Clovette : predicting preferences for flowers
Author(s)
Lee, Jeeyun Jennifer
DownloadFull printable version (12.64Mb)
Alternative title
Predicting preferences for flowers
Other Contributors
Sloan School of Management.
Advisor
Tauhid Zaman.
Terms of use
Metadata
Show full item recordAbstract
Flowers are often gifted for major holidays and personal holidays, for both personal and corporate purposes. Today's solutions in the market are abundant but scattered, with many players offering products of varying quality at a range of price points. To command higher prices and stay relevant in the market, florists need to distinguish themselves through high quality and/or niche product and ease of service. The goal for this project is to map the current competitive landscape and supply chain of the flower industry, and to determine whether predictive modeling in the floral industry is feasible as a point of difference for new gifting company Clovette. Data collection through distribution of a survey called "Discovering Floral Preference" assessed the potential for prediction. Furthermore, the project explores Clovette's brand identity and potential "good" business development through sustainability initiatives and supply chain optimization. Keywords: random forest, predictive modeling, flowers, gifting, sustainability.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 69-73).
Date issued
2016Department
Sloan School of ManagementPublisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management.