Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Social media data analytics for business decision making system to competitive analysis

Abstract: For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%.

Social media data analytics for business decision making system to competitive analysis

Abstract: For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%.

Innovations in Experimental Learning – A Study of World Top Business Schools

Abstract: Innovations in higher education include improvements in existing pedagogies and developing new pedagogies based on the subject to be taught. Depending on the age, gender, regional background, the effective teaching and learning methods may be different. Business schools are providing higher education to improve the necessary skills of the student either to start new a business or to manage an existing business effectively by means of making effective decisions on a business problem by considering the business environments effectively. Experimental learning is a new pedagogy in business management learning method where students are exposed to real business problems and made them as part of decision-making team. Such exposure in real-world problems will improve the risk taking ability and the confidence of the students while becoming an entrepreneur after their graduation. Based on the success of experimental learning method introduced in many business schools, the method is becoming more and more popular and is finding a place in the pedagogy of many top level Business schools in the world. In this paper, we have made an empirical study on adopting experimental learning scenario in some identified top business schools in the world. We have collected information from the website of top 25 Business schools based on recently announced B-school ranking and studied their effort and results of such method adoption in a curriculum.

Methods and system for providing real-time business intelligence using search-based analytics engine

Abstract: The methods and systems for providing real-time business intelligence using search-based analytics engine facilitate a user to input a natural language query with regard to business analytics and obtain an analytics report in response without the user aggregating or processing raw data from a database query. Such business intelligence platform may receive a data analytics request including a user-desired data variable via a user interface; receive, via the user interface, user-configured parameters identifying a user-selected data source and a user-defined data set; form the user-defined data set based on user-configured parameters; query the user-defined data set to obtain a query result of the user-desired data variable; and generate a user interactive graphical representation of the query result of user-desired data variable.