Customer Hierarchies Research Articles

Multilevel Value Co-Creation Within Key Accounts

Optimizing complex service partnerships requires an understanding of multilevel value co-creation processes in a customer-supplier ecosystem. Such strategic business-to-business relationships impact supplier costs and revenues, necessitating dedicated personnel across hierarchical levels to co-create value. The authors study the customer-supplier ecosystem intra-organizationally across firm levels to understand value expectations and orientations, and extra-organizationally to identify supplier harmonization strategies for managing multilevel value co-creation. Empirically, the authors conduct a multilevel-multisource qualitative case study within a strategic partnership, with a service contract valued at over $100 million per year, that includes senior- and frontline-level interviews in the key account customer organization (32 participants) and supplier organization (22 participants). The analysis focuses on how institutions, spanning contracts, and behavioral norms within the business-to-business customer-supplier ecosystem, govern actions and aspirations. Findings typify the nature of divergent value expectations between customer hierarchies along with level-specific co-creation orientations. As future prognosis hinges on harmonizing practices and institutions to address discrepancies in value visibility across customer levels, such an assessment also enables actionable strategies to enhance reengagement. The findings collectively offer theoretical implications for key account management (KAM) research, influencing areas such as professionalization of KAM, effectiveness in KAM, and network dynamics within KAM.

Single-period stochastic demand fulfillment in customer hierarchies

To maximize profits, given limited resources, companies commonly divide their overall customer base into different segments and use allocation policies to prioritize the most important segments. Determining the optimal allocations is challenging due to supply lead times and uncertain demand. Another challenge is the multilevel hierarchical structure of the customer segments. In general, available quotas are not determined by a central planner with full visibility of all individual customer segments but rather result from a sequence of allocation steps with an increasing level of granularity. In this paper, we investigate this sequential allocation process. Specifically, we identify crucial information for making allocation decisions in customer hierarchies and propose decentralized methods that lead to near-optimal allocations while respecting the requirements for information aggregation. We evaluate the methods by comparing their information requirements and reflect on the role of information sharing in hierarchical allocation decisions.

Deterministic allocation models for multi-period demand fulfillment in multi-stage customer hierarchies

In manufacturing environments with heterogeneous (e.g., because of different profit margins) customers the optimal matching of available supply with dynamic demand is a challenging task as soon as supply becomes scarce. In Make-to-Stock systems, Demand Fulfillment first allocates these scarce resources (in form of Available-to-Promise — ATP) to customer segments on basis of their forecasted demand. The resulting quotas are then consumed (“promised”) when real customer orders arrive. In a multi-stage sales hierarchy, this allocation process often has to be executed level by level, on basis of decentral, aggregate information only. Decentral, multi-period, deterministic linear and non-linear programming models are proposed approximating the first-best benchmark of a central, multi-period allocation planning with full information. Roll over simulations have been performed to obtain insights about the behavior of the proposed decentral models and to demonstrate their benefits in comparison with common quantity-based methods, especially for high levels of customer heterogeneity and high shortage rates.

Decentral allocation planning in multi-stage customer hierarchies

This paper presents a novel allocation scheme to improve profits when splitting a scarce product among customer segments. These segments differ by demand and margin and they form a multi-level tree, e.g. according to a geography-based organizational structure. In practice, allocation has to follow an iterative process in which higher level quotas are disaggregated one level at a time, only based on local, aggregate information. We apply well-known econometric concepts such as the Lorenz curve and Theil’s index of inequality to find a non-linear approximation of the profit function in the customer tree. Our resulting Approximate Profit Decentral Allocation (ADA) scheme ensures that a group of truthfully reporting decentral planners makes quasi-coordinated decisions in support of overall profit-maximization in the hierarchy. The new scheme outperforms existing simple rules by a large margin and comes close to the first-best theoretical solution under a central planner and central information.

Implementing the Marketing Concept at the Employee–Customer Interface: The Role of Customer Need Knowledge

Abstract Although the identification of customer needs constitutes a cornerstone of the marketing concept, the accuracy of frontline employees' perceptions of customer needs has never been examined in a systematic manner. Following research in social cognition, this article introduces the concept of “customer need knowledge” (CNK), which describes the extent to which a frontline employee can accurately identify a given customer's hierarchy of needs. The results of two large-scale, multilevel investigations involving data from three different levels (customers, employees, and managers) demonstrate the importance of CNK for the provision of customer satisfaction and customer value. In particular, CNK fully mediates the influence of employees' customer orientation and cognitive empathy on these customer outcomes. Moreover, whereas the length of the relationship between an employee and a particular customer enhances CNK, a large age discrepancy in relation to the customer decreases employees' level of CNK.

Implementing the Marketing Concept at the Employee-Customer Interface: The Role of Customer Need Knowledge

Although the identification of customer needs constitutes a cornerstone of the marketing concept, the accuracy of frontline employees’ perceptions of customer needs has never been examined in a systematic manner. Following research in social cognition, this article introduces the concept of “customer need knowledge” (CNK), which describes the extent to which a frontline employee can accurately identify a given customer's hierarchy of needs. The results of two large-scale, multilevel investigations involving data from three different levels (customers, employees, and managers) demonstrate the importance of CNK for the provision of customer satisfaction and customer value. In particular, CNK fully mediates the influence of employees’ customer orientation and cognitive empathy on these customer outcomes. Moreover, whereas the length of the relationship between an employee and a particular customer enhances CNK, a large age discrepancy in relation to the customer decreases employees’ level of CNK.