Marketing from ‘Traffic’ to ‘Retention’ -- Establishing Brands and Models Based on Value Proposition and Customer Relationships

Abstract

With the gradual depletion of the traffic dividend in the mobile Internet era, retail enterprises are facing profound challenges in their strategic transformation from “traffic acquisition” to “retention operation”. The traditional traffic marketing paradigm, which centers on exposure volume and customer acquisition scale, is experiencing a continuous decline in marginal benefits. In contrast, the “retention” mindset based on the management of the full life cycle value of users has increasingly become a focus of attention in both academic and business circles. This paper takes the “traffic-retention” transformation as the core issue, and systematically explores how retail enterprises can achieve a paradigm shift from one-time traffic consumption to sustainable user asset accumulation through the construction of precise value propositions, multi-dimensional customer relationship building, and the deep integration of digital technologies, by relying on two theoretical pillars: value proposition construction and customer relationship management. The study takes Uniqlo and Suning Xiaodian as typical cases, and through a comparative analysis of the practical paths of the two enterprises in terms of differentiated positioning of value propositions, omni-channel customer relationship management, and data-driven refined operations, it distills a closed-loop operation framework of “value proposition - customer demand - customer relationship - value realization”. The research findings are as follows: First, a precise value proposition driven by big data is the fundamental prerequisite for the transformation from traffic to retention. Enterprises need to form dynamic and adaptive differentiated value outputs based on in-depth user insights. Second, a multi-dimensional customer relationship system that spans the entire pre-sale, in-sale, and post-sale cycle is a key mechanism for enhancing user stickiness and repurchase rates, with the core being to take customer lifetime value as the decision-making anchor point for resource allocation. Third, the integration of artificial intelligence and big data technologies provides technical support for the digital closed loop of “prediction - optimization - service - retention”, enabling enterprises to achieve integrated management of demand prediction, intelligent replenishment, personalized recommendation, and effect evaluation. The theoretical contribution of this paper lies in the systematic integration of value proposition theory and customer relationship management theory, and the construction of an analytical framework for the “retention” transformation of the retail industry, filling the gap in the existing research in the intersection of the two theories. At the practical level, it provides operational implementation paths for brand building and business model innovation of new retail enterprises.