Driving increased bookings for Solv urgent care partners

Solv Connect is Solv’s marketing and patient acquisition solution for urgent care clinics. Our team’s objective focuses on maximizing the value our urgent care partners receive from their listings on Solv’s marketplace.

According to Solv’s internal data gathered from Mixpanel reports, 35% of Solv’s consumers land on a non-partner clinic details page as opposed to a Solv-partner clinic details page (internally referred to as “CDP”), while searching for nearby urgent care appointments.

This is significant because these clinics are not affiliated with Solv, therefore we do not have insight into their scheduling software making their pages appear as un-bookable to consumers.

This creates a negative experience for consumers who are eager to find care and are instead faced with a non-bookable option. As well as results in a missed opportunity for effectively funneling potential patients towards Solv’s bookable partners.

Identify opportunities across the non-partner clinic pages to boost visibility of nearby partner clinics and redirect traffic towards those clinics.

Our overall objective was to increase ROI for our Solv Connect partners. One of Solv’s 2023 OKR’s was to reach 60% of Solv Connect partners meeting an average monthly ROI of <$25 cost per booking. At the end of Q1 2023, before the project kickoff, we were pacing at 41%. This project initiative aims to boost that percentage by redirecting patient traffic to increase partner booking rates and therefore ROI.

In the first 30 days after release, we saw a
10x improvement in the number of bookings created and 35% booking conversion lift compared to the previous non-partner clinic page designs. Contributing within one quarter to a 15% increase towards our OKR goal of partners meeting Solv’s ideal ROI of <$25 cost per booking.

Due to the nature of Solv as a two-sided marketplace, with any changes to our consumer-facing product we must be conscious of potential impacts to personas on both the consumer and supply sides. These objectives would serve as a backdrop to many of the design decisions made.

Using Mixpanel data, I mapped out the landscape surrounding the non-partner clinic details page (CDP) experience, capturing common entry-points and available user actions. Each of the downstream actions provided an opportunity to funnel traffic towards bookable Connect partners. I highlighted potential design intervention opportunities to help inform where to begin ideation. (see diagram below)

From the user flow analysis, we were able to identify what parts of the flow to focus on. Now, I wanted to begin converging on more specific design interventions by examining the existing interface under a microscope. During this analysis my primary focus was on identifying potential opportunities where we could promote bookable options.

I wanted to dig into what solutions other two-sided marketplaces employed for their partner vs. non-partner pages. Among others, a few examples I analyzed included Zocdoc (one of Solv’s main competitors), OpenTable, and DoorDash. Here were some of the common themes:

During the ideation phase, I wanted to explore several diverging approaches; all informed by the main question: How might we funnel Mary’s who land on non-partner CDP’s to bookable partner clinics? Several design critiques, product reviews, and reviews with marketing and SEO team members helped to converge on a design direction. Below are some of the concepts I proposed and the feedback received.

Collaborating with the product manager and engineer, we set up an A/B/C test to inform which mobile design to move forward with. Specifically, we were monitoring potential impacts on SEO performance, click-through rates, and conversion rates of each option.

We anticipated option A to have the least negative SEO impact but potentially lower performance due to its page placement. Options B and C were used to test two different popup triggers. B activated the popup upon a content-switching event, when a user scrolled upward after having scrolled down on the page. While C employed a time-based trigger, activating the popup 5 seconds after the page load.

30 days post-release of the A/B/C test, the data revealed that the popups had a 55% conversion lift compared to the in-line design. And of the two popups, the time-based trigger outperformed the scroll-based trigger by about 15%. Consequently, we decided to proceed with option C.

Ultimately, it was exciting to see such tangible results and be able to trace OKR improvements back to this project effort. However, beyond just business impact, these design changes helped 10x more patients find the care they needed.

Moving forward, I’d love to be able to identify more design opportunities such as these to help move the needle with our OKR’s. Some of the other design explorations done during ideation left a lot more room for further discovery. I’d be curious to validate if it may be worth following up with any of those designs in the future.