In Search of Balanced Metrics: Understanding the Role of Consumer Mindset Metrics in Online Interactions

Abstract

The rise of digital advertising has provided marketers with more ways to communicate with consumers. This increase in potential advertising channels has created complexity in measuring advertising impact as consumers now take an active role in their own advertising exposure through communication channels such as search. Commonly used metrics for advertising performance have not been adapted to account for this and we still possess limited knowledge about what leads consumers to engage with a brand online.

Prior research has shown a weak correlation between customer mindset metrics (CMMs) and online behaviours at an aggregate level (Pauwels & van Ewijk, 2020), however, the differences across consumers at an individual level are less understood. Using an innovative approach to data collection, this research demonstrates how consumer attitudes influence interactions with brands online at an individual level for 27 brands across three categories. The application of hurdle model analysis enables us to comprehend not only the effect of CMMs on the probability of a brand website visit, but also the frequency of such visits.

Results show that 85% of all visits come from consumers with at least one strong positive CMM and those with the most positive mindsets are up to 24 times more likely to visit a brand website. Furthermore, the optimal manner of representing CMMs is not via the traditional measures from a hierarchy of effect models (awareness, consideration and preference) but rather via separate measures of familiarity and liking. Neglecting to consider the relationship between CMMs and website visits in metrics and performance models could result in an excessive allocation of resources to media channels in which consumers initiate their own advertising exposure and as a result be biased towards those individuals who are already predisposed to a brand.