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.

Enhanced business and inventory management systems

Abstract: Various methods and systems such as in a software application are presented which may include automated purchase recommendations based on amalgamated purchase constraints (4) for business inventory maintenance, notation of events through noted event entry portals (31) for business related observation recordation, automatic facilitation of regular debut of initial suggested industry tied information (50), presentation of business performance data (65) and disparate substantially redundant business performance data (67) in a business performance display (66) for a summary review of business operations, and easy selection of inventory items (83) from a recommended hierarchical categorization of inventory items which may be used to create buying reports, ad hoc reports, and the like.

Adaptive big data analytics for deceptive review detection in online social media

Abstract: The explosive growth of user-contributed reviews in e-Commerce and online social network sites prompts for the design of novel big data analytics frameworks to cope with such a challenge. The main contributions of our research are twofold. First, we design a novel big data analytics framework that leverages distributed computing and streaming to efficiently process big social media data streams. Second, we apply the proposed framework that is underpinned by a novel parallel co-evolution genetic algorithm to adaptively detect deceptive reviews with respect to different social media contexts. Our experiments show that the proposed big data analytics framework can effectively and efficiently detect deceptive reviews from a big social media data stream, and it outperforms other non-distributed big data analytics solutions. To the best of our knowledge, this is the first successful design of an adaptive big data analytics framework for deceptive review detection under a big data environment.

Predictive Analytics in Higher Education

Abstract: Higher education institutions have multiple technologic subsystems for administrative, pedagogical management, and quality purposes, which gather an immense volume of data from various sources and which do not analyzed. The domain of the analytic performances in education emerges from the need to aggregate multiple sources of data, which the selected information will make it possible to understand reality and optimize management actions. Using predictive analytics has several key benefits, including better future business performance, more insight into business dynamics, and optimum use of available data. Higher education has access to realms of data which can be used to improve decision making. Predictive analytics is "an area of statistical analysis that deals with extracting information using various technologies to uncover relationships and patterns within large volumes of data that can be used to predict behaviour and events." Analytics is the process of discovering, analyzing, and interpreting meaningful patterns from large amounts of data. The importance of predictive analytics lies in its ability to suggest the most favourable planning for the future by combining data (1). Higher education is a relatively late adopter of predictive analytics as a management tool. Predictive analytics has been used in other industries for many years, especially in the area of assessing consumer behavior (7). For instance, automobile manufacturers and dealers use predictive analytics to assess the likelihood of a customer who leases a car to either purchase the vehicle or choose to lease a new vehicle at the end of the lease. Using predictive analytics, BMW might extend an offer for a no-penalty early lease termination to select customers who the data suggests are likely to move to Mercedes Benz or Audi, if they agree to a new BMW lease. In this scenario, offers would not be made to customers who the data suggest are predisposed to continue with BMW; instead, they are designed to generate repeat business from those most likely to defect. In a similar way, colleges and universities can deploy predictive analytics to determine which students are most at risk for attrition and - armed with deep, historical data - craft segment- specific retention strategies designed to compel them to persist toward degree completion. Believes that predictive analytics, while by no means a "silver bullet" to solve all of higher education's problems, is an important tool that can positively impact efficiency and effectiveness across the academy. To better understand predictive analytics, the researcher includes interviews with practitioners from a variety of institutions, including public and private four-year universities and a community college. Who are analytics thought-leaders or who at least had experience in its implementation? (9)(10). This paper is designed to explain predictive analytics, followed by a look at how it can impact activity at the highest levels of institutional management. We provide examples of how predictive analytics has been used at a variety of institutions, including a review of its potential pitfalls and benefits. This paper provides concrete examples of how predictive analytics has led to measurable performance improvements. Finally, recommends to all colleges and universities to consider building predictive analytics into their toolbox of techniques that inform and enable evidence-based decision-making.

Mining Business Opportunities from Location-based Social Networks

Abstract: Urbanization's rapid progress has modernized a large number of human beings' lives. This urbanization progress is accompanied by the increase of a variety of shops (e.g., restaurants and fitness centers) to meet the increasing citizens, which means business opportunities for the investors. Nevertheless, it is difficult for the investors to catch such opportunities because opening what kind of business at which place is not easy to decide. In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district. Specifically, we exploit the data from location-based social networks (LBSNs) to mine the business opportunities, guiding the business owners to open new commercial shops in certain categories at a particular area. First, we define the properties of a business district and propose a greedy algorithm to partition a city into different districts. Next, we propose an embedding model to learn latent representations of categories, which captures the functional correlations among business categories. Furthermore, we propose a ranking model based on the pairwise loss to recommend categories for a specific district. Finally, we conduct experiments on Yelp data, and experimental results show that our proposed method outperforms the baseline methods and resolves the problem well.

Development of an electronic-business planning model for small and medium-sized enterprises

Abstract: Many companies want to conduct more business and improve performance using electronic, computer-based and telecommunication-enabled business processes. However, many small and medium-sized enterprises (SMEs) have little understanding of, and cannot comprehend, future technological developments. This paper presents the findings of a research project funded by the Centre for Enterprise in 2000–2001 into developing an e-business planning model for SMEs. The model architecture is top-down in the sense of moving from a high-level strategic appraisal of e-business opportunities in level 1 to the selection of particular e-business technologies in level 4; but it is also critical for a company to integrate the different levels of decision-making from the identification of business value to the implementation of relevant technologies in order to facilitate the delivery of products/services to customers. The paper also reports the findings of a case study conducted in a SME in order to illustrate how the e-business...