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The Impact of Web Mining on Value Creation Research Paper

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Updated: Jul 20th, 2021

Abstract

The paper analyzed the usefulness of web mining tools in business intelligence. As a result, a case study analysis was conducted on Meteocentre.com, an online weather service center. The concept and definition of business intelligence, data mining, and techniques were discussed in the paper. Consequently, the paper examined some literature on the topic. Based on the objective of the study, the problem statement and research question were adopted. The analysis revealed the web mining supports organizations for income generation, preference, and customer satisfaction.

Introduction

The web is the central hub for social, political, and business life and contains a variety of information and applications. Web mining has turned out to be exceptionally crucial for online presence management, building adaptable websites, and service personalization. The Internet is developing as a gateway for data collection and a medium for directing business. Web mining is the extraction of engaging and valuable knowledge and valid data from relics or movements associated with the Internet. Web mining has been utilized in the past for examining immense accumulations of data.

Presently, it is connected to different areas of human discipline. Web mining is focused on valuable information from the web structure to find trends and patterns that connect clients with the web. Business managers have been interacting with the Internet to spot trends and patterns. These characteristics have been helpful in the decision-making and performance of most organizations.

Background

The Internet influences human activities and enables managers to comprehend the world and investigate it more profoundly. Through the Internet, individuals gather a great measure of data. With the enhancement of customer learning level and the difference in customer satisfaction, individuals want more differentiated items and customized services. Organizations are utilizing the Internet to get a more profound comprehension of consumer needs and to strengthen the bond with consumers. The Internet empowers organizations to offer a superior item and to upgrade collaboration with clients. In this way, more people and associations utilize the Internet. Under such conditions, information and data are generated purposefully or incidentally.

With the consistent improvement of innovation, personal reasoning is evolving. Technical viability allows individuals to break obstacles in processing large and confounded data. With the ascent of many computing innovations, data experts are creating new values from Internet traffic. These days, organizations can evaluate Internet network traffic and observe the estimation of the data from a new realm. Data mining, a suborder of software engineering is the way toward learning substantial data sets. Data mining intends to discover information from large data and change it into a logical outcome (Mebrahtu & Srinivasulu, 2017).

Such information can be used to evaluate trends, patterns, consumer needs, and the bandwagon effect, which is strategic in business intelligence. As a result, data experts uncover the hidden information on the Internet. This information refers to designs and patterns, which can be utilized to reveal incomprehensible business data (Bhanap & Kawthekar, 2015).

Research Problem and Objectives

Weather conditions are firmly bound with human life, and the weather service provides an update on climate patterns and forecasts. As a result, people are furnished with climate warnings. The adaptability of online weather services has pulled in an ever-increasing number of clients. Like the majority of other Internet-based services, customers produce many data while utilizing the online weather service. Data mining enables the online weather service agencies to find answers for enhanced service delivery and quality forecasting.

Additionally, it could help weather service organizations to maximize profit. Much has been discussed about data mining and its correlation with business intelligence. This paper seeks to use a component of data mining to evaluate the relationship with value creation. As a result, the paper adopts the web mining technique to investigate its influence on value creation. The objective of this research is to gain knowledge on data mining concepts, understand the consumer experience on services offered by the weather service organization, and identify the implication of web mining for the firm and client. Based on these objectives, the paper proposes a research question for the study.

Research Question

R1: What is the influence of web mining on user experience?

Literature Review

Business Intelligence

Business Intelligence (BI) is an idea of applying specific techniques to convert data into relevant information. Mostly, the term business intelligence has two different implications, which include human knowledge and learning. The deployment of human insight and innovations is utilized for administration and leadership in various business-related issues. Architects and software engineers still use business intelligence (Søilen, 2015).

BI is described as structures that accumulate, change, and present data from different sources while reducing the expected time to collect business data and empower their productive use in the decision-making process. BI enhances dynamic data analysis, business recovery, analysis, and interpretation of the necessities of regulatory decisions (Søilen, 2016). As demonstrated by Søilen (2016), BI focuses on a social event, process, and presents data concerning clients, competitors, business areas, innovation, and items. Fourati-Jamoussi and Niamba (2016) describe BI as a methodology that joins a progression of exercises, being driven by the specific data needs of decision-makers.

BI is a structure that changes data into information and after that into knowledge, thus upgrading the organization’s operations and management process (Kurnia & Suharjito, 2018). Consequently, business intelligence can be described as a framework and a response that causes decision-makers to fathom the financial status of the firm (Kurnia & Suharjito, 2018). BI assists managers by separating data from different assets in administration at a strategic level and business level (Kurnia & Suharjito, 2018).

Business Intelligence using Data Mining

The development of business intelligence has created new metrics for data measurement. Thus, hazard management and management strategy are influenced by business intelligence. Therefore, BI enhances business performance by utilizing data mining in various ways. The interest in more complex and sophisticated systems is growing because storage capacity develops as data increases. This uneven development relationship influences data processing time using traditional business intelligence frameworks. There are assortments of innovative data procedures that help BI frameworks to run effectively. The process of applying business intelligence for a business issue is called knowledge discovery in databases (KDD).

This procedure is crucial for effective data mining operations (Bhanap & Kawthekar, 2015). Darma (2016) studied the relationship between the quality of BI systems and the decision-making process. The author surveyed fifty-four business managers. Data were coded and analyzed using regression analysis. Based on the outcome, the author revealed that the quality of BI systems influenced the quality decision-making process. Sadiku, Tembely, and Musa (2016) discussed approaches in business intelligence.

The researchers gave an account of how organizations are utilizing business intelligence to enhance the adequacy of decision-making. Business intelligence is essential for investors to examine, and establish their brand. The authors concluded that business intelligence plays a role in a competitive environment. The primary goal of business intelligence is to empower managers to evaluate data and have the capacity to lead the investigation and effective decision-making process (Gauzelin & Bentz, 2017).

BI has become a critical plan for administrators since they are mindful of its incentive in giving an aggressive differentiator at all levels of the organization. Chen and Piani (2017) conducted a review on business intelligence with data mining applications. The paper discussed the ideas of business intelligence, data storage, and its impact on performance. A study of business intelligence systems for creating data storage links was introduced and dissected. The paper additionally synthesized a business situation to extrapolate relevant data knowledge. The authors concluded that test mining significantly influenced business intelligence.

Business Analytics

Business analytics is a noteworthy piece of business intelligence. Data mining and intelligence support business analytics. Business intelligence collects and evaluates valid information while applying the outcome to different techniques. Business analytics involves the use of abilities, innovation, and calculations of data mining. Thus, it delivers valuable data to help investors and administrators settle on better decisions concerning their investment and have legitimate control of their operations.

There are two features of business analytics; the back-end where the first utilization of data mining occurs and the front-end, which is a grouping of different data and reporting measurements. Thus, organizations that strategically execute this function to establish their core competence enhance business actions and effective operations.

Utilization of Data Mining in Business Analytics

Patil (2017) revealed that the primary engine driving the use of business analytics in businesses is DM or KDD. DM provides a perspective of previous circumstances and a comprehension of the likely future results, which can stimulate successful outcomes. Therefore, clusters are created by analyzing the clients’ conduct of trade, product choices, and buying behavior. Straightforward extrapolation is utilized to describe the concept of data mining.

Inquiries identified with data in different data programming software enable researchers to extricate valuable data (Bhanap & Kawthekar, 2015). Organizations use data mining for the development of business through the disclosure of useful examples. Thus, questions enable us to recover data of which have a pre-knowledge, while DM encourages researchers to find complex realities in the databases.

Data mining can be seen as an outcome in the development of data innovation. Data trade has witnessed several phases in the development of data accumulation, data administration, and complex information evaluation. The examination and growth in data frameworks since 1970 have advanced from stratified data frameworks to conventional database systems, data modeling instruments, and evaluating procedures (Bhanap & Kawthekar, 2015).

Besides, clients gained helpful and adaptable data access through query languages, UIs, the streamlined inquiry process, and management. Efficient systems for online dealings process (OLTP) have contributed to the advancement and acknowledgment of relative innovation as a large device for information safety, recovery, and knowledge management. Application-based data frameworks, text mining, spatial data mining, web mining, multimedia, dynamic sensor, and logical databases have also prospered (Maksood & Achuthan, 2016).

Issues related to transfer, diversification, and information sharing have been extensively considered. Therefore, acquiring information from Internet users requires effective approaches. Content-based frameworks, structure deep net querying, and weblink clicking facilitate this practical approach (Maksood & Achuthan, 2016).

Web Review

The quantity of distributed investigations associated with online weather services is restricted. The procedure is primarily to search helpful data through the Internet, which is identified with the online weather service agencies and its growth pattern. With the goal to assess the capability of the online weather service, a critical phase of the analysis is to determine the size of the online weather forecast. However, there is no metric to demonstrate the information about weather services. Therefore, researchers and weather experts rely on articles and web journals from financial gathering to discover proof to evaluate the size of online businesses.

From another angle, researchers and weather experts evaluate different organizations with the goal to find the pattern of the weather service development. During this procedure, they considered the attributes of those organizations and the emphasis of their development. Given the findings, the authors distinguished a few patterns of online weather service development. With the tremendous growth of data accessible online, the Internet is a stage for storage and information transfer. Because of the heterogeneity and unstructured nature of data on the Internet, information search is lumbering and tedious (Maksood & Achuthan, 2016).

As a result, web mining was introduced to solve the challenges of data collection and processing. Web mining is the method for using data mining strategies to separate valuable data from the Internet. Mebrahtu and Srinivasulu (2017) discussed the concept of web mining, its techniques, and approaches. The authors explored several mining techniques and suggested ways of improving value creation. The authors concluded that web mining algorithms play a unique role in data collection and analysis. Based on the study, the researchers recommended the development of the parallelization process.

Customer Journey Map

As a typical test of services, the gap between the service supplier and clients is reflected in the low consumer loyalty. The research gap can be closed when services are seen as a voyage or a cyclical progression of experiences that occur over channels (Mebrahtu & Srinivasulu, 2017).

Consequently, the consumer journey map is an instrument that centers on the client’s past and present experience after buying and using a product or service. As a result, the customer journey map can be a suitable apparatus for evaluating consumer loyalty and experience. The tool is used to create conceivable solutions to improve the value or reduce exercises that negatively affect consumer satisfaction. With the goal to gain insight into the contribution of Internet-based weather service and envision the client’s experience, the researchers mapped the user experience in the analytic tool.

Methodology

Case Study for the Analysis

The researcher conducted a semi-structured analysis of a case study. The study seeks to evaluate the influence of data mining in business intelligence. Specifically, the researcher tested the importance of web mining on user experience. The case study is Meteocentre.com, an online weather forecast service. The research solicited the consent of the participant before conducting the face-to-face interview.

The contact person for this study is the web directory of the weather service company. The research adopted the network picture to visualize the weather service operations. In this area, the techniques chosen for this exploration and data collection are highlighted. Each research technique presented is discussed, and the reasons for emphasizing the procedures were explained. Because of the research question and significance, this paper represents three exploration phases.

The first phase seeks to provide a review of the literature on data mining and its implications on business intelligence. The next step of this research is to conduct the analysis of the weather service and to clarify the correlation between web mining and user experience. The researcher will then combine results from the review of works of literature and analysis to provide proof of the capability of data mining for the Internet-based weather service organization.

Experiment Design

Data mining is a procedure for removing the concealed, secret, and essential knowledge from the gigantic, fragmented, and arbitrary data (Mebrahtu & Srinivasulu, 2017). From an innovation point of view, the expression “data mining” contains a few layers of importance that are summarized below.

  1. The data source must be available and copious.
  2. The reason for data mining is finding client intrigued information.
  3. The outcome is satisfactory, conceivable, and usable.
  4. The result applies to specific circumstances and cannot be generalized.

DM aims to find knowledge and information mostly associated with relationships and trends in the area of data mining (Mebrahtu & Srinivasulu, 2017). Data experts analyze the information as the premise to extricate knowledge from the database. The sampled data could be structured, semi-structured, and special-shaped. Structured data include information on relational databases. However, semi-structured data include pictures, text, and comparative data. Data mining can be extricated by utilizing distinctive ways, which include science-based techniques, deductive strategies, and inductive techniques.

Thus, data mining can be connected to different fields, which include data administration, inquiry improvement, decision process, data management. In this way, DM is an interdisciplinary innovation that gives the researcher an extraordinary capacity to process large data, which lifts the data application from a fundamental level to a more extensive field. It is essential to note that data mining would not find new scientific hypotheses, arithmetic equations, or mechanical hypotheses (Adeniyi, Wei, & Yongquan, 2016).

Result Analysis

User Experience

With the consistent improvement of innovation, personal reasoning is evolving. Technical viability allows individuals to break obstacles in processing large and confounded data. With the ascent of many computing innovations, data experts are creating new values from Internet traffic. These days, organizations can evaluate Internet network traffic and observe the estimation of the data from a new realm. Data mining, a suborder of software engineering is the way toward learning substantial data sets. Data mining intends to discover information from large data and change it into a logical outcome. The consumer experience refers to the recognition and reactions from the utilization of a product or service. In another word, consumer experience mirrors the client’s inclination. Based on the study of relative data, the findings will be summarized below.

  1. The weather service users rely on brand awareness using feedbacks and website ranking. The primary motivation of customers is awareness and to satisfy their needs.
  2. Website layout plays a role in the user experience. As a result, the weather service companies share resources between online marketing and content development. Content development significantly influences the user experience.
  3. Visual experience creates a long-lasting impact on customers. The site appearance is considered as a direct indicator of a cooperating user. The visual experience is not exclusively the firm’s online presence, and it incorporates the structure, style, shade pattern, page format, page estimate, text dimension, and brand logo. It is exceptional in conveying the value of a website through visual experience. Two structure patterns of visual experience cannot be overlooked. The structures include web blueprint and content rendering. A responsive web configuration is a design approach that makes the site compatible with many devices. As a result, a user would have the same experience when surfing from a mobile phone, desktop, laptop, and other communication gadgets. In this way, it is essential to create a variant online presence for all devices. The content rendering alludes to a simple layout and rich media content. Text content is a vital carrier of Internet data; however, the media content is more creative and eye-catching. The media content contains a more noteworthy measure of data than the text layout (Chen & Piani, 2017). For online weather services, these two patterns should be considered during the formation phase.

The Influence of Web Mining on User Experience

Web mining gives dominant methods to assist individuals to understand the user experience by finding customer’s behavior patterns. These patterns can be utilized to assess the user experience. Using conventional strategies such as statistical analysis and data capture techniques, researchers could gather user information, but it is difficult to evaluate the user experience. Consequently, the online weather service organization needs viable strategies that empower them to find hidden knowledge. Approaches like surveys and interviews can assist organizations with obtaining the user experience; however, the process is costly and time-consuming. Consequently, the feedback strategy could be used to collect data, but its usefulness is restricted.

The organization needs a considerable measure of time with the goal to discover valuable data from the analysis. Data mining innovation gives appropriate and productive techniques to understand the user experience. Given the discoveries of data mining, web developers can improve customer experience and deliver more customized services. Web mining techniques give helpful knowledge to enhance the user experience. The clustering method assists organizations by segmenting valued users to offer them customized content based on individual attributes. Web mining improves the connection structure of the website and its content design.

Conclusion

The Internet influences human activities and enables managers to comprehend the world and investigate it more profoundly. Through the Internet individuals, gather a large measure of data. With the enhancement of the customer information level and the difference in customer satisfaction, individuals want more differentiated items, customized services, and lower cost of products. Web mining has turned out to be exceptionally crucial for online presence management, making versatile websites, business, and service personalization. The development of business intelligence has exposed metrics for data measurement.

Thus, hazard management and management strategy are influenced by business intelligence. Therefore, BI enhances business performance by utilizing data mining in various ways. The interest in more complex and sophisticated systems is growing because storage capacity develops as data increases. This development influences data processing using traditional business intelligence frameworks. This paper has identified vital areas that improve customer experience. However, factors that affect the user experience are not limited to data mining. Therefore, future work should focus on these factors. It is essential to study a new model for web mining and knowledge learning.

References

Adeniyi, D., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-nearest neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90-108. Web.

Bhanap, S., & Kawthekar, S. (2015). Data mining for business intelligence in social network: A survey. International Advanced Research Journal in Science, Engineering and Technology, 2(12), 129-131. Web.

Chen, J., & Piani, N. (2017). Research on business intelligence with data mining applications. International Journal of Business and Economics Research, 6(2), 19-24.

Darma, J. (2016). The effect of quality of business intelligence system on quality of decision making (survey on financial institutions in Medan city: North Sumatera. European Journal of Accounting, Auditing and Finance Research, 4(5), 49-56.

Fourati-Jamoussi, F., & Niamba, C. (2016). An evaluation of business intelligence tools: A cluster analysis of users’ perceptions. Journal of Intelligence Studies in Business, 6(1), 37-47.

Gauzelin, S., & Bentz, H. (2017). An examination of the impact of business intelligence systems on organizational decision making and performance: The case of France. Journal of Intelligence Studies in Business, 7(2), 40-50.

Kurnia, P., & Suharjito, S. (2018). Business intelligence model to analyze social media information. Procedia Computer Science, 135, 5-14. Web.

Maksood, F., & Achuthan, G. (2016). Analysis of data mining techniques and its applications. International Journal of Computer Applications, 140(3), 6-14.

Mebrahtu, A., & Srinivasulu, B. (2017). Web content mining techniques and tools. International Journal of Computer Science and Mobile Computing, 6(4), 49-55.

Patil, P. (2017). Application for data mining and web data mining challenges. International Journal of Computer Science and Mobile Computing, 6(3), 39-44.

Sadiku, M., Tembely, M., & Musa, S. (2016). Toward better understanding of business intelligence. Journal of Scientific and Engineering Research, 3(5), 89-91.

Søilen, K. (2015). A place for intelligence studies as a scientific discipline: Halmstad Sweden. Journal of Intelligence Studies in Business, 5(3), 35-46.

Søilen, K.S. (2016). A research agenda for intelligence studies in business. Journal of Intelligence Studies in Business, 6(1), 21-36.

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