This field-based case allows instructors to explore the world of data analytics with students, using Brandefy, a startup company, and its plan to expand its app into additional categories of the grocery store as a sandbox for learning. Using R (or some other statistical programming language/software, such as Python, SAS, Stata, or SPSS) and retail scanner data from 84.51? (https://www.8451.com/area51), the case sets the stage for instructors to explore some fundamentals of data science, as they apply to the study of customers.
The idea behind Brandefy came to its founder, Meg Greenhalgh, when she worked as a category manager and discovered that some competitors were actually name brands supplying store-brand versions of products. In addition, as a consumer herself standing in the grocery store, Greenhalgh found herself wondering when that was the case and, when it was, how she could figure it out quickly. What if there was an app that shoppers could use to learn the differences between name- and store-brand products?
By the spring of 2018, Greenhalgh was ready to launch an app for Brandefy. It would compare name and store brands using consumer feedback on 200 product reviews. The reviews either confirmed that the generic version was as good as the name brand or presented data to suggest it was not. While excited about the app, Greenhalgh considered several questions, including some she had from the very beginning. How many times might people want to use the app in a given trip to the grocery store? What categories should Brandefy prioritize as it expanded?
