Clovette : predicting preferences for flowers
Name
958500412-MIT.pdf
Description
Full printable version
Size
12.64 MB
Format
Adobe PDF
Checksum (MD5)
34d123cd95aa67c880f4494aa69b2ee5
Author(s)
Lee, Jeeyun Jennifer
Advisor(s)
Tauhid Zaman.
Alternative Title
Predicting preferences for flowers
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
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).
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
Terms of Use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
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