Posted: August 12th, 2022

Predicting Consumer Tastes with Big Data at Gap Case Discussion

Predicting Consumer Tastes with Big Data at Gap Case Discussion
1.Discuss the creative ways that prediction machines are driving the three types of AI discussed in the article. How is prediction critical to the success of each type of AI?

2.Position the three types of AI discussed in the article within the 2×2 of knowns/unknowns from your book and our lecture together. Discuss the potential pitfalls of each type of AI from this context, along with the typical types of mistakes that each AI could be prone to make based on their positioning within the 2×2.

3.The case concludes by discussing the future cognitive company, and highlights marketing, health care, financial services, education, and professional services as industries primed for an infusion of AI. I’d like your group to consider our new economic perspective on prediction, and come up with what you believe to be an unexpected (or non-obvious) industry outcome from a sharp drop in the price of quality predictions. This could be unexpected due to your identified impact coming from an unanticipated industry, or it could be an unanticipated impact from within one of the industries highlighted in the article. Don’t overthink the mechanics of this – we are early in our discussion of AI and are working with a limited but powerful toolset. At the industry level, what unexpected outcome from a sharp drop in the price of prediction can your group anticipate as you consider the impact of the three types of AI that the article describes?
Institutional Affiliation
1. Discuss the creative ways that prediction machines are driving the three types of AI discussed in the article. How is prediction critical to the success of each type of AI?
Prediction machines have been used for Reactive AI specifically the provision of predictable output based on the input it has received. Particularly, the prediction analysis uncovers the patterns in previous customer behavior to come up with algorithm-driven protocols for customizing products that could be in line with the predicted customer behavior. The predictive machines are used in the development of new products where all the consumer preference data is aggregated to learn the popular elements which are then used in designing other different products.
Secondly, the prediction machines were used in selling the existing products since data is mined for generating product recommendations to potential consumers. This is a form of Limited Memory AI which looks into the past and builds experiential knowledge by observing the data. Thirdly, the big data used in predictive analysis is used to improve the supply chain’s responsiveness and inventory management process especially after the design of a new line of product.
2. Position the three types of AI discussed in the article within the 2×2 of knowns/unknowns from your book and our lecture together. Discuss the potential pitfalls of each type of AI from this context, along with the typical types of mistakes that each AI could be prone to make based on their positioning within the 2×2.
With Reactive AI and the Limited Memory AI, it is possible for its users to be limited in terms of their creativity in their operations. These AI types will be in a position to learn over time from the pre-fed data and past experiences to a point its decisions are no longer creative. Another potential pitfall is the malicious use of AI whereby this information could easily be manipulated to bring forth inaccurate and selfish results. The use of the individual customer information already poses a considerable data security risk. The absence of substantial security mechanisms means that the pre-fed information can be manipulated easily.
3. The case concludes by discussing the future cognitive company, and highlights marketing, health care, financial services, education, and professional services as industries primed for an infusion of AI. I’d like your group to consider our new economic perspective on prediction, and come up with what you believe to be an unexpected (or non-obvious) industry outcome from a sharp drop in the price of quality predictions. This could be unexpected due to your identified impact coming from an unanticipated industry, or it could be an unanticipated impact from within one of the industries highlighted in the article. Don’t overthink the mechanics of this – we are early in our discussion of AI and are working with a limited but powerful toolset. At the industry level, what unexpected outcome from a sharp drop in the price of prediction can your group anticipate as you consider the impact of the three types of AI that the article describes?
Human beings can drastically change their tastes and preferences that are completely different to the predictions made by AI. This would lead to a sharp decline in the price of the predictions made from Reactive and the Limited memory AI. one unexpected outcome is the market shifting towards purchasing items that are made from human creative processes. It is easier to change a creative process that primarily involves decisions made by human beings compared to changing an entire AI system while considering the new tastes and preferences.

References
Israeli, A., & Avery, J. (2017). Predicting consumer tastes with big data at Gap. Harvard Business School Publication (July 10).

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