In order to model our proprietary attributes onto cookies to provide targeting and addressable insights, we must first understand the relationships between survey responses and online behavior. To do this, Resonate creates a single-source response set of survey answers and online behaviors. Online behaviors are populated through traffic partnerships spanning several million unique domains and hundreds of millions of pages, which are analyzed using advanced natural language processing techniques to identify the topic graph of each person at the attribute level.
When Resonate surveys a respondent, we screen for the presence of this cookie. If the cookie is present, we proceed with allowing the respondent to take the survey, providing us with survey responses and online behaviors for the same person (single-source sample). Resonate does not use the survey to drop new cookies, nor to populate existing cookies with survey attributes. The combination of survey response and behavior only exists within our research database, and is only used to uncover the predictive relationship between the two.
In the course of our survey data collection process, we may collect responses to questions that are considered sensitive, such as personal relationships, income, etc. We ask these questions in a thoughtful, non-biasing manner, demonstrating respect and thereby incurring low levels of survey abandonment at these junctures. As such, it is important that we protect the identities of our respondents. By not receiving any personally identifiable information on our respondents, the sensitive data remains anonymous. When we build models around this data and extrapolate to our full device pool, the data continues to be anonymous, with no links between our devices and PII. Resonate data is not subject to the electronic data restrictions of HIPAA, due to the lack of personal identifiers within the datasets.
Advanced Machine Learning
Resonate uses survey data appended with web behavioral data for the survey respondents to train advanced machine learning models to predict characteristics of web users based on their web behavior. By looking at the patterns of behavior of devices in the wild, using both domain information and natural language processed data, Resonate can predict with significant accuracy the demographic, psychographic, and behavioral characteristics of visitors on the web. Marketers can then use these insights to learn more about who is visiting their web properties, who is responding to their advertising efforts, or determining who to target in marketing efforts.
Artifact of Independent Models of Attribute Values
When a Tag is combined with a survey attribute value together in an audience definition, our methodology uses independent models to produce insights. These independent models may not be aware of follow-on survey questions. Here's an example of a follow-on question: Do you have children? If yes, what ages are your children? “What ages are your children” is the follow-on question, and is only shown in the survey to people who answered yes to having children.
This can result in unexpected insights, such as seeing an insight that says some of your audience does not have children when the audience was defined as having children of a certain age. This only occurs when a tag is combined with a follow-on survey attribute value. We are working to address this within our methodology.
A work around to this is to include the preceding survey question in your audience definition; in this case, add “has children” as an attribute value.