In a typical satisfaction or brand image study, you want to know how consumers rate the performance or imagery of products/services on many different dimensions. The challenge is to clearly summarize results that often generate a large number of interrelated measures. Structural equation modeling (SEM) is an approach Morpace uses to help overcome such challenges.
SEM is sometimes referred to as causal modeling. It is used to test the reasonableness of alternative hypotheses regarding the causal relationships between various measures, and their relationships to underlying dimensions or latent variables.
Morpace can use SEM to assess how the performance of various dimensions influence one another, and how they ultimately influence a customer’s overall satisfaction and loyalty. SEM is also a proven tool for modeling brand image and brand equity because it can measure the relative impact of factors such as awareness, image and engagement on overall brand consideration. A key output from a SEM analysis is the indexing of the relative impact of various measures on important outcome variables, such as overall satisfaction or brand consideration. A pictorial presentation is prepared to show the relationship strength between variables in the model, a visual that can greatly facilitate comprehension and understanding of survey results.