Clovette : predicting preferences for flowers

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.