Looking at consumer perceptions of multi rate in an insurance quoting experience.
Recently, we conducted unmoderated surveys with 20 test participants (10 desktop and to mobile) with the goal of better understanding their perception of multi-rate price presentation as found on an auto insurance quote page. It is understood that the survey would be broad and the findings directional in nature. This would provide areas of potential focus for more granular future research
Date completed: September 2021
Design Thinking focus area(s): Empathy; Define
My role: Research Lead
Project Overview
The research that I conducted for this project had 2 goals:
1) Identity consumer perception of a multi rate experience
We have shown multiple rates to consumers on quote pages for some time without understanding the impact of these experiences on conversion and yield
2) Create a scalable pattern for future research work within the team
With a newly formed research team, it is important to set patterns for success and iterate on them in order to ensure a quality, consistent deliverable.
Research Setup
Intake
This project came to the research team by way of a product team. After learning of the request, I set up a stakeholder interview to learn more about the need. We met for an hour and filled out the Research Project Canvas.
Create Test Plan
With the stakeholder interview complete, it was time to create a test plan. Again, a canvas was used to detail the plan. I find these canvases are much more engaging than a text document
While creating the test plan, it became apparent that a moderated contextual inquiry would be the best research method. However, being sensitive to our companies needs, I presented an unmoderated option as well. Ultimately, the product team opted for an unmoderated test conducted using usertesting.com.
🤔 If I had to do the plan over again, I would have probably narrowed the focus a bit. It became apparent that there was a wide net cast and to me the data felt a bit disjointed. Also, I wanted to report the high level results a bit faster. Combing through smaller amounts of data would have sped this up.
Launch the test
With the test plan finished, I created the screener and test in usertesting.com. The target demographic of the product was US-based adults 30-55 who are currently shopping for auto insurance. We launched two tests. Test 1 was for 10 desktop users while test 2 was for 10 mobile users.
Analyzing the data
With all of the test sessions complete, I combed through 20 videos totaling about 6 1/2 hours. This is always the most enjoyable part for me. I love to hear people discuss their thoughts! I put all of the data into a Miro board and began the work of creating affinities from the comments
Findings and Delivery
After a few days of synthesis, patterns and findings emerged and we created the deliverable. The executive summary is pictured below:
With meeting time at a premium, we decided to deliver the results as a deck to the team with a video walkthrough. This allowed the product team and other leadership to review asynchronously and get any clarification they needed. Additionally, 'office hours' specific to this project were set up for the following week for those that needed or wanted to chat live.
Links
Figma
Raw Data
Desktop:
Mobile:
Comments