Decoding the Black Box: Strategic Methods for Tracking Relationships Between Digital Exposures and Behavioral Outcomes

Theoretical Background and research questions/hypothesis: Part of an effective online and offline health behavior campaign includes demonstrating the link between digital exposure and behavioral outcomes. While linking these exposure and behavior can be a challenging task for simple point-of-sale initiative, it is even more so when public health awareness campaigns seek to measure the knowledge, attitudes or behaviors, and ultimately health outcomes for their populations of interest. However, the private sector has made some progress, and insights from their efforts can help make such campaign measurement more effective.

Methods and Results (informing the conceptual analysis):  One such private sector approach to track the relationship between digital exposures and behavioral outcomes occurs through a “search lift ” method, where consumer data is gleaned by examining online search behaviors (e.g., via a Google search). At the core of this methodology is the belief that what consumers search for online is indicative of what they are thinking and potentially doing offline.

Conclusions: After a campaign launches, changes in online search behavior can be measured and used as a proxy for the extent to which consumers are thinking about and/or acting upon the campaign’s content.

Implications for research and/or practice: This “search lift” method is one example of how private sector insight can be leveraged to improve campaign metrics. This will be demonstrated within the context of smoking cessation, in which a recent study found that people who viewed test smoking cessation ads online are significantly more likely to search for information on smoking cessation-related products or services. Using a similar approach, users might track online conversations in order to glean consumer insights on target audience interests and even the language/word choices they make. This discussion will also consider the relevance of these approaches within the first presentation’s use of the AIR Health Behavior Engagement Model, and explore how search data can be used across the consumer journey.