Individual choices determine their well-being. Identification of these choices is required for the growth of organizations. Not surprisingly, various models have been proposed to discern how individuals (e.g., consumers) make choices (Ross 1979; Thaler 1985; Bettman, Luce, and Payne 1998). However, the use of analytics is unraveling patterns of consumer choice not known before. For example, it is now becoming apparent that significant life events—such as marriage, buying a new house, or divorce—lead to changes in the purchase of coffee, cereal, or beer, respectively (Duhigg 2012). Further, organizations are influencing customer choice, by leveraging the power of analytics for advertising, as organizations are integrating their advertising across mediums, tracking consumer choices and optimizing ad strategies and spends (Nichols 2013). Similarly, technologies are enabling advanced loyalty and reward programs (Starvish 2011; Corstjens and Lal 2014). Firms are using location-based mobile advertising, making offers to customers based on their geographic location (Fang, Luo, and Keith 2015). For more details on these aspects, participants may read Chapters 2,5, and 6 in Setia (2018) (available for free to the computational society premium members for the first 6 months).
The book discusses a model for human choice in the era of computational technologies.
Chapter 2 of the book discusses the foundation concepts of choice, explaining what is choice and the information processing basis for choice. The book identifies two questions: why and how technologies influence choices. Further, the chapter outlines the choice making process, accounting for errors in human choice-making.
Chapter 5 explains the logic of choice. Notably, it defines the notion of cognitive schema or a mental model that guides how human beings think and act. Further, building on the neuroscientic foundations, the chapter explains that living beings are wired to seek rewards. Choice is a mechanism to seek reward.
Chapter 6 proposes a model of choice. Building on the expectancy value theories, the chapter proposes that humans (and other living beings) make choices to realize emotions. While emotions are primary aspirations guides choices, the chapter also outlines that human aspire to enhance their consumption and increase their affordability of various transactions that enhance their emotional fulfillment.
Case Study #2 Topic Choices
Topic 1: What are the types of choices customer make while shopping, working, playing, relationship, or learning? How are firms using analytics to influence these choices? The case study may focus on any one or multiple domains or companies.
Topic 2: How are organization/s using digital advertising to market their offerings? The case may examine how organizations are using analytics to advertise their offerings to the new age consumers, by changing the content of the message, the timing of the message, or the channels and strategies used to send the message.
Topic 3: What is the role of analytics in influencing organizational mechanisms to incentivize customers to delay rewards. The study may focus on the influence on analytics on schemes for loyalty and participation in rewards programs, which encourage customers to stay with the company by delaying incentives.
Topic 4: How are firms using the power of analytics to identify what customers want and serve like-minded customer segments? How is analytics helping organizations customize offerings, to serve customers with similar aspirations?
Topic 5: Case study may highlight the challenges and opportunities in the organizational use of analytics. For example, does analytics use has any ill-effects on affordability (say jobs or income) of individuals. How are firms or individuals harnessing the power of analytics while overcoming these challenges?
Other topics: Participants are encouraged to find another topic (not listed here) in the domain of retail automation and analytics, for their case study. Please write to us at firstname.lastname@example.org, to check if the topic may be appropriate for the competition.
- Books and Research Links:
- Brynjolfsson, E., & McAfee, A. (2014a). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: WW Norton & Company.
- Edelman, S. (2008). Computing the mind: How the mind really works. Oxford, NY: Oxford University Press.
- Krugman, H. E. (1971). Brain wave measures of media involvement. Journal of Advertising Research, 11(1), 3—9.
- O’Shea, M. (2005a). The brain: A very short introduction. Oxford, NY: OUP Oxford.
- Morse, G. (2006, January 1). Decisions and Desire. Retrieved November 3, 2017, from https://hbr.org/2006/01/decisions-and-desire
- Peterson, R. L. (2005). The neuroscience of investing: fMRI of the reward system. Brain Research Bulletin, 67(5), 391—397. https://doi.org/10.1016/j.brainresbull.2005.06.01
- LeDoux, J. (1998). The emotional brain: The mysterious underpinnings of emotional life. New York, NY: Simon and Schuster.
- Other References and Publications
- 3 ways IOT is shaping consumer behavior
- How marketing changes when shopping is automated.
- 4 brands that are winning at Location-based marketing and How
- How to integrate Data and Analytics into Every part of your organization
- Data Analytics: Hyped up Aspirations or True potential
- Analytics 3.0
- Successful Loyalty through Analytics
- Targeted Ads Don’t Just Make You More Likely to Buy — They Can Change How You Think About Yourself
- Using marketing analytics to drive superior growth
- Analytics in Marketing
- How companies are using big data and analytics
- The Analytics Advantage
- Analytics: The new path to Value
- Becoming an analytics-driven organization to create value