The Significance of Data Mining Applications in Evaluating Customer Reviews in the Context of E-commerce
Transactions
Pritam Kumar1*
1 Lecturer, Digital Business Management, Martin de Tours School of Management and Economics, Assumption University, Bangkok, Thailand
*Corresponding Author: [email protected], [email protected]
Accepted: 15 September 2021 | Published: 1 October 2021
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Abstract: In this age of digitalization, most people use their smartphones and the internet to shop for not just luxury and convenience items, but also for necessities such as food and medicine. In the wake of the Covid 19 pandemic, businesses embraced e-commerce platforms, and customers opted to make purchases in a secure setting. Feedback from customers is critical for a variety of reasons, including helping to improve marketing and manufacturing methods.
The customer feedback or reviews are evaluated by the organisation with the use of data mining technique or applications. One of the primary goals of this study is to determine the impact of data mining applications in the assessment of customer reviews on e-commerce transactions.
The researcher will examine different research papers on certain topics such as data mining applications, e-commerce, contemporary information technology for data science and data mining, and customer reviews in an e-commerce platform, among others. According to the findings of this research, most e-commerce organisations and websites offer information about their goods and services. In addition, they enable users to comment on and record their opinions on different goods and services on the site. Then, with the assistance of data mining tools and technologies, they analyse and summarise the data to make a variety of important decisions or choices regarding products and services.
Keywords: data mining, e-commerce, data science, consumer review
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1. Introduction
In today's world, internet purchasing is a clever, simple, and convenient option for people in a pandemic scenario. Since the outbreak of the pandemic, there have been significant shifts in consumer preferences and purchasing habits. With the rapid development of Internet-based technology, many new possibilities have arisen for businesses of all sizes. As a result of the widespread adoption of digitalization and e-commerce, data mining has become more critical (Khan et al., 2014).
As e-commerce and mobile commerce becomes more popular, consumers throughout the covid 19 pandemic choose to buy essential goods and services online using mobile and other electronic devices. Data mining is beneficial in determining the connection between variables and discovering new trends in the market. It also assists organizations in making proper choices in the areas of marketing, distribution, and all other management decisions(Zhang, 2021).
Consumers find online shopping to be comfortable and convenient because it offers complete assistance in purchasing anything from anywhere and at any time. During pandemic times,
consumers are concerned about viruses and prefer safe and risk-free shopping, so they avoid physical shopping and shopping from physical stores. Since people are busy and do not have time to go shopping, online shopping allows them to save valuable time on their purchases (Dwivedi et al., 2021).
Each organization's primary goal is to give better services to clients and earn their loyalty by maintaining a personal touch and developing a stronger relationship with them. With the help of understanding consumer behavior and purchasing patterns, organizations may increase their revenue and profit. A variety of analytical tools can assist organizations in identifying changes in consumer behavior and purchase patterns (Ciotti, 2021).
It has been demonstrated in most of the research that consumer participation has a considerable impact on marketing, sales, and customer loyalty. All these variables provide a tailored experience for the customer. Providing targeted customers and promotional tactics using numerous promotional channels that consumers favor requires data analysis and science.
According to Shreyashi, (2020) consumers needed confidence while purchasing from an e- commerce site. Most of the e-commerce organizations' experience with consumers that buy online was based on their confidence in the online platform. They return to a store if they have confidence in the organization and are satisfied with the service and goods. According to earlier studies, 86 percent of e-commerce users have substituted their online buying, and 29 percent of consumers have reduced their online shopping if they encounter fraud, poor quality goods, or any other issue when shopping online.
To function effectively, organizations must have substantial information about the e-commerce platform, brands, and goods, as well as an adequate understanding of customer perceptions, preferences, and requirements, among other things (Vasic, Kilibarda & Kaurin, 2019).
It is essential for an organization to recognize the importance of its customers and comprehend their perceptions of the goods and their loyalty to those items. Because of e-commerce platforms, organizations have collected valuable information on their customers, such as online reviews of their products, pricing, and e-commerce platforms. All this information is useful in understanding the target client base.
The function of data mining in assessing customer reviews on e-commerce transactions will be investigated in this study. The researcher will examine different research papers on specific topics such as data mining applications, e-commerce, current information technology for data science and data mining, and customer reviews in an e-commerce platform. The fact that numerous studies and research in data mining have been conducted has sparked a great deal of interest from both the e-commerce industry and academia. The use of data mining methods by e-commerce organizations has been focused on understanding the many important aspects of their business to improve efficiency while also competing with their rivals.
2. Review of related literature
To better understand the role of data mining in e-commerce transactions, this research paper examines various studies. To accomplish this, the researcher gathered information from various peer-reviewed journals, published research and news, and blogs that dealt with e-commerce transactions, consumer reviews, and data mining. The purpose of this study is to investigate understanding and knowledge about customer reviews as well as the function of data mining
in the assessment of consumer reviews on e-commerce transactions, using an exploratory research methodology.
Customers' loyalty and meeting their individual needs, according to Gupta & Bhushan (2012), are tough to attain and maintain. Organizations are confronted with constant shifts in customer expectations, as well as intense competition from multinational corporations.
E-commerce organizations that operate in a global market encounter problems and obstacles when dealing with customers from various cultural and economic backgrounds.
E-commerce offers the benefits of high efficiency, low cost, and the ability to save both time and money for customers and merchants. It allows customers to make purchases at their leisure from any location at any time of day. However, it also has certain drawbacks, such as optimizing website structure and customizing services following customer preferences and behavior. Data mining methods are used in this article to provide an architectural framework for customizing and personalizing electronic commercial transactions.
Business transactions are being studied byChen, (2017), who is researching digital commerce and automated business transactions. With the assistance of current information technology, every organization has adopted a modern business network, platform, and style of operation.
Profit, economic advantages, and a high level of income are the primary objectives. The primary goal of this research is to get a better understanding of the intelligence, networking, and automation of corporate operations. According to the findings of this research, e-commerce has changed company practices, management psychology, philosophy, techniques, and the use of contemporary electronic modes of payment. It has brought about changes not just in incorporating processes but also in society. In conclusion, Web data mining in e-commerce transactions can search a large amount of data. Mining this data can provide specific guidance to e-commerce businesses to increase sales, improve the efficiency of the website, improve system performance, and enhance customer relationships, according to the findings of this study.
Liu et al., (2020) investigated online customer reviews to increase sales and income. The researcher conducted a comprehensive study to understand the strategies of reaction better. It is shown in this article that there is a connection between online and e-commerce responses and the performance of the organization. The researcher used the theoretical background to find the social network theory, which was then revealed in this paper. For this aim, the researcher used a web crawler to gather data and selected 3379 vendors from a Business-to- Business website as a representative sample. The researcher also develops certain assumptions and hypotheses as part of this investigation, which uses regression analysis to disclose the findings. According to the findings of this research, sellers' responses to unfavorable online customer evaluations had a beneficial effect on their sales. Product and price strategies are essential variables to consider throughout the networking procedure. Technical assistance should be provided by e-commerce organizations to improve online transactions. Customer feedback should enhance the product and services; therefore, producers and sellers should collaborate to make the most of it.
Zhang, (2021) researched the latest business trends to help businesses make informed choices.
The purpose of this article is to investigate the current data mining techniques used in e- commerce and the different uses of e-commerce transactions conducted via a website.
Furthermore, the experimental research design is used by the researcher to investigate the e-
commerce system. This research study proposes many models, including a contemporary mathematical model based on Intelligent Emotion Recognition, emotion-oriented intelligent model based on a simulation model, and a computational intelligence model based on emotion recognition. It is hoped that this research will serve as a future reference and practice for e- commerce to adopt software applications for the organization's growth.
3. The Research Objectives
The primary goal of this research is to investigate the function of data mining in assessing customer reviews on e-commerce transactions.
Secondary objectives of the study:
3.1. To investigate the many different applications of data mining.
3.2. To investigate the significance of data mining in the context of e-commerce transactions.
Research Questions
The study sought to provide answers to the following research questions:
RQ.1. What are the different functions of data mining while examining customer reviews on e-commerce transactions?
RQ.2. What are the most significant data mining applications, and how essential are they in evaluating customer evaluations in the context of e-commerce?
RQ.3. How can an e-commerce manager use all these evaluations to improve productivity and the efficiency of the e-commerce organization?
Concept of Data Mining
Stedman, (2021), defines data mining as the combination of data and information obtained via conventional data mining tools and techniques. According to Stedman, most of the information is gathered through the internet and the World Wide Web. Data mining is the process of removing or extracting nonspecific data and information that is buried in large amounts of random data, disordered data, or fuzzy data, among other things. When data mining is done correctly, it may discover and comprehend the essential information hidden inside large and complex data sets. It can also provide a suitable summarization of crucial information that is beneficial to the user. Data mining is finding the causal relationship between data and information and analyzing the correlation between different data. In recent years, every organization has found it necessary to use e-commerce, mobile commerce, and online platforms to expand their operations across the globe (Ismail, Mansur Ibrahim, Mahmoud Sanusi & Nat, 2015).
All these massive activities generate enormous amounts of data and collect a diverse range of raw data. However, most of the time, the business collects random data. As a result of this, organizations require a large amount of database and appropriate data for various decision- making processes. This is accomplished via data mining methods and apps, which transform random data into actionable information for businesses. Data mining is being used in virtually every industry, including retail, e-commerce, supply chain management, and customer relationship management, among others. However, according to the results of a previous study, most research academics are more concerned with the details of the data mining method and algorithm than they are with the different data mining applications (Toorajipour, Sohrabpour, Nazarpour, Oghazi & Fischl, 2021).
With the expansion and development of a corporate organization, the amount of data produced increases, necessitating a company's data warehouse and technological applications.
Furthermore, the company had collected a large amount of data, which necessitated the need for substantial relationship and summarization, the presentation of data in particular data classes, ideas for comparison, and the discovery of relationships between different classes of data. Data mining technology is a method of establishing a suitable connection between random, enormous, and vast data in an organized way.
4. Role of Data mining in the evaluation of consumer review on e-commerce transactions
4.1. Forecasting demand and predicting future trends
Data mining provides experimental models for predicting consumer behavior, which may be used to forecast customer reviews and opinions on e-commerce transactions, among other applications. It makes use of deep learning and machine learning to anticipate user perceptions, opinions, and intent-based on online and internet-based data from an e-commerce website's customer database.
With the assistance of customer reviews, it is possible to monitor and record the purchasing habits and patterns of consumers and users (Dwivedi et al., 2021).
With the assistance of click stream technology, e-commerce businesses may gather a wide range of information on their customers while shopping online. To analyze and mine the vast amounts of information gathered from several sources, e-commerce organizations create their data warehouse and data analysis system. Data mining enables data mining managers to combine large amounts of data from various sources. It has assisted in better understanding each consumer's requirements, buying patterns, and opinions about goods and services. Using sophisticated analytical methods, data analysts could mine accessible data and use all this organized and relevant information to understand consumer preference views and values better.
A variety of applications and indices may be used for data mining to anticipate customer behavior more precisely and thoroughly.
The client profile, order information, distribution system, and after-sales services are only a few of the aspects involved(Farruh, 2020).
4.2. Customer Profile
Infotech, (2020) asserts that the success of any e-commerce company is determined by the quality of the data it collects and the organization's decision-making ability. Consumer profiles are developed in data warehouses using data mining technologies, which analyze the complete information and data accessible in the warehouse and store it in a database. Then the organization collects data and reviews from customers and provides them to data mining for analysis, resulting in identifying key and significant relationships and buying patterns of consumers, which may then be used to make critical choices. All these choices are made based on consumer feedback and previous customer behavior patterns. With the assistance of consumer profiling, data mining may assist in determining the potential and preferences of services among consumers. Additionally, organizations may utilize the data gathered to increase income by making sound decisions about the different offers available by e-commerce organizations to particular and valued consumers.
Various businesses provide customers with access to information on the systems and configurations that they are shopping for. Then consumers exchange that knowledge with one another. Customer interactions are predicted using back-end technology systems that include data mining tools. These tools analyze consumer profiles and generate important
knowledgeable information. These data mining tools and technology are collectively referred to as predictive modeling of customer interactions (McCue, 2015).
With the use of a consumer profile, e-commerce organizations may attract new customers while keeping existing customers. In addition to anticipating consumer behavior, consumer profiling can increase the availability of goods and services. The primary goal of using data mining for e-commerce is to improve the overall efficiency of company operations, which in turn helps businesses to offer more value to their customers. Consumer profiling may help track the mobility of consumers and their potential and paying capacity for goods and services.
4.3. Expand marketing initiatives
In most cases, businesses conduct various marketing surveys and collect data from consumers and users. They also provide facilities for recording these reactions, opinions, and views about their website, as well as the products and services offered by that e-commerce website in question. It is possible to increase the number of consumers and users who engage with a particular website through data mining technologies, which are used in the e-commerce industry to assist businesses in learning about various marketing strategies.
Data mining can also be used to optimize marketing trends (Maria, 2020). During marketing campaigns, the opinions and responses of customers are significant to the campaign's overall success. The marketing department uses data mining technology to analyze all the feedback received from customers to develop a variety of marketing plans and execute marketing campaigns to improve the efficiency of e-commerce transactions.
4.4. Make important business choices
Data mining allows e-commerce organizations to create accurate perspectives and insights into their products and services, establishing assumptions and expectations about their products and services.
E-commerce organizations use data mining methods to learn about their customers' experiences and expectations with the brands, products, and services that they provide. Customer reviews may be determined by looking at consumer prospects interested in the products or services that the company is offering. Organizational managers may use this data and information to increase profit and revenue by making important decisions based on the information (Estay, 2020). It is not enough for businesses to rely only on assumptions; they must also gather accurate data from surveys and records of customer evaluations accessible on websites. For e- commerce organizations, data mining is used to analyze all user and customer evaluations to help them make better choices. Most strategic choices are based on information gathered via data mining.
4.5. Forecast the consumer E-commerce Behavior
The use of websites and e-commerce applications for marketing and sales promotions for sales products and services and customer service enhancements all contribute to developing a company's image and reputation. Because the website is user-friendly and straightforward, customers are more likely to regularly keep it and return to it. It indicates the success of a website. It reflects and directly impacts the sales and profits of a business that engages in e- commerce (Salehi, Abdollahbeigi, Langroudi & Salehi, 2012). The data mining process aids in predicting buying patterns and customer behavior concerning products and services. It is possible to increase the success of a website via the discovery and use of navigation patterns.
With the use of web server logs, the e-commerce site incorporated data from the user's most
recent purchase and their route traversal habits. Data mining provides information that may be used to forecast future user behavior, such as their traversal patterns and purchases. Data clustering may be used to similar group click-streams to identify learning behaviors in access consumers and user behavior in e-commerce sites.
4.6. Enhance Pricing Tactics
To succeed in the new market, it is complicated and challenging for new sellers and e- commerce businesses to establish themselves since consumers do not like and are not willing to purchase from new and unknown businesses with poor reputation ratings. New sellers provide the most delicate features at the lowest possible price while also providing various incentives to users and customers. Consumers browse various websites and social media apps to discover the most incredible deals from a variety of options. They base their purchasing choices on prior customer reviews and real-world pictures. Companies may look for and discover potential customers with data mining, which is made possible by the scores given by users. Data mining is used to discover patterns and algorithms that may assist organizations in developing pricing strategies.
Many customers prefer to buy from well-known vendors, so vendors work hard to retain a positive seller rating. E-commerce organizations track consumer preferences for goods, price changes and different discounts provided by the organization. Data mining may search for the best discount rate using an offer algorithm and transaction data.
4.7. Customization
Customers' requirements are identified by e-commerce organizations, which then create data mining patterns to customize and personalize the needs and desires of consumers.
Suppose e-commerce organizations provide personalized services and pay close attention to their customers. In that case, customers will be drawn to these websites. Therefore, organizations must focus on the content of their websites, the interaction process and user interface to improve the quality of the information and material presented by the organization (Soegaard, 2020). Every customer uses this website since it provides personalized services.
However, they do not return to this site because it contains much information and makes it tough to find products and services they want. Data mining makes it simple to find the correct information based on a consumer's previous search and choice by identifying patterns and relationships. An important attraction is experienced by consumers when an organization pays particular attention to them and tailors their experience to their needs. User participation, reviews, conversion rates, consumer preference, and user ratings are all used to assist businesses in creating the personalization and customization of their products and services.
Data mining focuses on developing prediction models and accurate algorithms for use in e- commerce transactions, with a particular emphasis on customization applications.
4.8. Fraud Detection and Security
E-commerce transactions are hazardous if they are not conducted with the appropriate precautions and security measures in place. They also attract hackers to commit fraud and launch attacks from third parties. If this has occurred, then e-commerce organizations must investigate fraud and hacking in real-time using weblogs, client purchase history, geographical location, social media feeds, and other data gathered from the internet, websites, and smartphone applications, among other sources. In addition, identity theft, credit card fraud, and product returns may all be detected and prevented by e-commerce companies using data analysis and data mining techniques(Delamaire, Abdou & Pointon, 2009).
Data mining collects personal and sensitive information and data from users and consumers.
For example, because of various frauds involving credit card information or insufficient security for personal information, or because consumers have experienced fraud and losses in e-commerce transactions, consumers prefer cash on delivery. If fraud occurs, data mining tools may assist in identifying the perpetrators and ensuring that personal information is kept in strict confidence at all costs.
5. Managerial and Practical Implications
A few management ramifications of this study are shown, which may be used by e-commerce organization and data mining managers to understand better how data mining can be used to assess customer reviews in e-commerce transactions. During covid-19 pandemic period, e- commerce and online shopping have become more significant, which has fueled the growth of mobile and online shopping. To improve the efficiency and efficacy of online purchasing, it is critical to evaluate customer reviews. Additionally, consumer reviews may be used to understand better market trends, consumer behavior, and consumer opinions about products and services. Consumer behavior and customer opinion, on the other hand, are challenging to study and understand. In this research, data mining managers learn about the critical role that data mining plays in producing valuable data. They also learn how to use this study to understand customer reviews better and create more valuable data. In recent years, e-commerce transactions have become increasingly popular. Most smartphone users take advantage of the mobile and internet to shop from any location. As a result, e-commerce organizations are under increased pressure to develop high-quality products or services to gain a competitive advantage in the marketplace. Customer review categorization and summarizing methods accept efficient consumer review classification and summary into different quantifiable groups. When used in e-commerce organizations, these methods aid managers in better understanding consumer behavior, buying patterns, market segmentation, and preferences of customers and users.
Because of the assessment of customer reviews, e-commerce organizations may concentrate on providing better goods and services. Data mining provides managers with information that they may use to make various choices about the general functioning of an e-commerce business.
6. Observation and Conclusion
Every organization needed useful information that considered the fundamental approach of customers and the variables that influenced the buying pattern and choices of potential consumers to be successful. Organizations are focused on knowing and understanding the actual opinions of customers related to the products, services, brand, and website content of the e-commerce organization; however, tracking every review, opinion, and thought of the consumers is a very complex and complicated process. Therefore, organizations must be able to collect valuable information from various sources, including random, enormous, vast, unstructured, and semi-structured evaluations and comments from customers and other stakeholders. Therefore, various sophisticated technologies and techniques were used by e- commerce organizations to create and discover helpful information from the massive database collected by the internet and websites.
Online transactions include the transmission of sensitive personal information. Then, to protect against the danger of third-party vendors accessing credentials, fraud, misalignment of money, and account information of the account attached in e-commerce transactions, it is essential to implement security measures. As a result, it is essential to provide appropriate security during
online transactions. The safety and security of any personal and vital information consumers provide on e-commerce websites and applications should be guaranteed by the websites and apps in question.
Clients are given a sense of significance when they get special attention and customized suggestions from an e-commerce website and specialized organizations associated with goods and brands. They are delighted, and as a result, they continue to make purchases from these websites, resulting in the organization gaining customer loyalty. Consumers may record and share their genuine opinions and evaluations, as well as information about their preferred brands, goods, and services, at this time. When a client's behavior is unfavorable, it is essential to express privacy concerns; this is especially true when personal information about the customer is revealed, communicated, or shared with others. The data security of customers' private and personal information is ensured by e-commerce organizations and websites.
Consumers and users of e-commerce organizations and websites can discuss and share their opinions on goods, services, and brands. The opinions and reviews may be expressed in text format or via digital ratings. These opinions and points of view have a significant effect on the buying patterns of customers as well as their future purchase choices. When used in conjunction with sentiment analysis, data mining technology may offer an accurate summary and categorization of e-commerce product evaluations. It can also aid in improving classification performance. It also aids e-commerce organizations in determining the efficacy of their custom domain names and URLs. Because of the expression of language in e-commerce customer evaluations, many new terms will be introduced because of emotional inclination. When it comes to data mining, it is a technology that has no understanding of emotional language or expression; it is unable to find and analyze new terms that have not been included in the conventional vocabulary of the data mining system. The data mining system will then fail to recognize this new reality, and it should be preserved. Consumer views are analyzed using Affective Analysis and Data Mining, which should be referred to as "opinion mining."
Most e-commerce organizations and websites offer information on the goods and services they are selling. In addition, it enables users to comment on and record their opinions on different goods; it provides the ability to score products on varying scales. Then, with the assistance of data mining tools and technologies, they analyze and summarize the data to make various essential choices about products and services.
It is possible to process and analyze large amounts of data using various applications and tools, which can be used to establish relationships between data sets and identify specific patterns in data. This analysis can increase the value of data and turn it into knowledge due to the development and growth of data science, data mining, and sentiment analysis. Data mining of online reviews of customers and users on e-commerce platforms and the internet, using data mining tools utilized by the organization, is thus performed.
7. Constraints and Prospective Orientation
This research examines the various factors associated with e-commerce shopping, the reviews provided by consumers during e-commerce shopping, and the various roles played by data mining in evaluating consumer reviews during e-commerce shopping, among other things.
However, there are several fundamental limitations to this research. This study solely makes use of secondary data and already completed research projects. Therefore, a future effort will be needed to understand this study area better and provide a seamless customer experience for the end-user.
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