Dynamic Relationship Marketing

Firms routinely engage in relationship marketing (RM) efforts to improve their relationships with business partners, and extant research has documented the effectiveness of various RM strategies. According to the perspective proposed in this article, as customers migrate through different relationship states over time, not all RM strategies are equally effective, so it is possible to identify the most effective RM strategies given customers㤼㸲 states. The authors apply a multivariate hidden Markov model to a six-year longitudinal data set of 552 business-to-business relationships maintained by a Fortune 500 firm. The analysis identifies four latent buyer㤼㸶seller relationship states, according to each customer㤼㸲s level of commitment, trust, dependence, and relational norms, and it parsimoniously captures customers㤼㸲 migration across relationship states through three positive (exploration, endowment, recovery) and two negative (neglect, betrayal) migration mechanisms. The most effective RM strategies across migration paths can help firms promote customer migration to higher performance states and prevent deterioration to poorer ones. A counterfactual elasticity analysis compares the relative importance of different migration strategies at various relationship stages. This research thus moves beyond extant RM literature by focusing on the differential effectiveness of RM strategies across relationship states, and it provides managerial guidance regarding efficient, dynamic resource allocations.

Keywords: hidden Markov models, relationship marketing


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