Case Study #1: Retail Analytics and Automation of Choice (e.g., through Internet of Things (IOT))

Retailing is one of the oldest human endeavor, and various civilizations–such as the Roman Empire–have organized activities to connect retailers and consumers (such as through Trajan markets) (Richard 2010). Throughout history, many technologies (such as the point-of-sale scanners or barcodes) have transformed retail. Modern technologies are catalyzing analytics and automation at unprecedented scales. For example, retailers are using such technologies as the Internet of Things (IoT) to automate their operations, influencing how retailers offer and customers choose (Gregory 2014). Many choices in retail are being automated, as smart kitchens are projected to revolutionize ordering, enabling machines (such as refrigerators) to make choices on behalf of the consumers (Yurieff 2017). Analytics is being used to offer discounts, analyze queues, send assistance within retail stores, check real-time on-shelf availability, and other retail operations. In general, contemporary computational technologies are automating many choice decision, becoming the secondary choice-makers, by aiding human (the primary choice-makers’) decision-making (Setia 2018). The case study may address one of the topics below, related to automation of choice. Specifically, the case study may:

Case Study #1 Topic Choices

Topic 1: Identify consumer choices in retail. What are the new analytics and automation technologies influencing these choices? How are retailers adapting operations, to accommodate the advent of these technologies?

Topic 2: Identify errors in choices that lead to a loss in value (such as due to return costs) for the retailer or the consumer. These errors may arise due to time constraints for human beings. For example, billions worth of gift cards goes unused every year (Tuttle 2012). How are analytics or automation based technologies enabling retailers to overcome these errors?

Topic 3: Complementing human choice makers: Identify scenarios where analytics and automation technologies are complementing human information processing and choices in retail . What are the advantages of using these technologies for retailers or consumers?

Topic 4: Substituting human choice-makers: Identify substitution of human activities through analytics and automation technologies in retail. Projects indicate computational technologies, such as drones or autonomous cars, will substitute human activities. What are the advantages to retailers or consumers, in using computational technologies that substitute their activities? Are there any negative implications of using these computational technologies?

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, to check if the topic may be appropriate for the competition.


Participants are advised to start by reading the book:

How Computational Technologies Influence Choice: A Neuroscientific Perspective Part 1. By Dr. Pankaj Setia
Accessible at ,, and at other retailers. Participants may write to for a free copy!

Other Rules

  • The project submissions must entirely be the work of the individual or the project team. While faculty and other individuals can help review the submission, they should not contribute to the content of the report or the solution.
  • Incomplete submissions will not be considered, so make sure you have all of your submission deliverables in the submission package.
  • The contest materials must be submitted by the due dates. Late submissions will not be accepted and no extensions will be given.
  • For participating in the second stage of competition, teams and individuals must be members of an AIS Student Chapter.


References (also see more under the case 1 on the[1]):

Gregory, Jonathan. 2014. “The Internet of Things: Revolutionizing the Retail Industry.” 2014.

Richard, Carl J. 2010. Why We’re All Romans: The Roman Contribution to the Western World. Rowman & Littlefield Publishers

Tuttle, Brad. 2012. “$2 Billion of Unredeemed Gift Cards Go Unused in 2012 | TIME.Com.” 2012.

Yurieff, Kaya. 2017. “In the Future, Your Kitchen Will Think for Itself.” 2017. [1]For access, write to

Additional Resources

  • Please visit the competition page on The Computational Society ( for more details about the challenges.

How to Get and Process Data

Creating Maps

Visualization and Infographic tools resources

You may access some tutorials from to do the case study


How entries will be evaluated

All entries will be evaluated by the judges on two major criteria: content and presentation. Judges will evaluate the case studies based on the following:

  • Clarity (how well the case study explains the topic)
  • Novelty/creativity (originality of thought)
  • Insight (how well the case study uses graphic aids to explain the data)
  • Utility (what is the value of case study to firms or society)
  • Completeness (degree to which the case study answers the chosen topic)
  • Depth (sophistication of the logic or analysis)
  • Consistency (conclusions consistent with the logic or analysis)

The points will be awarded based on the following: 

  1. Context (explaining what phenomenon does the case study examine) (20%)
  2. Problem statement (identifying the problem) (10%)
  3. Computational technology (explaining the features and capabilities of the technology) (10%)
  4. Case dynamics (describing how the computational technology influences the context) (25%)
  5. Tables and figures (giving an in–depth view into the above topics and may include data and analysis) (20%)
  6. Conclusions (discussing the implications for society, organizations, or individuals (15%)

Your entry will be disqualified if…

  • It is submitted after the deadline.
  • The attachments won’t open or are in the wrong file format.
  • You don’t specify the chosen case study topic.
  • Team member names and college name are not on the case study.
  •  The cover letter is missing.