2026-09-18T19:34:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1045492022-01-13T07:54:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tauhid Zaman.
Lee, Jeeyun Jennifer
Sloan School of Management.
Sloan School of Management
2016-09-30T19:35:05Z
2016-09-30T19:35:05Z
2016
2016
http://hdl.handle.net/1721.1/104549
958500412
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).
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.
by Jeeyun Jennifer Lee.
M.B.A.
73 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Clovette : predicting preferences for flowers
Predicting preferences for flowers
Thesis
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Publication
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<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.</Abstract>
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