Big Data Analytics, New Product Ideas, and Decision Making: An Abstract
As customers, operations, and supply chains produce ever more volumes of data, firms must explore new ways of extracting valuable insights to improve efficiency and efficacy of decision-making processes at the same time as reducing noise and abnormalities in data streams (Sivarajah et al. 2017). Machine learning and artificial intelligence techniques can aid the creation and design of new products and services by producing highly sensitive analytics. These analytics can increase the speed of idea generation or “creative intensity” (Erevelles et al. 2016) by providing real-time assessments of multiple offering variations and predictions of their potential market success (Lehrer et al. 2018). Furthermore, these analytics can be a source of competitive advantage in highly competitive markets that require a continuous degree of product newness. Although several studies show that various organizational processes can be optimized and automated to some degree (Bradlow et al. 2017), relatively little is understood about the value that advanced data manipulation systems bring to the decision-making process at individual, cognitive, level, in particular when choices and judgments of product creativity or innovativeness are involved. Understanding how to capitalize on augmented and data-driven decision-making processes in relation to different creative alternatives becomes essential to generate competitive advantage in this area. Individual decision makers, with their limited mental capacities, are still required to frame problems, select which data to collect, assess the robustness of the data and their sources, and decide on analytical frameworks. Most importantly, decision makers must interpret the findings within the business context in which they operate and use insights to support strategic, tactical, and operational decisions leading to the development of new market offerings. Within this context, this study seeks to understand how managers use analytics to stimulate new product and service innovations and aid creativity decisions. In particular, the study seeks to contribute to the field of marketing decision-making by providing an assessment of how machine learning and artificial intelligence affects the generation and selection of new product ideas. It also contributes to the growing multidisciplinary literature on data analytics by assessing its value from a decision-making perspective.