Using behavioral data to learn about consumer preferences and behavior is a very hot topic for marketing researchers. After all, we all know that surveys aren’t perfect: our samples may not be representative, our respondents’ memories may be flawed, our survey questions may be badly designed, or we may not use the correct analytical technique. Moreover, even if all of those factors work perfectly, the fact is that survey data is limited to a sample of respondents at one point in time. Researchers have always struggled with these limitations, so it is no wonder that behavioral data, with its promise of ongoing, inexpensive, accurate data about what consumers actually do, is very compelling.
Collecting and analyzing behavioral data is not new, of course. Marketing researchers have been using behavioral data for years, mostly in the form of qualitative techniques, including observations or ethnographies. While these approaches can be very useful, they utilize very small sample sizes, which limits their value for solutions to marketing problems.
The behavioral data that is currently available does not have those limitations. We now have data from browsing activity, search behavior, app usage and activity, and online purchase and return behavior. It is a fairly easy task to track customers and website visitors to your website – everywhere they go on the web other than your website – and then create marketing strategies, tactics, and campaigns based on individuals’ behavioral profiles. (All data collection software has unique but similar tools for linking survey data to online behavior. See this blog for a detailed description of the process with QuestionPro.)
The Insights Association recently posted a document from the Center for Democracy and Technology (CDT) that gives guidelines for using this type of data. CDT defines online behavioral market research data as: “Information about a data subjectʼs Internet activities, collected for the purpose of generating online market research findings, that is observed or inferred or has not been reported by the data subject for the purpose of online market research. “The CDT goes on to further define the types of data included:
- “Clickstream data: Data collected about a data subjectʼs website visits or other online activities for the purpose of generating online market research findings, with or without the data subjectʼs awareness. Some current examples are the data subjectʼs IP address and cookies, the date and time of the activity, the URL of a requested site, the data subjectʼs browser and operating system types, the links the data subject clicks on, and the referring URL.
- “Communication content: The substance of a transmission destined for one or more specified individuals (as opposed to a site, service, or application). Current examples include the subject and body of an email and the content of a voice call or text chat.
- “Online behavioral advertising: The collection of data from a particular computer or device regarding Web viewing behaviors over time and across Web sites for the purpose of using such data to predict user preferences or interests to deliver advertising to that computer or device based on the preferences or interests inferred from such Web viewing behaviors.
- “Ongoing data collection: The collection of either self-reported market research data or behavioral market research data either continuously or at multiple instances, such that data from one instance or period in time is associated, at the individual level, with data from another instance or period in time or with data from other sources.
- “User-generated data: Data generated knowingly by a data subject. Some current examples are search terms, input into online form fields, and posts on public forums.
- “User interaction: The transfer of data between a data subject (or his or her device) and a site, service, or application.”
The science of analyzing online behavioral data is now widespread in businesses of all sizes. However, what is missing from this data is the descriptive data (demographics and attitudes/beliefs) that help us understand who is behaving this way and why the behavior is happening. That’s where surveys come in. Because of the ease of tagging survey data with online identification, combining online behavior data and survey data becomes easier and faster for marketing researchers. And that is where the true insight happens!
Watch for our next blog on this topic: Combining Behavioral and Survey Data for Better Insights!