A Study of Consumer Data-Driven Purchasing Behaviour Insight and Prediction in the Context of Digital Marketing

Consumer data is an important support for analysing and observing consumer behaviours in the era of digital marketing, and constructing models to predict consumer purchasing behaviours. In this paper, we select the Retailrocket consumer behaviour dataset based on real shopping websites, analyse the distribution of various types of consumer behaviour over time and other data characteristics, and gain insights into the behavioural habits of consumers when shopping. Based on the XGBoost algorithm in machine learning, a prediction model of consumer behaviour is constructed, and the genetic algorithm is used to optimize and improve the XGBoost algorithm.The XGBoost prediction model has a significantly better prediction performance than the LSTM prediction model and the LR prediction model when facing the data under the under-sampling data balancing method and the improved random under-sampling method based on the K-means algorithm. . The performance of the GA-XGBoost prediction model optimised by the genetic algorithm is significantly improved compared to the XGBoost prediction model, and substantially better than the LSTM prediction model and the LR prediction model. The accuracy and F1 value of the GA-XGBoost prediction model in the data under the improved stochastic undersampling method are 0.90865 and 0.92435, respectively, which are improved by 14.69% and 17.26% relative to the XGBoost prediction model. Meanwhile, the stability of GA-XGBoost prediction model is also significantly improved compared to XGBoost prediction model.