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.
Highlights
In recent years, with the rapid development of the economy, people’s living standards have risen
For enterprises, how to carry out effective marketing has become the key to the survival and even prosperity of enterprises
How to collect and process the data, understand the user’s needs, accurately find the target user group, and provide corresponding solutions so as to achieve enterprise profit and user experience win-win is in line with the trend of the times
Summary
With the rapid development of the economy, people’s living standards have risen. E advantages of the SVM classification algorithm include effective solution to machine learning problems, high-dimensional problems, nonlinear problems in small sample cases, and avoiding neural network structure selection and local minimum point problem. Decision tree algorithm, and KNN algorithm learn faster in the modeling process than ANN algorithm and SVM algorithm. Erefore, based on the theoretical research basis of the classification algorithm as above, this paper collects the sales data of luxury goods in a certain area and conducts the corresponding customer demand forecast analysis. Some algorithms need to standardize data, such as logistic regression algorithm, while some others do not, such as decision tree and artificial neural network algorithm. E data normalized conversion function is as follows: yik xik − min xk . max xk − min xk
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