A Framework for Conducting Linkage Analysis

The prospect of linking metrics across functional areas is an exciting but challenging exercise. Hasty or analytically weak decisions can unfortunately lead to severe adverse consequences. For instance, a bank that reduces the number of tellers in its branches as a cost cutting measure will likely experience erosion of customer loyalty because of increased waiting time at the branches. In the short term, however, despite eroding customer loyalty, profits might go up, largely because of the cost reduction measures undertaken by the bank. The link between customer loyalty and the financial performance of the firm might thus seem negative in the short run, thereby challenging the wisdom for cross-functional linkages. This negative relationship will however correct itself over time, as more unhappy customers defect to other banking institutions, leading to reduced profits. The ability to link these data will then provide an early indication of the long-term risk exposure of the bank, and allow the bank to take proactive corrective actions. Such examples bring to light the onus associated with performing linkage analyses, and the need for a systematic and rigorous approach to establishing the hypothesized links.

Significant challenge also comes from the infancy of research on conducting such linkage analyses. The few studies documented in the academic world are less constrained by time and other resources. In the applied world, however, resource limitations are more real. The ability to work within a given budgetary and time constraint is critical in business world applications, introducing greater pressures to perform the analyses within the limited resources. In view of the aforementioned challenges and potential pitfalls, the need to adopt a systematic and rigorous approach to linkage analysis that is also sensitive to the resource limitations suggests the need for following a best-practice approach to performing these analyses. Therefore, we present a 10-step approach for linking various measures of organizational performance. To provide an easy-to-follow framework, we present a case where attitudinal measures of customer loyalty are linked to measures of customer behavior and downstream financial results. The implications of the framework however apply to all sorts of organizational data.

The proposed approach offers four key benefits: First, it allows the parties engaging in linkage analyses to evaluate the need for, and benefits of, undertaking linkage analyses. Second, it allows them to gauge their readiness for conducting linkage analyses by getting a better understanding of resource requirements for successful completion of the project. Third, it sensitizes linkage analysts to some of the common pitfalls encountered during the analytical process. Finally, the proposed framework allows easy customization for adaptation to various industry and marketplace environments. Past research has shown that a variety of factors, such as the degree of competition, market cycle, consumption cycles, and formality of the buyer-seller relationship can all influence the appropriateness and viability of these links. Our approach is sensitive to the need for customization, and thus refrains from proposing a standard one-size-fits-all approach. Instead, it provides a framework, individual components of which can be customized to suit the needs of a specific project.