Research on Product Iteration and Optimization Path of DTC Personal‑Care Brands Based on Buyer Feedback Data

Abstract

Against the backdrop of digital trade, DTC personal‑care brands have rapidly penetrated the market relying on the disintermediation model. Nevertheless, new‑product failure rates remain persistently high. Conventional market research suffers from lags and high costs, making it difficult to adapt to brands’ fast‑paced iteration demands. Multi‑source buyer feedback data has become a core carrier for excavating users’ real‑world demands. Grounded in the Signaling Theory, Closed‑Loop Product Innovation Theory and Configurational Theory, this paper constructs a five‑order closed‑loop analytical framework. Drawing on 126 427 pieces of buyer feedback data from overseas DTC personal‑care brands, this empirical research adopts LDA topic‑sentiment mining (Martilla, J. A., & James, J. C., 1977), IPA analysis, fsQCA configurational analysis and multi‑case validation. This study extracts five core dimensions of user feedback including product efficacy and performance. It verifies that no single necessary condition exists for high‑performance product iteration, and the synergy of multiple elements forms four equivalent optimization paths. Moreover, the research confirms that iteration proactively excavating explicit and implicit demands delivers remarkably better outcomes than passive negative‑review remediation. It provides theoretical support and practical references for data‑driven product innovation of DTC personal‑care brands.