Research on the Innovative Marketing Model of E-Commerce Platform Driven by Big Data in the Era of Network Economy

Abstract

With the booming development of Internet information technology, e-commerce platforms in the era of network economy have undergone great changes, triggering a new marketing model change. Innovative research on marketing models can help the transformation and development of small and medium-sized e-commerce companies, which has important practical significance and theoretical value. The prediction of e-commerce sales is one of the key aspects of the evaluation of innovative marketing models, and only an accurate prediction of future sales can lead to a reasonable marketing plan. Therefore, a big data-driven e-commerce sales forecasting method is proposed. First of all, for 1703 real e-commerce companies, a large number of relevant data that affect sales are selected, including sales records, product information, product evaluation, and other information. A knowledge graph was then used to preprocess the data samples to produce a sample set containing concepts, entities, and relationships. Next, the knowledge graph K-modes clustering model is established. By fixing the affiliation matrix and the clustering cluster matrix in turn, the minimum of the objective function is continuously solved to obtain the cluster centres. Finally, sales prediction is achieved based on the clustering results. The experimental results show that the proposed clustering model is able to obtain better performance in terms of cluster purity, NMI, and F-value. The proposed clustering model has high sales prediction accuracy and has certain reference value for e-commerce enterprises of different scales to formulate innovative marketing models.