Precision Marketing Strategies for Small and Medium-sized Clothing E-commerce Enterprises Based on Big Data

Abstract

Based on the big data analysis technology of small and medium-sized clothing e-commerce enterprises, this paper provides theoretical and practical methods for e-commerce enterprises to apply big data technology. The data sources come from questionnaires and online data grab. Through the Google online questionnaire survey, 252 valid questionnaires were received. Python is used to search valid reviews on Tmall and Internet with some keywords, and a total of 11,864 valid comments were obtained. We find that the reason why men buy clothes is as high as 68% because of the seasonal change, but only 31% of women buy clothes because of the seasonal change. The higher the average monthly income of women, the more attention is paid to whether the style of clothing design is suitable for themselves, and the more attention is paid to the service attitude of e-commerce enterprises. From decision tree analysis, the fit of clothing style is the most important factor affecting the shopping choice of customers. From the data of customer reviews, the most mentioned variables are clothing style, fashion, service attitude and price. The number of reviews and the sentiment analysis score on clothing style are all ranking first. The number of reviews and the sentiment analysis score on clothing fashion are all ranking second. We also proposes corresponding precision marketing strategies based on the results of big data analysis, such as gender recommendation strategies, personalized recommendation strategies, differentiated marketing strategies, enterprise training and clothing design strategies.