Analysis And Research On Refrigerator Product Power Improvement Strategy Based On SVM Classification And K-means Clustering

With the expanding scale of e-commerce,consumers’ acceptance and requirements for online shopping goods are constantly increasing,and their satisfaction requirements for online shopping experience are also getting higher and higher.How to improve customer satisfaction and how to carry out product iterative design has become a key issue for the development of enterprises and even the industry.It is extremely important to deeply analyze and mine the influencing factors of online shopping users’ satisfaction,study the characteristics and attributes of online shopping users’ demands,and then help enterprises improve their services and products.In view of the key issues of how to improve the satisfaction of online shopping users and how to carry out product iterative design,this paper takes the text data of refrigerator sales evaluation as an example to study the influencing factors of refrigerator user satisfaction,and then explores the strategy of improving refrigerator product power.First of all,this paper uses SVM classification model to classify the refrigerator online shopping user evaluation text data,and obtains positive and negative classification results;Then,on the basis of the classification results,this paper combines the product price,brand,commodity attribute and other indicators to conduct K-means clustering analysis on the user evaluation text data,and obtains the product portrait of the user’s key concerns,and then analyzes the influencing factors of refrigerator product satisfaction.Finally,according to the analysis results,this paper gives targeted suggestions on the promotion strategy of refrigerator product power,and provides some reference for the research of product power promotion in related fields.