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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).
Managing supply chain resources with Big Data Analytics: a systematic review
Published in International Journal of Logistics Research and Applications, 2018
Marcelo Werneck Barbosa, Alberto de la Calle Vicente, Marcelo Bronzo Ladeira, Marcos Paulo Valadares de Oliveira
Integrating customers into the Product Development process reduces time to market. It involves determining sources for ideas and even considering incentives for developing new products (Croxton et al. 2001). The adoption of BDA in this context has been possible due to the large amount of customers’ data that can be analysed to generate insights to new products. Customers generate a plethora of data when they interact with service providers and when they buy products either in physical or online stores. The data that portrays their behaviour and preferences have been subject to analysis by what has been called Customer (or Consumer) Analytics, Big Data Customer Analytics and other variations. The study of Consumer Analytics lies at the junction of Big Data and consumer behaviour. The capabilities achieved through consumer insights obtained from Big Data facilitate value creation in marketing activities due to improved decision-making (Erevelles, Fukawa, and Swayne 2016). The integration with clients not only supports the development of new products but also allows the company to better understand their customer base and, as a consequence, with the aid of BDA, it is possible to offer additional services in conjunction with the product.