Intelligent customer relationship management (ICRM) by EFLOW portal

Customer relationship management (CRM) has become a strategic initiative aimed at getting, growing, and retaining the right customers. A great amount of numeric data and even more soft information are available about customers. The strategy of building and maintaining customer relations can be described with 'if… then' rules acquired from experts. Doctus Knowledge-Based System provides a new and simplified approach in the field of knowledge management. It is able to cope with tacit and implicit rules at the same time, so decision makers can clearly see the satisfactory solution (then and there). It reasons both deductive and inductive, so it enables the user to check on the model graph why is the chosen solution in the given situation most appropriate. It is upgradeable with in telligent portal, which presents the personalized (body-tailored) information for decision makers. When we need some hard data from a database or a data warehouse, we have automatic connection between case input interface and the database. Doctus recognizes the relations between the data, it selects them and provides only the needed rules to the decision maker. Intelligent portal puts our experience on the web, so our knowledge base is constantly improving with new 'if… then' rules. We support decision mak ing with two interfaces. On the Developer Interface the attributes, the values and the 'if… then' rules can be modified. The intelligent portal is used as a managerial decision support tool. This interface can be used without seeing the knowledge base, we only see the personalized soft information. ICRM (intelligent Customer Relationship Management) helps customer to get the requested information quickly. It is also capable of customizing the questionnaires, so the customer doesn't have to answer irrelevant questions and the decision maker doesn't have to read endless reports

Use data mining to improve student retention in HE - a case study

Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007). Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention

Implementation of Business Intelligence on Banking, Retail, and Educational Industry

Information technology is useful to automate business process involving considerable data transaction in the daily basis. Currently, companies have to tackle large data transaction which is difficult to be handled manually. It is very difficult for a person to manually extract useful information from a large data set despite of the fact that the information may be useful in decision-making process. This article studied and explored the implementation of business intelligence in banking, retail, and educational industries. The article begins with the exposition of business intelligence role in the industries; is followed by an illustration of business intelligence in the industries and finalized with the implication of business intelligence implementation

Using data mining to improve student retention in HE: a case study.

Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention
 (National Audition Office, 2007).
Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention

Guidelines for the analysis of student web usage in support of primary educational objectives

The Internet and World Wide Web provides huge amounts of information to individuals with access to it. Information is an important driving factor of education and higher education has experienced massive adoption rates of information and communication technologies, and accessing the Web is not an uncommon practice within a higher educational institution. The Web provides numerous benefits and many students rely on the Web for information, communication and technical support. However, the immense amount of information available on the Web has brought about some negative side effects associated with abundant information. Whether the Web is a positive influence on students’ academic well-being within higher education is a difficult question to answer. To understand how the Web is used by students within a higher education institution is not an easy task. However, there are ways to understand the Web usage behaviour of students. Using established methods for gathering useful information from data produced by an institution, Web usage behaviours of students within a higher education institution could be analysed and presented. This dissertation presents guidance for analysing Web traffic within a higher educational institution in order to gain insight into the Web usage behaviours of students. This insight can provide educators with valuable information to bolster their decision-making capacity towards achieving their educational goals

Business Intelligence Applied to Sentiment Analysis in a Higher Education Institution

Social media allows institutions to not only publicize their work and get feedback from the community about it, but also to keep in touch with their alumni network and foster conversations between the academic community. While sentiment analysis allows a better understanding of what is being said about a brand and how to improve the use of this communication platform. The main goal of the current work is to build a Business Intelligence System for a Higher Education Institution (HEI) based on content extracted from social media. So, Posts, likes, dislikes, shares, comments and number of visits were extracted from Facebook, Google Maps Reviews, Instagram, LinkedIn, Student Forums, Twitter and YouTube. With this data and the ETL process a Data Warehouse (DW) in SQL Server and 17 Dashboards in Power BI were developed. Posts that had the most likes were about reporting a death of someone from the school, the school mascot, the pandemic or welcoming new students. Overall, the weekends were the days with more interactions. Students are concerned about accommodation, transport, and the school academic offer. This analysis allows a better understanding of what is being said about this HEI and how to improve the communication strateg