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Business analytics
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Business analytics refers to the process of examining an organisation’s data to measure past and present performance to gain insight that will help with future planning and development. It includes various types of analytics including descriptive, predictive, prescriptive and diagnostic (see Chapter 10). Business analytics also refers to the set of skills, tools, software and statistical analysis techniques that are used to capture, explore, analyse and visualise data, along with models that can help to make predictions about future performance. It can help businesses to learn from past mistakes, refine their business strategy, develop and improve products or services, make comparisons with competitors, improve public relations and retain staff, for example. Business analytics can also draw on insights from customer analytics, which uses customer behaviour data to develop models and drive decisions concerning customer transactions, engagement, satisfaction, relation management, development and retention (this can also be referred to as customer relationships management analytics or CRM analytics). Bijmolt et al. (2010) provide a detailed discussion for those who are interested in analytics for customer engagement. A related method is marketing analytics, which seeks to use customer data to offer more value to customers, increase customer loyalty, enhance customer experience, drive marketing activities and improve company profits, for example. If you are interested in finding out more about marketing analytics, useful information is provided by Grigsby (2015) and Winston (2014), whereas a critical examination of marketing analytics is provided by Wedel and Kannan (2016).
Evaluating analytics DSS for the COVID-19 pandemic through WHO-INTEGRATE EtD for health policy
Published in Journal of Decision Systems, 2022
Manuel Mora, Fen Wang, Gloria Phillips-Wren, Jorge Marx Gomez
In the practitioner analytics literature, analytics is described succinctly as the business organisation’s ‘ability to collect, analyze, and act on data’ (Davenport, 2006, p. 1). This description advances the Business Intelligence (BI) concept where the business deploys a strategic, centralised approach to gathering and analysing data compared to a distributed approach across the organisation. A similar brief description of analytics is ‘the scientific process of transforming data into insight for making better decisions’ (INFORMS, 2019). Similarly, Power et al. (2018, p. 51) defined Business Analytics (BA) as ‘a systematic thinking process that applies qualitative, quantitative, and statistical computational tools and methods to analyze data, gain insights, inform, and support decision-making.’ Any analysis may use a variety of techniques including diagnostic, descriptive, predictive, prescriptive, and optimisation models. Thus, BI and analytics could be described as data-driven decision support (Power, 2002).
Combining biased regression with machine learning to conduct supply chain forecasting and analytics for printing circuit board
Published in International Journal of Systems Science: Operations & Logistics, 2022
Business analytics has become a popular research issue in operations management (Addo-Tenkorang & Helo, 2016; Oliveira et al., 2012; Wang et al., 2016; Zhong et al., 2016). Typically, it consists of four modules (Albright & Winston, 2016; Camm et al., 2016; Chen et al., 2012): (1) Descriptive analytics gains insight to ask ‘what happened in the past’. (2) Diagnostic analytics explores the hidden factors to ask ‘why did it happen’. (3) Predictive analytics employs proactive modelling to forecast ‘what will happen in the future’. (4) Prescriptive analytics uses quantitative optimisation and stochastic simulation to support ‘how can we make it happen or avoid bad outcomes’. Business analytics is powerful to assist supply-chain practitioners in accomplishing data-driven decision-making in sourcing, planning, making, scheduling, delivering, recycling, and forecasting (Oliveira et al., 2012; Trkman et al., 2010; Wang et al., 2018). In modern supply chain management, a firm cannot make decisions independently without considering its upstream suppliers (material vendors) or downstream retailers (product sellers). Thus, unlike previous studies focusing on the optimisation of internal resources, this research helps practitioners to quantify the impacts of external markets (downstream consumer products) on upstream PCB manufacturers.
Mapping social media analytics for small business: A case study of business analytics
Published in International Journal of Fashion Design, Technology and Education, 2021
Previous research proposed three types of business analytics methods based on the level of complexity (i.e. descriptive analytics as the simplest analytics and prescriptive analytics as the most complex analytics). This research proposed business analytics methodology as a procedure to generate prescriptive analytics, instead of categorical methods with a level of complexity (Proposition 1). Some KPIs in the Facebook Insights are generated by descriptive (e.g. a majority of customers’ age range) or predictive analytics (e.g. paid-advertisements will increase Reach from t-test) which provides a guideline to perform prescriptive analytics (e.g. more paid-advertisements targeting the major consumer profile). Other KPIs are generated by descriptive analytics but provide the trends of the KPIs at the same time. For example, the description of total minutes viewed of short videos per day (Descriptive analytics) simultaneously shows the trend of this KPI over time when connecting the data descriptions of each day (Predictive analytics), which directs a future marketing strategy (Prescriptive analytics). Therefore, business analytics methods were an order of a process, instead of categories of complexity.