Using Data Mining to Analyze Customer Behavior in International Cosmetics and Personal Care Brands

Cosmetics and personal care industry has emerged as one of the most active and competitive market segments of consumers in the last ۱۰ years. The market value of the global is estimated to be in hundreds of billions of dollars, and it is projected to be growing; growth, however, has come with the added competition in the brand, increased number of sales channels, and further complexity in consumer behaviour. Within such an environment, profound, evidence-based knowledge of customer behavior has ceased to be a non-essential analytical tool and become a strategic requirement. Data mining and machine learning provide the ability to systematize the use of large, heterogeneous customer data generated in many different touchpoints, such as purchase transactions, digital interactions, social media interactions, and beauty applications, loyalty-program data, and even multimedia data, such as selfies and makeup-tutorial videos. With the implementation of sophisticated clustering and classification techniques, market-basket analysis, sequence and journey modeling, sentiment and text analytics, and multimodal deep learning, companies will be able to identify latent behavioral trends and transform them into operational marketing choices. These decisions involve customizing product suggestions, pro-optimization of product portfolio and assortments, creation of targeted online promotions, and regional and channel demand predictions. The article explains how data mining can be used in the analysis of the customer behavior in global cosmetics and personal care brands. It starts by describing the theoretical basis of the data mining and machine learning in customer analytics, followed by explaining the nature of industry-specific data and the preprocessing needs thereof. It then examines fundamental methods to derive complex patterns of behavior customer segmentation, association-rule mining, customer journey modeling, and sentiment analysis and explains how they are applied in practice to improve the marketing strategy in the form of demand prediction, recommendation personalization, channel-mix optimization, and data-driven loyalty-program design. The article also discusses widely cited industry examples—including global players such as L’Oréal and Sephora—to illustrate how data platforms and AI can support Beauty Tech initiatives and personalization across markets.

کلیدواژه ها:

Data Mining ، Machine Learning ، Customer Analytics ، Cosmetics Industry