- Interaction
- UI
- Analytics
LocalMed was in the pivot phase of growing its business. This is how I used analytics to help LocalMed's mission through improved User Experiences.
We wanted to show potential customers the value of our product. To do this, I set up analytics to track conversion funnels, heat maps to see where users were focusing, and videos to enable user observation in spite of no research funding. When we analyzed the data, we saw one thing clearly.
Users were confused about the process.
When the user hits the schedule, we showed them available appointments. But, they had not specified what appointment type they needed. Changing their appointment type often resulted in no available appointments. Where did that schedule of available appointments go?
If the user changes their type to a returning user, we would ask the user to select an appointment type, which would allow them to select appointment types which had no openings. Why are we showing appointment types for which we have no openings?
The analytics would confirm what we can already see. To solve this, I worked with my business stakeholders to draft on OKR. From these I formulated a hypothesis. If we can drive user investment while also reducing the steps to schedule and only showing relevant information, we can improve conversions and improve our sales story.
The first step would be asking the user for the type of appointment they'd like to schedule up front. The appointment types available would only be appointments with openings and the user could quickly jump to another provider with a single click if appointments of their choosing weren't available.
This work would solve the primary issue we saw in testing - appointments disappearing. But in testing we would find another issue, no matter how much we improved the top of the funnel, appointments were erased at the last step.
The initial implementation of the final scheduling step did not retain the user’s selection from the scheduling widget to LocalMed’s final scheduling UI.
To solve this, I developed a simple URL query pattern that could be passed to LocalMed’s site to maintain the user’s previous selections.
After running A/B tests we had our results – conversions as a whole increased with Final Step Conversions improving from 60-70% and overall conversions improving from 48% to 67%.
And, well, that gave us our sales pitch.