Research: behavioral KPI VS branding KPI correlation
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The document analyzes whether ad server metrics like click-through rate (CTR) and rich media interactions can predict brand performance. It finds: - CTR has a weak positive relationship with brand awareness and message association, but regression models explain little variance (2%). - Interaction rates and expansion rates did not significantly correlate with or predict changes in brand metrics. - In general, ad server metrics were poor proxies for measuring changes in brand attitudes compared to survey-based experiments. While they track direct response, they do not reliably indicate impact on brand objectives.
Research: behavioral KPI VS branding KPI correlation
- 1. Can Rich Media Metrics Predict Brand Impact? Ken Mallon Rick Bruner SVP, Custom Solutions Head of Research, NA Sales Dynamic Logic Google
- 2. Situation Overview Ad serving metrics (click rate, rich media interactions) are standard direct-response measures of campaign performance Some advertisers care more about brand objectives than direct response online Third-party survey-based test/control experiments have become the norm for measuring lift in brand attitudes
- 3. Key Research Question Could ad server metrics (click rate, rich media interactions, expansions) be proxies for assessing brand performance of campaigns?
- 4. Methods (Database Construction) Rich media ads served by DoubleClick that were also measured by Dynamic Logic Final analysis dataset – blinded to advertiser – 4,299 records (creative units) – Contained both ad interaction data and brand impact metrics Merging was performing by independent 3rd party to maintain the blind
- 5. Methods (Metric Definitions) Behavioral metrics – Interaction A person is said to interact with a rich media ad if they hover over it for at least one second – Click-through-rate – Expansion rate Percent of impressions in expandable format that generate an expansion Brand metrics – Aided brand awareness – online ad awareness – message association – brand favorability – purchase intent
- 6. Methods (Statistical Analyses) Correlation – Each brand metric was correlated with each behavioral metric – This was done both on the original scale as well as log- transformed scale for behavioral metrics Linear regression – Models were developed to predict the brand impact metrics as a function of each of the log-transformed behavioral metrics – Adjustment variables included baseline brand levels (brand levels within each ad campaign among those not exposed to the ads), the category of the advertised brand and other factors
- 7. Results Relationship between CTR and Brand Metrics Very weak positive relationship between CTR and both ad awareness and message association Using linear regression, only ad awareness remains statistically significant with a model r-square of 2% Linear Regression 0.40 0.14 0.120 0.12 0.10 0.20 adaw are = 0.10 + 0.02 * CLICKLOG 0.08 0.067 adaware R-Sq uare = 0.02 0.06 0.04 0.022 0.02 0.00 0.00 Ad Awareness Message Brand Opinion Intent -0.02 Association -0.022 -0.04 -0.20 -6.000 000 0000 -4.000 000 0000 -2.000 000 0000 0.00 000 0000 0 CLICKLOG
- 8. Results Relationship between Interaction Rates and Brand Metrics Weak positive relationship between ad interaction rates and both ad awareness and brand favorability Negative relationship with message association Regression analyses revealed statistically significant but practically unimportant relationships (r-squares in the range of 1-3% for the 4 models) 0.15 0.132 0.120 0.10 0.05 0.00 Ad Awareness Message Brand Opinion Intent Association -0.05 -0.048 -0.10 -0.15 -0.162 -0.20
- 9. Results Relationship between Expansion Rates and Brand Metrics Weak positive relationship between ad interaction rates and ad awareness Negative relationship with purchase intent Regression analyses revealed statistically significant but practically unimportant relationships (r-squares in the range of 2-4% for the 4 models) 0.25 0.20 0.188 0.15 0.10 0.071 0.05 0.00 Ad Awareness Message Brand Opinion Intent -0.05 Association -0.053 -0.10 -0.15 -0.20 -0.190 -0.25
- 10. Conclusions Ad behaviors not good predictors of brand impact These results show that rich media ad behaviors such as clicking, interaction and expanding are not good predictors of the branding impact of ads It may be that people interact with ads that are eye- catching or have an interest game, etc. but that this activities may actual distract from delivering brand messages and other brand attributes Suggest using copy-testing as a better predictor of in- market branding success