This is an data-driven visual explainer detailing what “psychobehavioral segmentation”is and how we can use this approach to drive vaccine uptake. Rather than just looking at demographics, the approach divides people into segments based on their behaviors, motivations, and beliefs, which allows us to understand individual’s unique barriers and develop targeted solutions.
The explainer was able to distill a complex technique,commonly used in marketing but not as well known in public health, to something easily digestable to the public and decision makers. It was also extremely timely given that we applied the approach and provided suggested solutions at a time when vaccine uptake in the U.S. was plateauing.
Using our rich nationally representative survey of 2,747 U.S. adults, accompanied by our full reports, developed framework, and press releases, we were able to provide a resourceful package for local decision makers to easily apply such approach to their communities to increase vaccine uptake. We also collaborated with several health orgs to develop piolit programs within their priority groups by explaining the approach with the explainer, as well as using the vaccine personas quiz developed by our data science team. Finally, we also collaborated with the New York Time on an opinion piece in order to reach a wider audience.
For the narrative and design, we started by identifying the main message, which is that targeting only “demographics” isn’t enough. Instead, we need to target people’s barriers and beliefs.
We then developed storyboards and brainstormed how to best visualize the data. We decided that D3 animated circles can show how each segment, when created based on people’s barriers and beliefs, includes at least some of every demographic.
What was the hardest part of this project?
Due to a tight timeline and the timeliness to have a professional and functional product to make the most impact on driving vaccine update, the narrative and design were being developed in parallel to the interactive development and implementation, and new survey data was coming in at the same time, so we had to learn to be both efficient and flexible in our collaboration of design (Daisy) and code (Julia), and make many iterations as we update new data and narrow down our core message.
Moreover, it was not very straightforward to develop this story across screen sizes and devices, since the way the visualization is set up requires more vertical space, and that was true especially for PCs with certain aspect ratios, so there had to be various workarounds so that not too many crashes happen between the data visuals and the key when the circles animates.
What can others learn from this project?
When developing projects that is incredibly timely (such as for COVID), it is especially imporatant to balanace impact and design/project goals. Of course we strive always for quality product, but if we want the design and graphics to be absolute perfect and iterate for a long time, then the project will loose it’s impact if it doesn’t come out to the world! Rather, identify your priorities and what you are willing to compromise at the beginning. For us, it was important to not comprimise the quality of the data analysis , reporting and our core message, but we opt for a more simple graphic design aesthetics so that it won’t take as much time and can be more adjustable. Even though it was still quite a challenge , we were able to create the explainer, from idea to sketch to final, in 4 weeks, with mainly just two people working on the project.