Swipe cards to left to review tracked business trips
QuickBooks Automatic Mileage Tracker
0 → 1 Product for iOS, Android and Web platforms
Product overview
For small businesses, tracking mileage for tax deductions is a high-friction, manual task with real financial consequences.
QuickBooks Mileage Tracker leverages smartphone sensors and machine learning driven classification to automatically detect and organize trips for tax reporting, transforming a manual process into a seamless, accurate system.
Role
Product Designer
Responsibilities
- User research
- Concept testing
- Product design for iOS, Android and Web platforms
- Prototyping
- Service blueprinting
- Creation of location based components for QuickBooks design system.
Business miles tracked
1.3 Billion
Taxes saved
+$730M
Patents granted
4
Research & Findings
The goal of this preliminary research was to understand how self employed individuals tracked their mileage in real world scenarios, their driving patterns and mobile usage behavior. I created a research plan and with the help of my PM, conducted in person interviews, contextual inquiries and ran a survey. We were able to narrow down our focus to these 4 findings:
Most used calendar apps, gas receipts, and pen & paper to maintain a mileage log. But these are often inaccurate due to the tedious nature of tracking every trip.
Designing a digital mileage log
Swipe cards to right to review tracked personal trips
Trip route rendered on map
Groups for similar trips
Handling no trip route information
Handling no trip route information
Bulk review control for rideshare drivers
Insights from user testing
The tracker status
The switch lets users know if their location is being tracked and gives them a control to turn it off if they don't need their locations to be tracked. Users preferred a visible and easy to reach option to have better control over their location privacy.
The trip cell
A trip cell is the digital representation of an individual trip. Users preferred using maps over addresses to identify locations, especially if they drove around a lot as the maps gave them a sense of spatial awareness. Other important pieces of information in recognizing trips were: Date, Time, City of trip start / end and distance driven.
The review mechanism
Users preferred a quick and easy way to sort trips into reviewed or unreviewed buckets. The interaction of swiping to the right or left to categorize trips performed the best as it was easy to do single handedly and during multi tasking.
Phase 1 launch and reception
Phase 1 of QuickBooks mileage tracker was launched in 5 countries (US, CA, UK, AU and SAF) and was fully tax code compliant from day 1.
Business trips logged in year 1
14.5 M
Trips reviewed
79%
Foundations for Phase 2
Once the usage for mileage tracking reached a critical mass, the business focus shifted from user growth and acquisition to user retention.
To define our product roadmap, my PM lead and I decided to do an audit of the E2E product experience, both from a user perspective and an internal process perspective.
We gathered usage metrics across onboarding, active usage and behavior patterns. I conducted a series of contextual inquiries with users, our product team and the customer success team to map the product experience and to identify areas of opportunities.
Users often visit the same locations repeatedly unless they drive for a ride sharing / delivery service. And even then, they have consistent working hours.
Grouping trips
There was already a way to create a rule to categorize trips in the app. But the usage was very low and one of the most common feedback we got from users was that it was cumbersome to go through a wall of trips, recognize them and then categorize them as Business or Personal. To address this problem, I began looking at ways to group trips based on commonalities between them
Exploration of grouping layouts
With the grouping variables identified, I conducted a brainstorming session with the design, product and engg teams to explore grouping layout options.
User testing plan
Testing the design concepts
I created prototypes with actual user data to test 3 different concepts of how the grouping pattern would work in the app.
Testing the accuracy of the algorithm
I worked with the data science and engg teams to design an A/B test focused on the accuracy of the algorithm by removing group descriptions and identifiers.
Concept testing for groups
Concept 1
A familiar grouping pattern. Prominent section at the top of the page. Users felt it was easy to discover and understand. At a glance, they could see how large the group, and the pay off were.
Concept 2
Group is contextual to the trip in focus. Users felt this pattern was intuitive, and easy to understand. However, there was some confusion around multiple access points to review the same trip. There were also feasibility concerns from the engg team.
Concept 3
Group is exposed based on user action. Users mentioned that this felt like a pop-up, and overall this pattern made the groups hidden.
Testing the accuracy of the grouping algorithm
Metrics measured: Avg review rate, review rate in groups, and accuracy in prediction.
Test results
Primary results
- Avg trip review rate increased by 8%
- Review rate for trips in groups was measured at 80%
- Accuracy of predictions was ~87%
- Trip review engagement increased by 15%
- Smart groups concept was patented and the grouping pattern & interactions were added to the Intuit design system
Refined product experience
Based on the results from the 3 concepts tested, the refined grouping layout was shipped.
