Cause of pain test
Building trust with patients by asking about their cause of pain
After the launch of our care-forward onboarding, we started running user tests to gather qualitative feedback to supplement the quantitative data we were gathering from patients. Although the new onboarding was doing a better job of showing our value, we still struggled with building trust with a highly skeptical patient base.
I began noticing in these tests that people often wished we asked about their cause of pain. Although some pain patients were unsure what might be causing it, others were very attached to their diagnosis and became highly skeptical of us when we didn’t ask about it. With this data in mind, I designed an A/B test introducing a question that asks patient about their cause of pain.
Lead growth product designer, working with a growth PM & lead engineer; supported by product design team.
Building trust with pain patients by asking them about their cause of pain.
116% improvement in conversion rates in patients who interacted with cause of pain screens, proving to be statistically significant.
HYPOTHESIS & GOALS
To frame the purpose of this test, we gathered baseline data and formulated a hypothesis on which to base our results.
Overall funnel conversion rates sat at around a 2% for the new onboarding flow
We were noticing that many patients tried to give us their diagnosis at various touchpoints (ie. when we asked about their goals or pain locales)
Our patient experience team frequently received the question, ‘can you treat my type of pain?’, but we didn’t address this question during onboarding
In qual testing, patients continuously told us they wanted to tell us about their diagnosis to find out whether we treat their specific type of pain
By building in a moment of legitimacy in response to ‘can you treat my type of pain?’ we believed that we could increase conversion rates because we are building enough trust with our patients for them to decide to give us a try
By exposing suggested causes of pain, we established ourselves as a legitimate provider in pain care as we are showing what types of conditions we are able to treat
By reassuring patients, regardless of where they are in their pain journey, we are providing a moment of comfort and education that helps us build trust
Higher buyer intent leading to overall higher conversion rates
After an initial discovery period defining our problem and hypothesis, I approached Dr. Jacob Hascalovici, our Chief Medical Officer, to talk about the type of conditions we can treat. He provided a list of conditions that we are either already treating or have the ability to treat organized by pain locale.
What I thought was most interesting about this list is that it was organized by where someone was feeling pain, and for the most part, a lot of conditions were repeated in different pain locales. I had an initial idea that we could help establish our legitimacy by surfacing potential causes of pain based on 1) where they told us they are feeling pain and 2) the most common conditions based on pain area.
I also started thinking through how to best speak to patients who may be at different points in their pain journey. We know we have patients who have a clear diagnosis, while others have no idea what’s causing their pain, and others who have received dozens of diagnoses over the years. The attitude of each of these personas is entirely different so the way we asked about their cause of pain, as well as our response, had to take that into account.
I decided to test framing our question as ‘Do you know what’s causing your pain?’ with three options to choose from: yes, no, & it’s complicated. This prevented limiting responses to diagnosis, allowing patients to instead tell us about any accidents, injuries, or trauma that could be causing their pain.
I went through a week-long exploration process where I user-tested ways of collecting conditions as well as potential responses to surface to patients based on whether they told us yes, no, or it’s complicated. I decided to leverage existing components we were already using in onboarding, as we had proven that these patterns were easy to use for our audience. I also developed a cause of pain logic to limit the number of suggested conditions based on pain locale to six.
I developed a final solution to launch as an A/B test – the design process took about two weeks, with another two weeks of build and QA
We ran the A/B test for about two weeks – in that time, we were able to reach a statistically significant result.
Patients who experienced the new set of screens converted at 4.11% vs. those prior at 1.91%. This is an increase of 116% improvement and statistically significant.
When we looked at patients between the steps BasicInfo to Trial, the set of screens converted at 9.03% vs. no screens at 3.47%. This is an increase of 97% improvement and also statistically significant.
We could confidently conclude that top-of-funnel changes can have a long-tail effect on the rest of the funnel by generating intent through building trust.