E-commerce Company Introduction

Data Analytics Association 12 Data Analytics Student name Course Tutor University Date Data Analytics for E-commerce Company Introduction Business organizations operate on large amounts of data that are stored on the firm’s databases (Hartmann, Zaki, Feldmann & Neely, 2014, 25). Accordingly, with the internet-based operations companies rely on data analytics to develop business models that are used to predict the operations of the firms (Woerner, & Wixom, 2015, 61).

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Data Analytics for E-commerce Company
Introduction
Business organizations operate on large amounts of data that are stored on the firm’s
databases (Hartmann, Zaki, Feldmann & Neely, 2014, 25). The IT departments are usually
responsible for maintaining and providing business data for functional use in the companies.
Consequently, most business organizations have transformed their business operations online
due to the technological advancements. Thus IT personnel in the companies get involved in
analyzing and predicting the business patterns of the organizations, basing on the data kept in the
databases. Accordingly, with the internet-based operations companies rely on data analytics to
develop business models that are used to predict the operations of the firms (Woerner, & Wixom,
2015, 61). In using data to predict business performance, data scientists get involved in analyzing
the key operations. Data analysts work on a variety of business data to understand and determine
solutions to the problems affecting the firm’s operations.
The major problem affecting the performance of the e-commerce company is the
decrease in profits due to increasing competition and unpredictability in customers’ expectations.
This is in spite of the company leading in the market with a variety of product selections. With a
wide range of product segments, it implies that the company has a vast amount of data on the
sales made. Therefore, the e-commerce company needs to analyze the sales data for each product
segment and the geographic region information, so that it can improve its performance. The
results of data analysis will be used in developing business models which can be employed in
making decisions regarding customers and the products sold. Data analysis strategies will work
out to explore the possible geographic region the company can target to add new customers and

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increase the sales made. In addition, the analytics intent to innovate new business strategies and
the products which should be prioritized so that it can retain more customers.
Background
Big data refers to a collection of massive and complex data volume that comprises of
huge amounts of data and data management capabilities (Anuradha, J., 2015, 321). This term
was coined because of the continuous generation of information from the different emerging
technologies. Researchers developed the interest in the field of big data analytics to help in
stimulating economic growth as well as predicting future trends for investors (Lee, Kweon, Kim
& Chai, 2017, 57). In online transactions, data analytics is based on information captured from
internet clicks, social media information, and mobile transactions as well as other customer
purchase transactions (Li, Xing, Liu, & Chong, 2017, 30).
According to the research study by Koirala, e-commerce firms emerged as the fastest
groups of businesses to adopt big data analytics (Akter, & Wamba, 2016, 180). Adoption of big
data in companies led to the reduction in cost and improvement of business performance due to
the vast storage and processing capacities of advanced technologies. E-commerce firms such as
Amazon and Netflix rely on big data information to improve the business operations. Data
captured by e-commerce firms is mainly categorized into transaction activity data, click-stream
data, video data, and voice data in tracking consumer shopping behaviors (Voytek, 2017, 1231).
Researchers analyzed the performance of giant retail companies and established that firms such
as Amazon generated almost 30% sales through data analytics (Lee, Kweon, Kim & Chai, 2017,
978).

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According to the efficient-market hypothesis, product prices reflect all information and
events of the online business operations performed 9 Arthur, 2018, 42). For instances, if firms
invest in big data analytics, information on the investment value gets reflected in the stock
prices. Investments made in big data analysis positively affects the stock prices, therefore,
leading to improved business performance (Wamba et al., 2017, 357). Several research studies
indicate that companies which adopt data analytics experience improvements in business
operations due to the speed of information processing.
According to Faed (2013), customer relationship management can be controlled by
analyzing data on online transaction platforms. Information can be captured basing on the
behavioral and non-behavioral reactions of customers who engage with the business firms.
Accordingly, several studies indicate that customer complaints on online platforms are crucial in
improving products quality (Kwon, Lee & Shin, 2014, 390). Consequently, data captured from
customer reactions can be analyzed effectively to enhance the loyalty of clients to the business.
Research Methodology
In data analytics, the whole process majors on the analysis of vast amounts of data
presented. The statistical data analysis approaches are applied in studying the behavior of the
market variables. In order to apply the statistical tools in data analysis, the data is modeled in a
way that the responses can be explained. Thus, data generated from the statistical methods are
applied in developing statistical models from which assumptions are made relating to the
normality, and randomization of data.
Generally, there are about seven big data analysis techniques that can be applied in
developing a business model in order to predict the performance behavior (Lin, 2015, 48). These

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strategies include association rule learning, classification tree analysis, genetic algorithms,
machine learning, regression analysis, sentiment analysis, and social network analysis
(Suthaharan, 2016, 56). Association rule learning method is used to establish the correlation
between the data variables for big data firms. The methodology is normally employed to assist in
extracting information on customers who visit the websites and placement of products in a better
proximity. Classification tree analysis is employed in data analytics to determine the category in
which specific data sets belong in. consequently, genetic algorithm methodology algorithm
methodology outlines the evolution of business mechanisms in solving problems which usually
require optimization.
On the other hand, machine learning methodology involves the application of software in
analyzing and predicting data behavior (Zakir, Seymour & Berg, 2015, 34). This methodology
will help in evaluating the customer preferences and determining the probability of customer
prospects. Accordingly, regression analysis is a statistical methodology that can be applied in
data analysis to manipulate the independent and dependent variables in the e-commerce industry.
In addition, social network analysis methodology is essential in analyzing big data generated
from the e-commerce firm.
In this study, a case study of 5 business companies will be used to predict the
performance of the e-commerce company. Correlation statistical method will be applied in
analyzing the data from the case study. The data provided is modeled using a Likert-rating scale
so that the results generated can be evaluated using statistical methods such as mean, standard
deviation, regression analysis and hypothesis testing (Baesens, Bapna, Marsden, Vanthienen &
Zhao, 2016, 110). Calculation of the mean is essential in establishing the general trend of the
dataset generated on the e-commerce sales made. Consequently, the standard deviation analysis

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of the data will determine how the data is spread around the mean. Dispersion of data from the
mean is used to analyze the customer behavior.
The business companies involved in this study are the consumer lending institution,
investment banking institution, securities trading institution, hedge fund institution, and a wealth
management institution. The survey study will establish the use of data analysis in improving the
performance of the companies. In this survey, literature survey is used to collect data from the
companies basing a nominal scale rating. Therefore, the scale comprises;
(5) Very High Role to Business Success
(4) High Role
(3) Intermediate Role
(2) Low Role
(1) Very Low Role
(0) No Role in Business Success
The scale is rated basing on the business factors which are a need for identification, market
segmentation, performance improvement, business model innovation and creation of transparent
infrastructure. After the data is coded, statistical methods are employed in analyzing and
determine the behavior of the e-commerce company.
Analytical Findings
Results from the studies indicate that the big data analytics contribute greatly to the
success of the business performance. Analysis of the results indicates that the business
companies are yet to fully integrate data science techniques in analyzing business performance.

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The results are recorded basing on the level to which big data mechanisms have been integrated
into the business factors of the business firms studied. Data captured from the studies rates the
level of success of the business performance, business model innovations, creation of transparent
infrastructures, data integrity, and ethics as well as the process management.
The results of the big data analytics in e-commerce using existing literature are presented in the
table below.
Big data analytics Average rating Percentage
Performance improvement 4 80%
Business model innovation 4 80%
Creation of transparent
infrastructure
3 60%
Analytical maturity 2 40%
Data ethics and integrity 2 40%
Process management 3 60%
The sales of e-commerce business are summarized below per geographic region as in the year
2018.
Geographic region Sales in billions (USD)
North America 249.8
Western Europe 184.3
asia 162.9
Latin America 16.9
Central and eastern Europe 15.2

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The Middle East and Africa 0.3
From the table above, it is evident that performance of business companies improved
with the adoption of data analytics methods. Consequently, the business model innovation is
rated at 80% due to use of data science in running the businesses. This implies that adoption of
data science in analyzing business performance can improve on the innovation strategies of
business models used. Moreover, the creation of transparent infrastructure and incorporation of
data integrity measures is not substantially incorporated in the e-commerce industry.
Recommendations
The geographic region to target new customers
From the geographical e-commerce sales, I recommend the e-commerce company to
target the North American, Asia and Western Europe markets. This is because the sales data
from the markets is high compared to the other regions. Thus, the company will be able to reach
out to the huge customer population in these geographic regions. In addition, the company will
be able to increase its sales from the online transactions, thereby realizing an increase in profits.
Products to be prioritized
With the increasing changes in technological features, customers purchase electronic
products more than other items on e-commerce platforms. Therefore, the e-commerce company
is recommended to prioritize electronic products and clothing in order to acquire more
customers. Apart from the above products, the company should also base on household items due
to the increased demands.

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Impacts of shipping products
Shipping of products will be accompanied by both positive and negative consequences.
Positively, the service will increase the number of customers purchasing from the company. This
is due to the delivery of products to the residential regions. However, the shipping services could
be accompanied by the delivery of wrong products, products with different features and damaged
products (Bharucha, 2017, 150). Thus this could lead to failure of the company in retaining the
customers.
New innovative ideas to improve profits
To improve the profits of the e-commerce company, the company should incorporate new
ideas into its operational strategies. As an innovative strategy, the company must make
categorize the products data to ease the searching process. The company can also prioritize that
product and develop features that display the latest products within the firm's stores. Another
strategy is to advertise the company's products on the online social platforms.
Implementation Plan
When implementing the e-commerce innovative strategies, the company should target at
increasing the number of customers and retaining the current customers. The implementation
plan should involve creating advertisement features, training the personnel and improving on the
company's security features. The company needs to implement products with high demand in
acquiring more customers. This will involve adding functional features to the e-commerce
website that display the most purchased products. Moreover, the implementation plan will
involve adding security features to the e-commerce platform in order to improve the integrity of
data. After incorporating these features, the company will have to develop advertisement features

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on the social platforms so that more customers are reached out. Then the shipping service will be
adopted by building more store points in the geographical markets with high sales.
Conclusion
Business operations in e-commerce companies involve processing of large amounts of
data. As a result, data science analysis concepts are employed in the processing of huge
information and developing business models from the results. The e-commerce company in this
study is faced with performance constraints that have considerably decreased product sales and
profits generated. Data mining strategies were used in the study to examine mechanisms of
improving the business performance. From the previous studies done, it is evident that data
analytic techniques will improve the business performance of the company. Data analysis
techniques provided information on the geographical location and products information which
will relevantly help the company to operate on new business ideas.

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