Multi-Criteria Decision Making Application in the Education Context

Business schools are confronted with a challenge of developing students to be managers, focused on productivity and adding value at the work process. 21st century education should aid student population substantially in thinking beyond profitability and self-interest and lead their strategical thinking process towards sustainable development. Our aim is to help the teaching staff in business education by providing them the tools to understand their students’ decision-making process and preferences. The goal of the study is to investigate if modern technologies support responsible decision making of students. A quantitative study was carried. The tool used was Super Decisions Software. Our results show that technology, even though a potentially useful tool in the responsible decision-making process needs integration into the appropriate business models. The sample of students’ behaviours in decision-making process can also be identified as responsible. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Credit Ranking of Bank Customers (An Integrated Model of RFM, FAHP and K-means)

In this paper, with the aim to rank customers in terms of credit, three patterns namely Hsieh (RFM), FAHP, and K-means were integrated. The main effective factors on ranking customers including transactions, repayment and RFM (Recency, Frequency and Monetary) variables were defined. For classifying the legal customers of Export Development Bank of Iran in terms of credit, 5 variables were extracted from the bank’s database and normalized accordingly. The weight of each variable was calculated through interviewing bank experts using FAHP. Using the values of the variables and K-means algorithm, the optimal clusters of customers were determined. Finally, bank customers were ranked in 5 credit clusters and the value of each cluster was estimated. According to the findings, recency, repayment behavior, transaction, frequency, and monetary variables had maximum effects on customers’ ranks, respectively. Therefore, 54% of the customers fell in the third cluster (with cluster value of 0.95) and the fifth cluster (with cluster value of 0.76) composed of good and very good customers. Credit risk of the two clusters (especially the third one) was at least. 32% of the customers positioned in the second cluster (with cluster value of 0.59) including the average customers in terms of credit. 14% of the customers fell in the fourth and first clusters with cluster values of 0.42 and 0.26 including highest risky customers

A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services

Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion

Investigating Two Customer Lifetime Value Models from Segmentation Perspective

AbstractCustomer lifetime value has been a topic of interest for some years upon which plenty of academics and marketing managers have been dwelling. The topic plays a key role in customer relationship management and has been implemented in variety of sectors. The main goal of customer lifetime value is to specify the importance level of each customer for a company. Such questions as what sort of marketing strategies should be preferred for which customers, how much investments should be made for them and which marketing campaigns should be followed can all be determined by calculating lifetime value of customers. Many researchers have proposed different types of models for calculating customer lifetime value. Yet, the related literature lacks of comparative research on assessing the existing models, especially within the scope of segmentation. This paper aims at providing a classification for the current models in the literature based on their basic characteristics and making a comparison between two representative models from different classes using the same database. An evaluation from segmentation perspective was done and the results indicated that the model that represented the future-past customer behaviour model class was found to be superior than its peer using the same database and variables

Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations

Mining Implicit Patterns of Customer Purchasing Behavior Based On The Consideration Of RFM Model

Association rules have been developed for years and applied successfully for market basket analysis and cross selling among other business applications. One of the most used approaches in association rules is the Apriori algorithm. However the Apriori algorithm, has long known for its weaknesses that generate enormous amount of rules and alreadyknown facts. In this study, we integrate the RFM attributes with the classical association rule mining, Apriori. Based on RFM model, two indicators, RF score and Sale ratio, are used as measure of interestingness. We propose two algorithms, DWRF and DWRFE, to mine for implicit pattern. In our experimental evaluation, the performance of Apriori, DWRF and DWRFE are compared. The result of our algorithms offers an effective measurement of interesting patterns. Moreover, the DWRF algorithm that uses the RF score as a measure of interestingness seems to be able to promptly reflect the fast-changing customer’s purchase patterns

The Application of Target Analysis in Electricity Demand-Side Management

Recently, target analysis combined with database technology and data mining has been widely used in industries such as marketing, finance, insurance, telecommunications, advertising, and e-commerce. Because of the unique complexities of user behavior in electricity demand, examples of target analysis applications have yet to be seen. Considering the industry’s urgent need to enhance the efficiency of electricity demand-side management, this study aims to build a mining analysis model for potential target users of interruptible load that both fully reflects consumer behavior characteristics and serves as a rule for static comparisons. The results of a data mining analysis of the Taiwan Power Company (Taipower)’s interruptible loads 1 to 6 show that the number of potential target users is 1669, which is 21% of the original mining population. Additionally, the target users who were classified to have “the most potential” for all categories of interruptible load only accounted for 0.76% of the total mining population (= 59/7814), verifying the mining effects

A Fuzzy Linguistic RFM Model Applied to Campaign Management

In the literature there are some proposals for integrated schemes for campaign management based on segmentation from the results of the RFM model. RFM is a technique used to analyze customer behavior by means of three variables: Recency, Frequency and Monetary value. It is s very much in use in the business world due to its simplicity of use, implementation and interpretability of its results. However, RFM applications to campaign management present known limitations like the lack of precision because the scores of these variables are expressed by an ordinal scale. In this paper, we propose to link customer segmentation methods with campaign activities in a more effective way incorporating the 2–tuple model both to the RFM calculation process and to its subsequent exploitation by means of segmentation algorithms, specifically, k-means. This yields a greater interpretability of these results and also allows computing these values without loss of information. Therefore, marketers can effectively develop more effective marketing strategy

A fuzzy expert system (FES) tool for online personnel recruitments

The advent of the internet has facilitated greater access to the myriad of job opportunities available globally. Currently there exist many job application submission portals that are being used for online job recruitment purposes. However, the task of many of these job submission portals is limited to matching the professional and academic qualifications of applicants with the requirements of employers and several organisations and does not involve the ranking of applicants’ credentials according to their relative suitability for the jobs applied for. In this paper, we describe the implementation of fuzzy expert system (FES) tool for selection of qualified job applicants with the aim of minimising the rigour and subjectivity associated with the candidate selection process. A performance evaluation of the FES tool that was conducted confirmed the viability of a FES-based approach in handling the fuzziness that is associated with the problem of personnel recruitment