Designed new product for healthcare marketing teams to manage online reputation and consumer feedback.
- Introduction
- Opportunities We Saw
- Business Objectives
- Design Process
- “Engage” - Review Response MVP
- “Collect” - Review Boost MVP
- Adding to “Engage” - Canned Response
- Adding to “Engage” - Service Recovery
- Iterating on User Feedback - Review Filters
- Strategic Planning - Improving Response Time
- Embracing Innovation - AI Generated Response
- Outcome and Impact
- Learnings
Introduction
Healthcare consumerism has emerged as a growing trend in the healthcare industry, where patients search for, consider and decide on care more like consumers. In early 2022, Loyal decided to build a new online reputation management solution to help health systems adapt to this changing landscape.
Over two years, I led the 0-1 design of Loyal's online reputation management product, the very first of its kind in the healthcare industry. I collaborated with PM, engineers and cross-functional teams to deliver the MVP and iterate upon it based on customer insights, industry research, and business strategies. As a result, the online reputation solution, along with the broader reputation suite that includes two other products, acquired five enterprise and mid-market customers, generated $2.3 million in contract value, and contributed to Loyal's product-market fit.
This case study showcases my end-to-end design process, and you can delve into my approach for each feature if interested 🧐. The product leveraged multiple data dependencies across Loyal's products and third-party platforms. I collaborated a lot with engineers to map complex data relationships, which will also be elaborated further in the respective sections.
Opportunities We Saw
In this evolving healthcare consumerism landscape, having a good digital presence is crucial for health systems to attract and retain patients effectively. We identified several key insights:
- 94% of healthcare consumers use online reviews to evaluate care choices
- 73% of consumers say 4 stars rating is the minimum for them to engage
- 89% of consumers read businesses’ response to reviews
- And it’s 5-7 times more expensive to acquire a new patient than to retain an existing one
As a healthcare IT company, we realized that there were no tool designed specifically for health systems to manage their digital presence effectively, and we recognized our unique position to develop such a product. Our existing products provided a strong data foundation for the new product to leverage. This foundation, combined with our willingness to understand and address the specific needs of the healthcare industry, positioned us to create a comprehensive solution for the health systems.
Business Objectives
Given the opportunity identified, we decided to build an integrated healthcare-specific online reputation management product for the healthcare marketing teams, that empowers our clients to better attract new patients, retain existing ones and build trust and loyalty within their community.
The product comprises three major components to help health systems achieve their objectives in online reputation management:
Design Process
“Engage” - Review Response MVP
Overview
We started the product with the Review Response feature to help users "engage" with consumers, as this simple yet crucial functionality would bring immediate value to our clients. Without such a tool, users had to go to each listing manager’s website separately to respond to reviews. Design goal: Enable users to respond to online reviews directly within our product.
Challenge: Users had significantly different ways of managing reviews with their existing tools. My focus was on identifying the common needs and ensuring the MVP addressed these core needs effectively without favoring a specific workflow.
Outcome: I designed the MVP with all the basic functionalities, ensuring it was flexible enough to meet the diverse needs of different users. I also worked with engineers to figure out data dependencies and mappings, ensuring a streamlined experience on the UI.
“Collect” - Review Boost MVP
Overview
Once users could respond to reviews within our product, the next major step was to help them acquire more reviews. This process had been a huge pain point for our clients, as even with existing tools, a large portion of the process was still very manual.
Design goal: Enable users to solicit reviews easily from consumers after visits.
Challenge: My task was to design an innovative solution to address a huge pain point. With only one client to interview, whose experience was very specific to their existing tool, my challenge was to extract key insights and develop a streamlined solution that would simplify the experience for all users.
Outcome: I designed the MVP of the review boost feature to specifically address some needs and pain points of health systems, which generated a lot of excitement among potential clients.
Adding to “Engage” - Canned Response
Overview
With the completion of all the key MVPs, our immediate follow was the canned response feature, to improve one of our key metrics - response time.
Design goal: Enable users to respond to reviews more efficiently through pre-configured response templates.
Challenge: With limited research resources, I had to design based on many assumptions, and ensure that my design was flexible enough to accommodate various potential user needs.
Outcome: I designed and prototyped functionalities for both configuring and applying canned response with a flexible labeling system. However, we later decided to scope down and ship a minimal version first because of changes in client needs.
Adding to “Engage” - Service Recovery
Overview
While working on the previous features, I learnt that many users would escalate online reviews for a process called “service recovery”, to have the team address the negative feedback as early as possible. This is a critical workflow for health systems, as it plays a key role in patient retention and maintaining their reputation.
Design goal: Enable users to escalate and manage reviews for service recovery effectively.
Challenge: Clients have very different processes that involves various stakeholders. Extracting common needs and scoping were the key to the success of the MVP.
Outcome: I designed new service recovery functionalities on top of the existing review response feature. By leveraging insights from previous usability tests and task analysis, I iterated on the layout of the review card, adding more actions while maintaining an intuitive user experience.
Iterating on User Feedback - Review Filters
Overview
After over a year of hard work, we finally launched our product, and had our first client begin using the review response features! The first feedback I received from real users was their need to filter reviews by markets, locations and providers.
Design goal: Enable users to easily filter reviews by markets, locations and providers.
Challenge: It was a complex filter set due to the dependent relationships between markets, locations, providers and their listings. I iterated on multiple approaches, going through rounds of design, prototyping and usability tests.
Outcome: I designed a set of flexible filters that aligns with user’s mental model, allowing them to easily filter as needed. I clearly communicated the design and interaction details to the development team through prototypes, videos, and annotations.
Strategic Planning - Improving Response Time
With the core table-stake features in place, our next step was to further improve the product guided by product metrics and user needs. We primarily focused on improving response time, a critical metric for our review response feature.
Our team brainstormed several ideas to enhance response time. To assist my PM in developing a strategic plan, I broke down the ideas into smaller tasks, consolidated insights from sales conversations and client meetings, and created diagrams to help determine priorities. Since our focus at that time was on sales enablement, we prioritized features not only based on their importance to current and prospective customers but also on how we could better showcase our future capabilities to prospects.
Embracing Innovation - AI Generated Response
Overview
Among all the ideas we explored to improve response time, AI generated response was particularly interesting.
Design goal (initial): We started with the idea of fully automating review responses, as we focused on sales enablement and wanted to showcase exciting roadmap features to win deals.
Design goal (after research): After user research, we decided to narrow our focus to enabling users to respond to reviews more efficiently through AI suggested responses.
Challenge: Given a broad and ambiguous idea driven by market interest, the biggest challenge was to determine if the idea itself made sense and how to develop a solution that truly benefits users and meets our business objectives.
Outcome: I conducted user interviews to understand their needs and perceptions around automating review responses. This research led to a concrete solution that addresses user needs while driving sales. I delivered the design and also collaborated with the AI Labs team to train the model behind the feature.
Outcome and Impact
✅ ACQUIRED 5 ENTERPRISE CUSTOMERS
As of writing this case study, our product had already gained traction with one happy client actively using it, two clients nearing their onboarding date, and two more who had signed contracts. Additionally, one client was negotiating a product trial as they recognized that our review boost feature was far more efficient compared to their existing tool. We also have a strong pipeline, with numerous prospects showing interest in the product.
✅ 2.3M TOTAL CONTRACT VALUE
While I primarily focused on developing the Online Reputation product, I later became the designer for the entire Reputation product suite, supporting two additional products. Online Reputation was sold as part of the suite, helped make the suite a standout option to the customers, attracting lots of interest and resulting in a total contract value of $2.3M.
Learnings
It was an interesting and rewarding experience to navigate all the challenges and ambiguities throughout this design process. I collaborated with a director, multiple PMs, engineering teams, UX researcher, AI team, sales, customer success, etc. I learnt to be versatile, flexible, quickly adapt to changes, and stay focused despite all the context switchings. I’d also be happy to share more about these experiences:
- Dealing with research constraints
- Designing with unknowns and uncertainties
- Scoping and rapid iterations
- Designing for diverse user profiles
- Navigating changes in team structure
Thanks for reading!