Introducing Linkage Analysis
The core concept of “linkage analysis” is to connect information and feedback from various sources in order to support the decision-making processes at the firm. As we will discuss through case studies in this chapter, these sources could include survey and nonsurvey customer data, operational data, and financial metrics. They can potentially span the organizational workforce, current and potential customers, and other internal and external stakeholders. For example, the process at a large retailing organization started by exploring the linkages between survey-based overall customer attitudes and store-level financial performance. The initial objective of the linkage exercise was to test and validate the hypothesis that stores with more favorable customer attitudes generate higher store profits. However, as the process unfolded, the research objective expanded to include three other sources of relevant data—associate engagement for the store employees, relevant process data such as the productivity of each store, and store neighborhood demographic data, including median age and household income. A more comprehensive linkage analysis, in this case, was then able to bring together these diverse but relevant sources of data to provide valuable information to the management.
Results of the linkage analysis confirmed that stores with higher levels of employee engagement were more capable of generating greater customer loyalty. Loyalty in turn led to more favorable customer behavior in these stores, characterized by larger basket size in terms of dollar value as well as the number of units in the basket. Further, the analysis confirmed that the rewards of customer loyalty were almost instantaneous, and there was very little, if any, lag between positive customer attitudes and favorable customer behavior.
Other sources of data provided additional valuable information. For instance, we were able to identify two important drivers of customer loyalty—“store productivity” and the “age of the store.” This organization had been focusing on higher productivity in its outlets to generate greater profits. However, the analysis revealed that productivity levels above a certain threshold level adversely affected long-term store profitability. The negative relationship was associated with more unfavorable customer experiences in the highly productive stores where customers felt rushed and disliked the absence of adequate floor staff to assist them. This led to a decline in customer perceptions of their service experiences at the stores and their migration to competitors. The linkage approach was thus able to deploy multiple sources of data to explicate the negative effects of productivity gains on financial performance and discover the mediating role of customer perceptions. The age of the store was also found to influence the quality of service experience. Stores that were older than a certain threshold age had a dramatic drop in customer perceptions. Customers felt that these stores were run down, the aisles were too narrow, and the parking facilities were unsafe after dark. Finally, linkage analysis was able to identify neighborhood demographics that had the strongest impact on store-level profitability. Specifically, the median household size and household income in the neighborhoods around a property had a strong impact on store profitability. Two retail stores with identical levels of employee engagement and customer loyalty could therefore have substantial differences in profit because of their locations.
From the perspective of senior management, the value of the linkage analyses came from being able to bring together sources of data that had until then resided in independent locations within the organization. The ability to link these data in a set of comprehensive models allowed them to recognize and appreciate the linkages among the various performance metrics, their antecedents, and other uncontrollable factors. As a result of these analyses, management was able to allocate its resources more judiciously by comparing the impact of investments say in building and infrastructure improvements with say investing in associate engagement initiatives, because all these decisions had been linked to the same end result—the bottom-line financial performance of the organization.
Defined formally then, “linkage analysis” is the process of connecting multiple sources of organizational data to provide enhanced decision support that can improve the overall performance of the organization. The strategic and research objectives of the organization, as well as availability of relevant information needed to perform the analyses, drive the selection of the data sources. While the aforementioned retailing case study used multiple sources of data, a typical linkage project usually starts with fewer data sources. For example, a typical linkage analysis within the customer care environment might involve bringing together associate engagement and customer experience data to examine and demonstrate that more engaged employees are able to provide better customer experiences during call center transactions. Over time however, these analyses typically broaden their scope to include data on other relevant metrics of customer experience such as wait time, hold time, talk time, and after-call wrap time to understand the impact of these measures on the quality of customer experience.