Construction of the Luxury Marketing Model Based on Machine Learning Classification Algorithm

Abstract

China has become the world’s largest luxury goods consumer market due to its population base. In view of the bright prospects of the luxury consumer market, major companies have entered and want to get a share. For the luxury goods industry, traditional mass marketing methods are not able to serve corporate sales and marketing strategies more effectively, and targeted marketing is clearly much more efficient than randomized marketing. Therefore, in this paper, based on consumer buying habits and characteristics data of luxury goods, the paper uses a machine learning algorithm to build a personalized marketing strategy model. And the paper uses historical data to model and form deductions to predict the purchase demand of each consumer and evaluate the possibility of customers buying different goods, including cosmetics, jewelry, and clothing.

Product potential user prediction based on machine learning

“…Both Arif Furqon Nugraha Adz Zikri's [2] and Chen Qiaoshan's [3] studies state that the benefit of decision trees is that they can simplify complex decision-making processes. Enables decisions to explain solutions to problems better.…”

“…The primary drawback is that when the data have unequal sample sizes for every decision tree category, the information gain conclusions are biassed towards values with greater values. Furthermore, a decision tree needs help to handle missing data and can occasionally cause overfitting issues [3].…”

“…The SVM classification algorithm has several benefits, such as the efficient resolution of dimensional and machine learning problems, nonlinear difficulties in situations with small sample sizes, and the avoidance of local minima and neural network structure selection issues [3]. In contrast to its counterparts, SVM has the largest error rate due to a high number of false positive and false negative cases [4].…”

“…Pay Later defaults and reduces losses. Chen Qiaoshan [3] constructed a personalised marketing strategy model based on the data of Chinese consumers' buying habits and the characteristics of luxury goods. T K. Das [4] built a predictive model to evaluate customers' responses to a company's products based on past buying trends to increase product sales.…”