Customer Behavior Analysis Using Rough Set Approach

Dhandayudam, Prabha; Krishnamurthi, Ilango

doi:10.4067/s0718-18762013000200003

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…This research uses several aspects or attributes such as teaching learning, tutoring, and consulting, research and community service, and other tasks beyond the main task as a consideration in judgment Subsequent research that utilizes the Rough Set method, which uses a variable of education, academic position and Department of office is then used to determine the desired rector candidate in accordance with the existing provisions. The results of the study show that education is a major factor in the election of foreign rector which is then supported by the academic position and structural department owned by the lecturer (Chen et al, 2020;Turčínek and Turčínkova, 2015;Dhandayudam and Krishnamurthi, 2013;Shyng et al, 2007;Suchacka and Chodak, 2017;Sembiring and Azhar, 2017;Hadiansah, 2017;Wang and Chou, 2013;Sanny, Susastra, Roberts, and Yusramdaleni, 2020).…”

Section: Introductionmentioning

confidence: 88%

“…An increase in the number of customers who stay this can happen if customers are satisfied with the service facilities offered by the company. Services that do not meet the quality will be very easily abandon and eventually the customer will switch to another company (Yakut, Turkoglu, and Yakut, 2015;Dhandayudam and Krishnamurthi, 2013;Salajegheh, 2016;Li, Tang, Luo, and Xu, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Istianingsih¹,

Defit²

2021

IJEBA

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Purpose: The research aims to determine how the level of customer satisfaction in a small business is a field of flower arrangement services as well as information that can dug from a collection of customer data. Approach/Methodology/Design: The samples used were ten customers who considered loyal. The method used is a Rough set with a satisfaction attribute consisting of confidence, integrity, pride, and desire. Findings: Based on this decision rule, confidence becomes the most dominant attribute. The results showed that the four dimensions of the emotional bond used as an attribute in the Rough Set process to analyze the level of customer satisfaction that strongly affects is the dimension of Confidence, this is evident from the resulting General Rule. Every decision produced always uses the confidence dimension in the comparison input, meaning the level of customer confidence in the company should be a special concern. In the dimensions of the emotional bonds the first dimensions are constructed and fundamental is the confidence dimension, this dimension indicates the level of customer confidence in the company. Confidence dimensions cannot stand it self yet to build long-term relationships with customers without by other dimensions. Practical Implications: The study will contribute positively to give some information of knowledge that contained on customer satisfaction. It shown the alternative for measuring for satisfaction that can be used for a small business and academic. Originality/Value The results generated in this study opened new avenues for customer satisfaction analysis. We must not limit the analysis of customer satisfaction survey data using conventional statistical methods. The Rough theory Set is an innovative tool for finding Knowledge of customer behaviour patterns.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Istianingsih¹,

Defit²

2021

IJEBA

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“…For example, rough sets theory [12], [13], [4] maybe efficient to discover hidden information in data and to explore the rules and characteristics of customer churn. The decision rules can be transferred into a flow network graph to represent the connections of pathways and the degrees of their interdependency [20].…”

Section: Data Mining Modelsmentioning

confidence: 99%

“…n.estimators =500, learning.rate=0.08, max.depth=5, loss='ls'; 4. glmNet in R: family = 'binomial', alpha = 0, lambda = 2 12 ;…”

Section: Regulation Parametersmentioning

confidence: 99%

Prediction of the Shoppers Loyalty with Aggregated Data Streams

Nikulin

2016

Journal of Artificial Intelligence and Soft Computing Research

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Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. While dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers’ transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.

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“…PPV is not confined to medical tests, however. It can be used to assess the predictive ability of dogs to sniff out drugs or explosives in airports, cadaver dogs' ability to sniff out chemicals associated with the post-mortem state [275], the predictive ability of fortune tellers and psychics, whether death certificates accurately reflect cause of death in workplace homicide victims [276], in the jet-manufacturing industry [277] retrospective analysis of occupational exposure [278], in industry in general [279], business in general [280], ergonomics [281], customer behavior analysis [282], nutrition science [283], in computer science in general [284]- [287], for testing programs to catch spam [288], and to test whether a sobriety test has a high enough predictive value to evaluate driver behavior [289]. Indeed these simple statistics have been used to evaluate virtually any model, test, practice, method, or entity in any area.…”

Section: Where Does the Bailey Et Al Study Rank In Importance For Prmentioning

confidence: 99%

The Ethical Implications for Humans in Light of the Poor Predictive Value of Animal Models

Greek¹

2014

IJCM

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The notion that animals could be used as predictive models in science has been influenced by relatively recent developments in the fields of complexity science, evolutionary and developmental biology, genetics, and evolutionary biology in general. Combined with empirical evidence, which has led scientists in drug development to acknowledge that a new, nonanimal model is needed, a theory-not a hypothesis-has been formed to explain why animals function well as models for humans at lower levels of organization but are unable to predict outcomes at higher levels of organization. Trans-Species Modeling Theory (TSMT) places the empirical evidence in the context of a scientific theory and thus, from a scientific perspective, the issue of where animals can and cannot be used in science has arguably been settled. Yet, some in various areas of science or sciencerelated fields continue to demand that more evidence be offered before the use of animal models in medical research and testing be abandoned on scientific grounds. In this article, I examine TSMT, the empirical evidence surrounding the use of animal models, and the opinions of experts. I contrast these facts with the opinions and positions of those that have a direct or indirect vested interest-financial or otherwise-in animal models. I then discuss the ethical implications regarding research constructed to find cures and treatments for humans.

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Customer Behavior Analysis Using Rough Set Approach

Abstract: This paper is available online at www.jtaer.com DOI: 10.4067/S0718-1876201300020000

Cited by 15 publications

References 35 publications

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Prediction of the Shoppers Loyalty with Aggregated Data Streams

The Ethical Implications for Humans in Light of the Poor Predictive Value of Animal Models

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