Publication | Closed Access
Big Data and consumer behavior: imminent opportunities
232
Citations
43
References
2016
Year
Marketing AnalyticsBig Data RevolutionConsumer Decision MakingEngineeringData ScienceBig Data AnalyticsManagementMarketing Data ScienceConsumer ResearchBig Data ArchitectureConsumer BehaviorInformation ManagementMarketing PracticeMarketing InsightsMarketingBig Data InfrastructureBig DataBig Data Model
Big Data may shift the traditional theory‑experiment feedback loop in consumer behavior research, creating a new data culture while also facing challenges such as poor quality, unrepresentativeness, and volatility. The paper evaluates how the study of consumer behavior can benefit from Big Data, positioning itself as one of the first analyses of this evolution. It provides a conceptual overview of opportunities, emphasizing a move toward inductive data mining, A/B testing, and the use of numerous secondary data sources. Big Data can deepen understanding of every stage of the consumer decision‑making process, but managers will need new skill sets, like Big Data analytics, to leverage these insights.
Purpose – The purpose of this paper is to assess how the study of consumer behavior can benefit from the presence of Big Data. Design/methodology/approach – This paper offers a conceptual overview of potential opportunities and changes to the study of consumer behavior that Big Data will likely bring. Findings – Big Data have the potential to further our understanding of each stage in the consumer decision-making process. While the field has traditionally moved forward using a priori theory followed by experimentation, it now seems that the nature of the feedback loop between theory and results may shift under the weight of Big Data. Research limitations/implications – A new data culture is now represented in marketing practice. The new group advocates inductive data mining and A/B testing rather than human intuition harnessed for deduction. The group brings with it interest in numerous secondary data sources. However, Big Data may be limited by poor quality, unrepresentativeness and volatility, among other problems. Practical implications – Managers who need to understand consumer behavior will need a workforce with different skill sets than in the past, such as Big Data consumer analytics. Originality/value – To the authors ' knowledge, this is one of the first articles to assess how the study of consumer behavior can evolve in the context of the Big Data revolution.
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