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Behavioral Science
November 13, 2023
Explore the true value of behavioral science beyond answering complex questions about defining value and the role of sustainability in purchasing decisions.
Editor's Note: Continuing this series focused on closing the gap between what people say they do and what they actually do, known as the say-do-gap. Each article will look at a traditional research method (e.g., a brand tracker or a concept test) and show how researchers can overlay behavioral science to the method to get a deeper understanding of the why, as well as the what, uncover unconscious drivers and motivations, and close that say-do-gap. Bring on the learning! Meanwhile, in case you missed it, check out Part 1 of this series focused on behavioral science and segmentation: Behavioral Science and Segmentation.
Behavioral Science is often used to answer big complex questions; how do we define value? What role does sustainability play in our purchasing decisions? But to only look at behavioral science through this lens misses the true value of the approach.
Research participants don’t always tell us the truth. That’s not because they’re making an active or conscious decision to withhold, but more likely because we’re terrible at articulating our behavior and motivations.
This creates a gap between reported behavior and actual behavior that can lead to poor outcomes for brands, products, and services. However, there is an answer; behavioral science. In this series, I’ll be sharing how you can apply behavioral science techniques to traditional research methods and close the gap between claimed and actual.
Hearing from the customers in their own voice, going beyond the Likert scale, the depth interview is a powerful tool in the researcher’s handbook. You may think in this format of research it is easier to escape the rational mind of the respondent and get a deeper and more nuanced look at our behaviors and motivations.
But that’s not always the case, and the problems start with the discussion guide. Internally you find stakeholders adding dozens of different questions, moderators are briefed to cover everything, regardless of how the conversation is flowing. Moderators are told to speak neutrally, reveal nothing personal, and make sure every interview is ‘objective’, covering the same ground for comparability.
With this in mind, you might as well do these interviews through an automated chatbot. The need to rattle through the questions before time runs out, administering every interview identically, strips every morsel of personality and rapport from the conversation. By doing this we miss the real value of an in-depth interview and human connection.
By applying behavioral science to this method we can uncover new insights. Narrative qual looks to apply storytelling techniques to the interview as a more reliable way of accessing the memory of the respondent. This is not a one-way street. In narrative qual, the moderator focuses on rapport, and the session is participant-led.
There is room for the participant to ask questions, and the moderator to share their own stories. Ultimately it is a dialogue between two humans, the questions fall away and the narratives create emotional space. It becomes possible to probe deeply and go beyond the obvious, respondents will open up, shared stories will jog memories and feelings, and the insights will be real.
These techniques are particularly effective for topics that are hard to talk about; finances, sex, and health. Your respondents will come up with new ideas and experiences you would have completely missed otherwise.
Depth interviews are as vulnerable to the say-do gap as any other research method. This is because in many cases they are treated as smaller-scale quantitative methods. Take advantage of that smaller scale and let the stories flow, the analysis may take a little longer and lose some comparability. But at the end of the day if you want comparability, use a quant survey. Get to know the real person, and you have a much better chance of closing the gap.
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The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.
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