Optimization analysis of online marketing strategy based on the regression prediction model

Abstract

In response to the three new product sales recommendations of the Amazon online market “Sunshine”, based on the data set provided by the enterprise data center, the data in the NLP feature package is preprocessed in Python, and the AHP model is established to measure and establish the data. This experiment according to the quarterly time series ARIMA model to forecast the product sales, and then through the standardization of OLS regression model to predict the trend of the development of products. The results showed that customer text reviews were an important reference for the sunshine company and found that microwave oven sales increased by an average of 6.28%, while sales of baby pacifiers and hair dryers increased by an average of 1.02%. This experiment based on this, puts forward the corresponding marketing strategy. Online marketing strategy can pay more attention to the customer’s comments, make updates on the features of the product, the use of to meet customer demand. The study enriches the existing research results, and the sun of the company’s sales strategy provides feasibility suggestions, thus has important theoretical significance and practical value.