You were likely in school when you learned the “6 Ws” of writing a basic newspaper article: who, what, where, when, why and how. They are the important questions you need to ask when you’re gathering information, solving a problem, or explaining a purpose.
This basic framework can be applied to more than writing. As a researcher, you may even apply them to survey design.
Today, however, modern survey techniques make it possible to eliminate the need to ask as many four of those “Ws”. HOW is that possible? Because we can connect other data sources, compliantly, that effectively answer some questions without specifically asking them in the survey.
What “Ws” can you answer with connected data sources?
Firstly, a disclaimer. These data points either exist deterministically and/or probabilistically and vary by data set. Deterministic means that we know with certainty that a user exhibits a specific trait. Probabilistic or modelled data takes multiple inputs into consideration to identify users with a high degree of certainty to exhibit that trait.
Who: Skip the repetitive demographic questions
With first-party panel profile data, as well as demographic data providers like Wunderman, Acxiom and Experian, you can append information such as age, gender, geography, household income, occupation, presence of children and much more. Skipping demographic questions can be an easy way to trim time and question count for the respondent.
What: Remove boring behavioural questions
Purchase data can be used to identify grocery and retail shopping behaviour. Items purchased and frequency of purchase can be tied to a user. This can include items such as milk, diapers, eggs, or HDTVs, smartphones, and even cars. By eliminating these behavioural questions, you’re able to focus your survey design on the questions that will provide deeper insight into your initial research question.
Where: Know where respondents are located, compliantly
Location data can be used for much more than determining Designated Market Area (DMA), state or neighbourhood. Shopping behaviour, travel and commuting habits are just a few examples of valuable location data. This information can be used on a macro level to identify patterns and changing trends, or for targeting surveys or advertisements.
When: Understand timing using the same data points you used to learn “where”
Like “where”, these same characteristics and data points can be identified down to the minute. This gives you insight, for example, when consumers are making a purchase, so you don’t need to ask or rely on self-reporting.
Understanding the “Why” and “How”
By integrating other data sources with your survey research, you can bridge the “why” and the “buy” and uncover otherwise hidden insights. Spending more time asking WHY and HOW questions not only deepens your understanding of motivations, but it unlocks greater value from the survey. Instead of focusing on objective data, you’ll collect more unique and subjective insights.
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