Impact of Big Data Analytics on E-Commerce for Business

Application: A Review and Analysis of Its Essence

in a Competitive World

Rajkumar S

, Mukundh J

and Jenila Livingston L M

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

Keywords: Big Data Analytics, E- Commerce, Machine-Learning, Competition, Digitization, Data-Driven, Insights.

Abstract: Big data analytics has had a transformative effect on e-commerce. An estimated annual growth rate of 22 to

24 percent has been observed for the production of digital data, and it has resulted in the generation of

zettabytes of data. It is anticipated that this trend will continue in the future years, with projections

indicating a significant increase in the global data-sphere. The exponential growth of data has compelled

businesses to adopt a data-driven approach to rethink their decision-making strategies and embrace new

insights derived from vast troves of information, recognizing that the more they know about their data, the

greater their chances of success, as this mindset contributes to higher productivity, rapid expansion, and

creative innovations, resulting in distinctive competitive benefits. In this research, we attempt to present

genuine proof on how big data analytics might benefit e-commerce platforms to predict, monitor and

analyze sales, and build a better model, through a small illustration. Through this paper, we aim to validate

the positive impressions and potential of big data analytics on e-commerce.

https://orcid.org/0000-0001-5860-7161

https://orcid.org/0009-0007-2113-9879

https://orcid.org/0000-0002-6333-5751

Corresponding author

1 INTRODUCTION

Shopping, and trading goods have been an integral

part of society even before medieval times. The

emergence of E-commerce is not a surprise

considering this actuality, but the development in

this field is what is to be regarded as remarkable as

advancements in programming, machine-learning,

big data, etc. and digitization have brought

significant changes that are flourishing in

E-commerce and Business (Garg et al., 2021). The

most significant of them all is the involvement of

Big data analytics(BDA). In this big data and

technological era, e-commerce has flourished with a

great deal of anxiety about the product/consumer

market, competition and development, driving

e-commerce enterprises to substantially invest in

BDA (Gaikwad & Patil, 2022). It uses advanced

statistical and machine-learning algorithms to

analyze complex datasets for informed

decision-making, and is becoming increasingly

important as the volume, velocity, and variety of

data continue to grow exponentially in e-commerce

(Al-Alwan et al., 2022).

Many companies and businesses are rising with

the help of E-commerce platforms, where products

meet customers. E-commerce transactions provide

access to nearly every possible commodity and

service, including books, music, aircraft tickets, and

financial services such as stock investing and online

banking. BDA has emerged and revolutionized the

way businesses collect, process, and assess massive

amounts of data. With the help of powerful tools like

Hadoop, Tableau, Cassandra, Open Refine, Hive,

MongoDB etc. (Painuly et al., 2021), and

models/algorithms, organizations can now gain

precious revelations from their data to drive better

decision-making and enhance shopping experience,

S., R., J, M. and M, J. L. L.

Impact of Big Data Analytics on E-Commerce for Business Application: A Review and Analysis of Its Essence in a Competitive World.

DOI: 10.5220/0012881100004519

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture (ICEISA 2024), pages 40-49

ISBN: 978-989-758-714-6

gross-margin and sales (Alrumiah & Hadwan, 2021;

Sazu, 2022). BDA has a significant impact in

E-commerce, enabling companies to extract

actionable insights from their data to improve

performance/sales, increase efficiency, and drive

innovation (Farras et al., 2022). E-commerce has

developed throughout the past 40 years and paved its

way from electronic data interchanges and

teleshopping to modern cutting-edge online

electronic stores. It has become a contemporary,

widespread business model (Wong, 2022) and been

the subject of much research.

Understanding how consumers make purchasing

decisions online is one field of e-commerce study

that looks at things like website design, product

information, and usability. According to studies,

people tend to be more inclined to make purchases

from websites they feel comfortable with and

believe to be trustworthy and user-friendly (Guan et

al., 2021). Social media's influence in affecting

consumer perceptions of products and companies

(Peter et al., 2023). In e-commerce research, security

and privacy also pose major problems. With an

upsurge in online fraud and identity theft, academics

have looked into security measures such as

authentication and data encryption procedures (Alkis

& Kose, 2022). Given the growing importance of

timely delivery and efficient inventory

administration in e-commerce, another area of

research focuses on logistics and supply chain

management (Lee & Mangalraj, 2022; Kalkha et al.,

2023).

Researchers have investigated strategies to optimize

these processes through the use of technology like

RFID, GPS, and automated warehousing systems

(Kalkha et al., 2022). Some also examined the

influence of e-commerce on established supply

chain models and the prospect for new business

models to come into being and the role of other

technologies like Artificial Intelligence (AI) and

Cloud Computing in e-commerce (Zhuang et al.,

2021). The studies also looked at how e-commerce

has affected sociocultural norms, such as the

emergence of online communities and the changing

nature of both professional and recreational

activities (Onete et al., 2022; Chava et al., 2024).

Other areas of research related to e-commerce

include the adoption of e-commerce in emerging

economies (Almajali et al., 2021), the dark side of

e-commerce (Ho, 2022), and e-commerce return

regulation (Zennaro et al., 2022).

Gopal et al.(2022), essentially identify data

science, neural networks, enterprise resource

planning, cloud computing, machine learning, data

mining, RFID, Blockchain and IoT, and business

intelligence as the most recognized practices for big

data. The involvement of BDA in E-commerce was a

substantial growth required to impress a massive

amount of clients. E-commerce faces challenges

with big data, including managing volume, selecting

tools, shortage of skilled professionals, inadequate

security measures, and integrating data from

multiple sources (Painuly et al., 2021). With the

latest advancements in big data, data management

technologies (Li & Zhang, 2021) and

machine-learning models, E-commerce is striving to

reach goals that were once considered impractical.

BDA has become an integral part of modern-day

e-commerce, playing a crucial role in driving

strategic decision-making for organizations.

The importance of BDA in e-commerce cannot

be overstated. Firstly, it enables organizations to

gain deeper clarity into customer behaviour, market

trends, and other critical aspects of their operations

(Sulova, 2023). With the help of advanced analytics

tools, businesses can analyze massive volumes of

data generated from multiple sources to identify

patterns and trends aiding in making better decisions

(Kumar et al., 2022), strengthen strategic steps (Gao,

2021; Ranjan & Foropon, 2022) and gain

competitive advantage. Understanding customer

demographics assists business development teams in

visualising product sales pathways, customer

happiness, churn rate, and forecasting future sales.

Secondly, e-commerce systems that combine with

big data analytics can provide decision-makers with

real-time information to make informed decisions

quickly. By utilizing the power of predictive

analytics, organizations can make proactive

decisions that improve performance (Kumar et al.,

2022; Bogdan & Borza, 2019), reduce costs, and

increase revenue. This is especially important in a

rapidly changing business environment where timely

decision-making can mean the difference between

success and failure. In today's highly competitive

marketplace (Guan et al., 2021), businesses that use

advanced analytics tools can extract valuable

disclosures to identify new growth opportunities,

optimize operational efficiency, and create more

effective marketing campaigns.

As organizations grow and generate greater

quantities of data, their e-commerce systems must be

able to scale to meet their needs. BDA provides

Impact of Big Data Analytics on E-Commerce for Business Application: A Review and Analysis of Its Essence in a Competitive World

scalability by allowing businesses to store and

analyze huge amounts of structured (like Name, Age,

and Gender) and unstructured data (like clicks, links,

voices, and likes) (Wong, 2022), which provides a

more comprehensive view of their operations,

essential for organizations that want to keep up with

rapid growth and take advantage of emerging

opportunities. Lastly, implementing BDA can lead to

significant cost savings in various areas such as

supply chain management and marketing campaigns

(Lee & Mangalraj, 2022). Consecutively, it helps

businesses achieve sustainable growth and

profitability.

Big data analytics ideals helps reveal information

such as hidden patterns, correlations, market trends,

and customer preferences that can assist

organizations in making educated business decisions.

It provides businesses with advanced tools and

techniques to process, examine, and exploit data to

make better decisions and reconstruct strategies

(Zineb et al., 2021), enabling businesses to gain

more accurate perceptions into customer behavior,

market trends, and operational efficiencies, leading

to improved products, service quality, and customer

satisfaction. Consequently, it has revolutionized the

field of business intelligence, giving companies a

competitive edge in today's data-driven economy

(Tong-On et al., 2021). The utilization of BDA has

become indispensable (Rehman & Mehmood, 2022)

for E-commerce businesses in order to effectively

operate within their expansive market segment

(Farras et al., 2022). The techniques used to analyze

big data in general are: text analytics, audio analytics,

social media analytics and predictive analytics

(Rawat & Yadav, 2021), using renown algorithms

such as Neural Networks, Decision Trees, K-Means

(Zineb et al., 2021), SVM and Linear Regression

(Shahrel et al., 2021). Big data can be valuable in a

variety of customer places, such as boosting

innovation through purchase behavior, issue

recognition, and usage. Big data has altered the

capabilities that businesses require to function

efficiently. Trabucci & Buganza (2019), state that

organizations capable of processing fresh data are

more likely to be succeed. It is important to also note

that companies that can exploit big data in their

business processes, particularly, may have a

significantly higher chance of enhancing their

efficiency and revenue growth than their competitors

(Wang et al., 2022).

As a result, big data represents a novel sort of

capital for increasing business innovation efficiency.

The most prominent benefits that BDA provides are

improved decision making by identifying patterns,

trends and using information systems like DSS

(Decision Support Systems) which provide vital

information on customer behavior, market trends,

and operational performance (Phillips et al., 2021;

Eom, 2020); enhanced operational efficiency by

identifying inefficiencies, bottlenecks, and areas for

improvement thereby optimizing operations, cutting

costs, boosting productivity and promoting

organizational agility (Hammouri et al., 2022); quick

and accurate customer understanding through

analyzing demographic information, purchasing

behavior, and preferences (Mikalef et al., 2020),

aiding in the development of focused advertising,

customer segmentation, churn prediction and

sentiment analysis (Zineb et al., 2021), personalized

product recommendations, and enhanced customer

experiences (Varma & Ray, 2023; Farras et al., 2022)

which contribute to customer satisfaction and loyalty

(Rehman & Mehmood, 2022); improved product and

service innovation enabling organizations to develop

innovative products and services by analyzing

customer feedback and social media interactions

(Dwivedi et al., 2023).

2 CASE STUDY

In this study, we try to investigate the influence

BDA has on E-commerce platforms with the help of

a mini case study of Superstore dataset from Kaggle

(containing sample sales data for the period January

2015 to January 2019, url:

https://www.kaggle.com/datasets/rohitsahoo/sales-fo

recasting) and analytics/visualization tool- Tableau.

The schema for the dataset is as shown in Table 1.

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

Table 1. Schema of the dataset

Field Name Field Type Description

Row ID String Specifies the ID of the sales data

Order ID String Specifies the order ID of the product

Order Date Date Specifies the order date

Ship Date Date Specifies the shipment date

Ship Mode String Specifies the shipment mode

Customer ID String Specifies the Customer ID

Customer name String Specifies the Name of the Customer

Segment String Specifies the segment of the product sold

Country String Specifies the country of the shipment

City String Specifies the city of the shipment

State String Specifies the state of the shipment

Postal Code String Specifies the postal code of the shipment

Region String Specifies the region of the shipment

Product ID String Specifies the ID of the product sold in sample dataset

Category String Specifies the category of the product sold

Sub Category String Specifies the sub category of the product sold

Product Name String Specifies the name of the product sold in the sample dataset

Sales Decimal Specifies the sales quantity

3 RESEARCH OBJECTIVE

To assess the indispensability of Big Data Analytics

for the success of an E-commerce platform.

Understanding the data : The data was reviewed

to remove any duplicates and null values, and missing

values were replaced with aggregate values. Data

validation was done to ensure that every row

followed the data schema and ensured there weren't

any mismatches. Some basic analysis in Tableau

were performed, to gain a deeper understanding of

the data. Figure 1 depicts the sales by state, color

grouped by regions (Central, East, West, South) in

the US. It is observed that the maximum sales took

place in California, with a total sum of 446,306; the

lowest sales data in North Dakota with total sales of

920. Figure 2 depicts the distribution of sales across 3

product categories: Furniture, Office Supplies,

Technology, and their respective sub-categories.

“Technology” has the most sales under the “Phones”

sub-category.

Impact of Big Data Analytics on E-Commerce for Business Application: A Review and Analysis of Its Essence in a Competitive World

Figure 1: Highest Sales by State.

Figure 2: Sales by Category and Sub Category.

4 DATA ANALYSIS AND

FORECASTING

Effective data analysis is of prime importance, and

plays a pivotal role in helping sellers identify and

address areas where their sales are falling short. In

this section, we focus on obtaining fruitful and

actionable inferences that can benefit an

E-Commerce merchant.

On classifying the sales in different provinces for

different categories we observe the following. The

lowest sales for Furniture, Office Supplies, and

Technology were observed to be in Montana, the

District of Colombia and South Dakota, respectively

and the highest sales for all the categories were in

California (As seen in Figures 3, 4, and 5). To

effectively address low sales in a province (say,

Montana), sellers from the particular province(s)

could consider tailoring marketing efforts to better

resonate with the local customer base, capture the

attention of potential customers through promotional

offerings and campaigns, or even adopt strategies

from a competitor.

Figure 6 illustrates the company's sales

performance, that showcases a mix of highs and

lows throughout each year. Notably, the year 2018

stands out as a remarkable milestone with the

highest sales ever recorded, totaling around 104,691

units. On the contrary, in 2015, the company

experienced its lowest point, selling only 5,407

items. This pattern suggests a progressive trend

overall, despite the existence of fluctuations.

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

Figure 3: Sales by Province for the Furniture Category.

Figure 4: Sales by Province for the Office Supplies Category.

Figure 5: Sales by Province for the Technology Category.

Figure 6: Actual sales during 2015 to 2019.

Impact of Big Data Analytics on E-Commerce for Business Application: A Review and Analysis of Its Essence in a Competitive World

Powerful tools like Tableau, offer predictive analysis

and forecasting capabilities, Figure 7 depicts the

prediction of sales in the years 2019-2021, based on

the previously acquired data, which aids companies

to make informed decisions and develop effective

plans that align with anticipated market conditions, so

as to gain advantage over competition and maximize

business outcome. For instance, one of the

estimations is that there could potentially be a

significant decline in sales during February 2019,

which can eventually impact the company at that

particular period of time in various ways, providing

an opportunity for the company to take proactive

measures to mitigate any adverse effects and help

minimize any negative implications. The expected

sales by region is depicted in Figure 8, allowing one

to deduce the differential sales success across

different regions. It is clear that certain locations have

larger sales figures than others, which could aid in

understanding regional sales patterns and enable

organizations to deploy resources effectively,

optimize inventory management, and streamline

supply chain operations to match the requirements of

high-performing regions. Figure 9 offers a

comprehensive overview of the distribution of sales

within each region, showcasing the performance of

various product categories in each of these regions.

Analyzing such data enables businesses to understand

the sales composition and identify the sub-categories

that contribute significantly to overall sales in each

region and assist in product assortment and resource

allocation.

Figure 7: Predicted Sales for the period 2019 to 2021

Figure 8: Predicted Sales by Region

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

Figure 9: Sales for Categories in each Region

5 CONCLUSION

On the basis of our exploration, we find that in the current

digital environment, where competition is fierce and

consumer standards exceed expectations, businesses must

harness data to obtain an edge over rivals. With the rise of

Industry 4.0, everyone shall embrace digitization for

establishing more efficient, error-free, and open systems.

As data volumes continue to swell, new obstacles arise that

must be resolved expeditiously to ensure the development

of trustworthy systems. E-commerce platforms capitalize

on the power of data using BDA to comprehend the way

customers behave, adjust promotional efforts, optimize

processes, and drive revenue expansion, providing valuable

inferences into consumer preferences, market trends, and

productivity, facilitating intelligent choices in analyzing

and creating predictive models based on purchase history,

browsing behavior, demographics, and social media

interactions. Embracing the power of BDA is a strategic

move that empowers businesses to navigate the

complexities of the digital age and remain at the forefront

of the rapidly evolving e-commerce landscape, which can

help attain critical discernment, make informed decisions,

gain a competitive advantage, improve scalability, and

achieve significant cost savings. Through the investigative

analysis of the Superstore sales data, we were able to assess

and categorize the data using data analysis on different

aspects such as customer groups and regionality and answer

how exactly one can scrutinize the knowledge from

different charts to achieve intelligent business decisions

with the ambition to stay ahead of the curve through

constant improvisations in their workflow. Organizations

that seek to thrive in today's fast-paced business

environment shall thereby prioritize the incorporation of

Big Data Analytics into their decision-making processes,

for its high offerings and value in a business framework.

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Impact of Big Data Analytics on E-Commerce for Business Application: A Review and Analysis of Its Essence in a Competitive World