<p>Consumer market demand prompts e-commerce to carry out precision marketing. This paper first analyzed the relationship between consumer market demand and e-commerce precision marketing, then built user profiles by combining a recency, frequency, and monetary (RFM) model with a K-means algorithm, and used an improved particle swarm optimization (IPSO) algorithm to obtain the optimal number of clusters for the K-means algorithm. The results showed that the IPSO-kmeans algorithm had a higher accuracy, a lower Davies-Bouldin index value, and a better clustering effect compared with the K-means and k-medoids algorithms. When the IPSO-kmeans algorithm was used to cluster the user data of company A, four user portraits were obtained. After implementing the proposed precision marketing suggestions, it was found that company A had a higher user retention rate and a steady increase in monthly turnover. The results verify the effectiveness of precision marketing based on user portraits for e-commerce development. The method can be promoted and applied in practice.</p>