Advertising Performance MeasurementBrand Building
The University of Sydney
The rise of both digital advertising and eCommerce platforms has provided marketers with more ways to communicate with consumers than ever before. Yet this increased choice has created additional complexity in measuring the performance of advertising communications. A key reason for this complexity is the active role that consumers now take in their own advertising exposure through communication channels, such as search, which are classified as consumer-initiated contacts (CICs). Commonly used metrics and approaches to measuring advertising performance have not been adapted to account for this and consequently can overestimate the role of advertising that is consumer initiated (Blake et al., 2015). One major challenge in improving the way we measure CICs, including search, is the limited knowledge we possess about what leads consumers to engage with a brand on the internet. Prior research has shown a weak correlation between customer mindset metrics (CMMs) and online behaviours (CICs) at an aggregate level (Pauwels & van Ewijk, 2020), however, this does not allow us to understand the differences across consumers. While customer mindset metrics (CMMs), such as awareness and consideration, play a role in determining online behaviours, these attitudinal and behavioural approaches to performance measurement largely exist in silos, and there is limited understanding of how these two important streams of measurement can be linked. Using an innovative approach to data collection with passive metering technology which is able to capture data at the individual level, 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 positive mindset metric (a top 2 rating for either liking or familiarity), 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) nor, as prior research has suggested, purely using cognitive metrics (Cain, 2022; Dotson et al., 2017), but rather via separate measures of familiarity and liking which allows the strongest CMMs to be revealed as increasing website visit likelihood. 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. As a result, media investments may be biased towards those who are already predisposed to a brand. This can result in the diversion of funds away from advertising focused upon building positive brand attitudes. Since failure to build positive attitudes could risk the long-term sales potential of a brand, managers need to keep measuring CMMs even if advertising expenditure shifts online if the objective is to build the brand.