the RepairPal estimator is a core product that allows consumers to see accurate estimates for repairs and book appointments with shops that honor those estimates. It is core to the company's mission of bringing transparency to auto repair.
Based on qualitative and quantitative data, the estimator as is does not offer its potential business value.
Design Lead
May - November 2019 (7 months)
Figma, JIRA, Usertesting.com, Mixpanel
Create a transparent process for users that inspires trusted confidence. Users need to navigate through the flow with as little friction as possible. Our solution tackles the existing pain points within our estimator and layers in innovative additions to assist in our customers' repair journey.
Part of that is rooted in providing as much detail as users need regarding their selections step by step. We assist them in diagnosing car problems as accurately as possible in order to bridge the gap with unfair price quotes for those who are not as well versed in car mechanics versus those who are.
The kickoff included existing research the company has collected over time. When we started this project, we didn't have specific personas in mind since car repair spans across all (or most) demographics. The market research also informed our approach with user empathy and average confidence within the space.
A study conducted by Northwestern University (2012-2013) where men and women called the same group of repair shops to gauge quotes on a Toyota Repair, estimated to cost $365, revealed:
The North Star for our company derives from the lack of transparency within the car repair community. The estimator tool is one of many features built to combat unfair pricing quotes, and in addition to that, bring confidence back to the consumer.
50% of users complete all steps of the estimator, with the largest drop-off at the ZIP and service inputs.
33% of users have said they were satisfied with the results the estimator provided.
0.1% book an appointment after receiving an estimate.
Users need a quick way to pinpoint their car repair needs through our tool with as little guesswork as possible.
The end result of using RepairPal's estimator provides users with a complete breakdown of the price, including parts and labor costs, so they always know what to expect.
We partner with repair shops all over the country (500+ and counting). We direct users to our extensively vetted shop partners, providing confidence in their physical shop experience.
Target: at least a 10% increase in our estimator completion rates. Currently, only 30% of users complete the end-to-end flow.
Target: 15% increase in conversion rates from the initial 0.1%. Hypotheses for this measurement were rooted in UI clean-up for quick wins.
Goal: hit 50% or above when measuring user happiness and satisfaction through our NPS surveys.
An overview of the UX process applied to this project. Informing on what we started with, who we targeted, how we approached research, what we discovered, and the outcome.
We wanted to focus on the best strategy to understand how users gain value and confidence within their repair journey and translate that into a delightful digital experience.
With a core focus on empathy, we aimed to view product and market space pains from multiple angles. Research shows when car problems arise, the initial feelings are nervous, frustrated, and overwhelm. There is a specific focus on negative emotions attributed to cost fears. Research also shows the majority of issues that people experience with their cars are not covered by a warranty.
Empathy creates and maintains humanity for the people we are designing for. When people think of car repair, they don't think of "humanity". My approach for research was to start with a set of questions to gauge a user's emotional state depending on which stage of the repair process they were in.
We wanted to interview users that had undergone a car repair in the last 3-6 months within the age demographic of 21-50. We were seeking to gain an understanding of a user's mindset (feelings and concerns) as well as their approach (talking to family, friends, or individual research) when seeking repair guidance. We wanted to understand the considerations users made with online search results, driving past shops on their commute, or word-of-mouth recommendations.
We then scoped our interview questions to focus our conversations on users walking us through their end-to-end journey, starting with when they realized there may be an issue with their car.
Prior to speaking with users, we ran a query on the most common car repair searches and the standard process users follow when searching for mechanics. This formed a "skeleton" process for us to compare with what users were saying.
For usability testing, our sessions consisted of 3 unmoderated groups. All participants had outlined criteria to meet to be selected for the study.
The first test had participants go through the existing RepairPal website. This gave insight into how users felt about the current design and focused on any pain points, struggles, and areas of delight. We utilized these results to compare against further testing on proposed design iterations and weigh the pros/cons against all variations.
The second and third tests were a usability study with participants that had scenario-based tasks alongside an interactive prototype that contained our hypothesized feature updates and additions. These tests were focused on prioritizing which features should be involved in MVP, as well as assessing their ranked value.
We asked users to apply their previous repair journey to RepairPals estimator flow to provide feedback on the current experience.
8/20 participants used the estimator at some point during their exploration. Of those 8 participants, 5 had difficulty navigating the services portion of the estimator due to it seeming disorganized and gave up.
7/20 participants got a quote during their exploration of our competitor and all 7 responded positively to the search function, the 'Diagnostics & Symptoms' section, and the way the services were organized.
The disorganization of RepairPal's page design seemed to discourage participants. Users felt their lack of car knowledge was being highlighted when they were unable to find what they were seeking. 1 participant said "I feel like the repair calculator on RepairPal is too specific”
The Discovery Research Phase insights formed initial bets and assumptions based on the pain points and flow gaps we identified. The areas that were hypothesized to drive the most impact were:
Participants preferred our updated designs as compared to our legacy designs. Here's why.
We added a 3D car image for users to interact with when it came time to select a possible diagnosis. Users were allowed to select from 3 different car types: SUV, sedan, and truck. This improved the experience for users with little experience in car knowledge by providing accuracy when diagnosing due to using visual indicators.
Participants were able to reach their "a-ha" moment sooner by the added educational tools and dialogue provided within the "diagnosis" step. Their "A-ha" was understanding how to properly find and communicate the symptoms of their car issues.
Re-organizing the information architecture on the page allowed users to quickly comprehend and process information. Shifting the estimator steps to a progressive disclosure model rather than a "see everything all at once" model, users were able to digest the content.
In the new designs, we tested out car imagery for every make and model from our database to populate images when users were at the "select year, make, and model" step.
We learned that users did not respond as quickly with images for car make and model vs plain text lists due to their emotional connection with their vehicle. Users would spend time searching for a car that looked exactly like theirs instead of a basic stock image which caused them to overlook their intended selection.
Users liked the idea of utilizing an image to assist in their diagnosis when selecting from the diagnosis dropdown but having too specific representations of car color and other details caused users to overlook it due to their emotional connection. Utilizing a skeleton model for vehicle type, users would have no emotional connection to the visual, but would easily be able to select what they knew based on senses.
The majority of users did not know what was wrong with their car but knew where the problem or noise was stemming from. If a user is not technically proficient or familiar with mechanics, they can utilize an interactive vehicle image (example above) to select areas they might associate with their 5 senses such as touch, sight, or smell. This approach layers into our idea of "humanity in repair" within the designs.
If a user is technically proficient or familiar with mechanics, we still offer a quicker method of searching in a database or selecting from drop-downs with "top" or "popular" issues based on make or model.
These two methods allowed users who became overwhelmed quickly to easily navigate their experience, and also offered the experienced users a version that did not feel "dumbed down" (as quoted by some experienced users).
The designs we landed on after research, discovery, testing, validating, and ideating.
Progressive Disclosure and Estimated Time: I structured the process with a progressive disclosure model to simplify the user's view when making selections. I wanted to signify to users the amount of time they should anticipate spending on the process, and additionally orient them on a clear path following completion of each step, including what follows after receiving the estimate. Research showed if users did not have visibility into the amount of involvement, they would leave the page.
Affordances Through Structured IA and Options: I moved away from the drop-downs used in the legacy designs. I tested the idea of listing out all available make, years, and models in steps (steps 1, 2, and 3) to provide users a high-level view of options in order to make quicker decisions with fewer clicks. I also added the ability to “clear” input fields to start over or request other quotes without having to leave the current flow, whereas the original design would redirect you back to the start if you wanted to change input.
Inclusive of Persona Type: Users have the ability to select areas of the car based on the image populated, search through our database, sort or filter by most popular selections for this car make/model/year, and routine maintenance that is anticipated with this vehicle. This reduced information overload and any paralysis users experienced when they hit dead ends due to lack of repair knowledge, and alternatively, offer paths that would appeal to informed users.
Additional Value and Return Users: At the end of the flow, once users select their anticipated repair, they receive a price estimate range. Below the detailed breakdown, we provide a new entry point to our directory of certified repair shops. This provides users the ability to make an appointment and send their repair details directly to their shop of choice. This step aimed to increase marketplace awareness and shop appointment conversion %. The 2 experiences were separated in legacy designs which led users to exit RepairPal once they received their quotes.
