學門類別
最新個案
- Leadership Imperatives in an AI World
- Vodafone Idea Merger - Unpacking IS Integration Strategies
- V21 Landmarks Pvt. Ltd: Scaling Newer Heights in Real Estate Entrepreneurship
- Snapchat’s Dilemma: Growth or Financial Sustainability
- Did I Just Cross the Line and Harass a Colleague?
- Predicting the Future Impacts of AI: McLuhan’s Tetrad Framework
- Porsche Drive (A) and (B): Student Spreadsheet
- Porsche Drive (B): Vehicle Subscription Strategy
- TNT Assignment: Financial Ratio Code Cracker
- Winsol: An Opportunity For Solar Expansion
Nata Supermarkets: Customer Analytics
內容大綱
In January 2022, the vice-president of technology for Nata Supermarkets was reviewing the company’s performance against its competitors for the 2021 calendar year. The company had been performing poorly both based on its internal metrics and against competitor growth rates. The vice-president also noticed that many competitors began revealing new data analytics initiatives in their annual reports. Many companies experienced industry-leading growth because of these changes and upgraded their guidance for the following year. To compete with an increasing number of data-driven competitors, Nata Supermarket created its internal data set to collect information on customer shopping habits and customer demographics such as age, educational background, and frequency of complaints. With the emergence of visualization tools and data analytics, the vice-president was wondering what useful insights could be drawn from its internal data set. Could this information be useful to resolve various issues such as targeting promotions and forecasting demand?
學習目標
This case is suitable for undergraduate- and graduate-level courses that cover topics in machine learning and analytics, clustering in particular. Students with prior knowledge of the R programming language will be able to greatly simplify their analysis, although calculations can also be performed using different tools, such as Python. After completion of this case, students will be able to accomplish the following objectives:<br><br><ul><li>Manage a large number of features, identify erroneous data, and understand the “usefulness” of features.</li><li>Create visualizations that make sense in the context of one of the two main business problems: segmentation versus prediction of spending.</li><li>Understand the difference between the two main business problems and choose appropriate machine learning models.</li><li>Compare different models for each business problem, calculate accuracy, and generate predictions and insights.</li></ul>