Novo Quote
Rethinking the car insurance buying experience, reducing friction by asking one question at a time with conditional logic to skip what's irrelevant.
Rethinking the car insurance buying experience, reducing friction by asking one question at a time with conditional logic to skip what's irrelevant.
Overview
Novo Quote is Novo's primary customer acquisition product, the main way the company brings in new policyholders. Users arrive from lead partners like Insurify and MediaAlpha, paid ads, and organic traffic.
When I joined in late 2024, the flow was underperforming. The form felt long, some fields were confusing, and users were dropping off before finishing.
The Problem
"84% of insurance leads abandon their quotes, the highest abandonment rate of any industry, exceeding even e-commerce. The leading causes: multi-page forms without progress indicators, too much information required upfront, and poor overall digital experience."
ProPair / Industry Research
The original experience put every question on one long scrolling page: a dense form asking users to process and fill out everything at once. Most didn't make it to the end.
There was also no data pre-fill: users had to retype information Novo already had. And everyone saw every question regardless of their situation, with no sense of progress or whether they'd even qualify.
Underneath sat real complexity: strict regulatory rules about which questions get asked and in what order, plus branching logic that compounds fast. The core problem was handling all of that while still feeling simple and linear.
My Process
A question-by-question redesign meant rebuilding the entire funnel from scratch, which was no small ask. So I built the case first: competitive analysis, funnel analytics, and session replays. The replays were the most convincing part. Users weren't just leaving because the flow was long; they were retyping information Novo already had.
That evidence won over the PM and stakeholders. Along the way I also cleared the smaller usability debt: unclear labels, broken interactions, confusing copy. Rather than rebuild all at once, we phased the rollout over three months, validating as we went.
Competitive analysis, product analytics, session replay review, and getting stakeholder buy-in on the question-by-question direction and pre-fill strategy.
Designing the conditional branching logic, defining which questions could be skipped, and establishing the entry experience for different traffic sources.
Rolling out the redesigned flow in stages across the funnel over three months, iterating based on performance data along the way.
Refining copy, improving rate estimate placement, flagging ineligible users earlier, and polishing the overall experience.
Proposed Solution
One question per screen, and no "Next" button: tapping an answer advances the flow, which cut required taps roughly in half.
"Simplifying registration forms consistently produces hugely increased conversion rates. Removing friction from any step of a form reliably improves completion, and form usability improvements are one of the strongest ROI arguments for UX investment."
Nielsen Norman Group
Answers trigger conditional branching behind the scenes. If a question isn't relevant, users skip it without knowing. If more detail is needed, a follow-up appears.
We surfaced the rate estimate earlier, so users aren't waiting until the final screen to see a number. Rather than updating it in real time (which risks showing the price climb with every driver or vehicle added), we show it at specific moments.
A background check at entry, plus information from lead partners, lets us prepopulate fields for confirmation rather than re-entry, and skip questions a user doesn't need to answer at all.
Most users aren't familiar with telematics-based insurance. We break the concept across a few screens, in visuals and plain language, right when it's relevant.
Previously, some users completed the entire flow only to learn at the end they didn't qualify. We now flag them earlier.
The Flow
Eight steps from first landing to active policyholder. Each one designed to ask only what's necessary, pre-fill what we already know, and keep the user moving.
The user lands and sees what we already know about them, or a clean slate if they're coming in cold. A background check runs in the background to assess risk and prepopulate data where possible.
All household members and potential drivers are added to the policy. Known information surfaces for confirmation rather than re-entry.
Vehicles to be covered are added. Each addition adjusts the rate estimate at the right moment.
Driving history is collected or pulled from the background check. Occupation, prior coverage, and lapse of insurance round out the risk profile.
Users choose from three product variants. The Novo Safety Program is introduced here: a telematics approach that uses Bluetooth to track driving behaviour and adjust pricing over time.
The user reviews their quote in full. Changes can still be made. Last checkpoint before commitment.
Electronic signature, email verification, payment. The user is now a Novo policyholder.
Users download the Novo app and complete vehicle setup to activate the telematics safety program.
Results
Every key metric improved from Q1 to Q2 2025.
The table compares Jan–Mar against Apr–Jun.
| Jan – Mar 25 | Apr – Jun 25 | ||
|---|---|---|---|
| Quote Completion % | 16% | 50% | 3× |
| Quote to Bind % | 4.5% | 9% | 2× |
| Visitor to Bind % | 0.3% | 2.8% | 10× |
| # of Binds | 31 | 958 | 30× |
Time to quote dropped to around five minutes on average, with one user finishing in under forty seconds. New customers also showed stronger risk profiles, since the smarter flow collects better-quality data.
Novo Quote is live and growing. Over 2,500 paying customers since launch, and counting. These are monthly subscribers, not free sign-ups.
Growth was deliberately slowed after launch, trading volume for customer quality. More on that in the reflection below.
Reflection
The question-by-question pattern was the biggest lever, but not an obvious call. It took research to back it up, data to make the case, and stakeholder trust to execute.
What I didn't see coming was the churn. After a strong launch quarter, a meaningful number of customers dropped off within months. Optimising hard for conversion had a side effect: we were letting through users who weren't the right fit. The team tightened acquisition criteria and pulled back on ad spend, trading volume for quality. It was the right call, and it sharpened my thinking about what good conversion design means: not just getting people through the funnel, but the right people.
