Dynamic Pricing and Bias After reading the article, How targeted ads and dynamic pricing can perpetuate bias, in the Module 5: Lecture Materials & Resources , write a detailed summary on Dynamic Pricing and Bias. Submission Instructions: The paper is to be clear and concise and students will lose points for improper grammar, punctuation, and misspelling. The paper is to be 300 words in length, current APA style, excluding the title, abstract and references page. Incorporate a minimum of 2 current references (published within the last five years) scholarly journal articles or primary legal sources (statutes, court opinions) within your work. Complete and submit the assignment by 11:59 PM ET on Sunday. Late work policies, expectations regarding proper citations, acceptable means of responding to peer feedback, and other expectations are at the discretion of the instructor. You can expect feedback from the instructor within 48 to 72 hours from the Sunday due date. ------------------------------------------------------------------------------------------------------------------------------------------ Marketing | How Targeted Ads and Dynamic Pricing Can Perpetuate Bias Subscribe Sign In Diversity Latest Magazine Popular Topics Podcasts Video Store The Big Idea Visual Library Case Selections You have 1 free article left this month. Create an account to read 2 more. Marketing How Targeted Ads and Dynamic Pricing Can Perpetuate Bias by Alex P. Miller and Kartik Hosanagar November 08, 2019 Summary. In new research, the authors study the use of dynamic pricing and targeted discounts, in which they asked if (and how) biases might arise if the prices consumers pay are decided by an algorithm. Suppose your company wants to use historical data to train an algorithm to identify customers who are most... more Tweet Post Share Save Buy Copies Print In theory, marketing personalization should be a win-win proposition for both companies and customers. By delivering just the right mix of communications, recommendations, and promotions — all tailored to each individual’s particular tastes — marketing technologies can result in uniquely satisfying consumer experiences. While ham-handed attempts at personalization can give the practice a bad rap , targeting technologies are becoming more sophisticated every day. New advancements in machine learning and big data are making personalization more relevant, less intrusive, and less annoying to consumers. However, along with these developments come a hidden risk: the ability of automated systems to perpetuate harmful biases. In new research, we studied the use of dynamic pricing and targeted discounts, in which we asked if (and how) biases might arise if the prices consumers pay are decided by an algorithm. A cautionary tale of this type of personalized marketing practice is that of the Princeton Review. In 2015, it was revealed that the test-prep company was cha.