IIeX Behavior in Chicago featured a great glimpse into how Behavioral Science, Behavioral Economics, Behavioral Design, and Neuromarketing are integrating into Market Research application. Bigcos like Pepsico, Kellogg’s, and Kimberly Clark shared practical case stories for how they’ve applied these fields to their day-to-day practice of Market Research.
What is Behavioral Science?
While many in the research industry think of behavioral science as a separate application, Terrae Schroeder (Kellogg’s) reframed that behavioral science isn’t a method or a tool, but instead influences how we think about all of the tests and experiments we’re doing.
Behavioral science can involve tech and devices like GSR (galvanic skin response), HRV (heart rate variability) and eye tracking. Presenters cautioned against DIY use of these tools because they’re complicated, many require calibration, and the back-end data doesn’t simply feed into a streamlined decision. A true professional is required. One of the audience members, from the academic world, asked about market research application, emphasizing the challenge: “In academia we have years to conduct the studies you’re discussing, but in market research, you only have weeks or months.” With startup competitors in this volatile market landscape, we’d argue that weeks or months are even a luxury Bigcos don’t necessarily have.
The Garage Group’s application of behavioral science comes to life in a much quicker, cheaper, yet rigorous way: Assumption-Based Development. Teams systematically identify the riskiest assumption that they’re not yet willing to bet their paycheck on before launching a new product or service to market. With the riskiest assumption in mind, teams then design an experiment that sets up a real-world context where the consumer needs to make a decision and have more skin in the game than typical research modes asking about purchase intent. This approach bypasses the research facility, behavioral science lab, and expensive, complicated back-end data outputs from eye tracking, GSR, and more. Rather than create a virtual store and have the consumer shop with a VR headset on, we seek to put the new product in a small set of real test stores. Startup leaders like Alex Osterwalder refer to this as “do” vs “say” testing.
Of the IIeX speakers, this approach is most similar to that discussed by Tyson Labs and their Yappah! launch via IndieGoGo. Stephen Mathews of Tyson Labs shared about their partnership with IoT-backed smart label provider adrich.io. Tyson Labs attached these smart labels to 100 products and shipped them to backers of their Indiegogo campaign to test in-use consumption, tracking things like when the product was consumed, where the product was consumed, with who the product was consumed (social or individual), and more. Instead of having to rely on recall or participants recording their consumption occasions, smart label technology opens up lots of experiment possibilities for testing assumptions about when/where/how consumers will use new products.
Challenges of Behavioral Science Methods
In this new era of behavioral science-informed data, traditional KPIs like purchase intent are overhauled. Speakers in the Insights function talked about the challenge of delivering this new, less universalized data to their multi-functional counterparts. One benefit that likely keeps organizations relying on metrics like purchase intent is simply that there is a shared language that everyone understands. In this new era, KPIs aren’t as unified but with a bit of extra work to bridge the gap and explain the value of these new metrics, corporate innovators resoundingly said that the fight for better data is worth it. So, as we move forward, we expect to need to tell more of a story to support the pivot, perish and persevere decisions teams are recommending based on these new experiments, as opposed to typical metrics.
Here are a few books we added to reading list after this conference:
