The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue growth. The present challenge facing HairCo is the lack of models that learn from aggregated data centered on the buying behavior, demographic, and other publicly available data of end consumers tied to historical sales data of their customers, i.e., salons and stylists. The proposed clustering and regression models achieved notably improved results using the aggregated data in comparison to models solely using internal company-provided data. Recommendations on which features are most important from both models that improve customer profiling and predicting sales were presented. With these results, HairCo can increase its revenue and expand its market share.
Sepenu, Alexander K. and Eliasen, Linda
"A Machine Learning Approach to Revenue Generation within the Professional Hair Care Industry,"
SMU Data Science Review: Vol. 6:
1, Article 6.
Available at: https://scholar.smu.edu/datasciencereview/vol6/iss1/6
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