Pet Insurance Cost Calculator

A new interactive cost calculator increased quote starts by 47% on the Coverage and Exclusions page and lifted quote completes by 9% overall, during a pre/post analysis test and was then rolled out across both pages and two additional product pages. Prior research had already shown that prospective customers weren't converting because they couldn't tell whether pet insurance was worth what it cost. I led research and design to turn that insight into an opportunity: a calculator, built from scratch within the existing design system, that reframed the decision around real, tangible savings instead of abstract premiums.

Quote starts

+47%Quote starts

Quote completes

+9%Quote completes

Product pages adopted

4Product pages adopted

Previous generative research had already surfaced the core tension: prospective customers understood that pet insurance existed, but many couldn't say with confidence whether the benefits actually justified the expense. That uncertainty was quietly suppressing conversion long before anyone reached a quote form. Competitors in the space had already built tools that made this value equation concrete, and ours hadn't. The client wanted to compete on the same ground.

How might we make the value of coverage transparent enough, and compelling enough, that prospective customers could resolve the cost-versus-benefit question themselves before they ever started a quote?

I opened with a competitive review of how other pet insurers were solving this same problem, to see what worked, what didn't, and where there was room to differentiate. From there, I ran several rounds of design exploration: testing directions that illustrated savings for specific, common conditions, appealed differently to cat vs. dog owners, and used progressive disclosure to avoid overwhelming users with every stat at once.

Each of these early frames tests a different way to make a savings number feel concrete rather than abstract: naming a specific condition, splitting the framing by pet type, or hiding secondary detail with progressive disclosure. Reviewing these side by side with the team narrowed a wide field of ideas down to two finalized directions with distinct visuals and copy.

Version A anchored on a single high-cost condition, dental cleaning, with a dropdown to swap in other conditions. Version B organized costs into illness, accident, and preventive-care categories, navigated with tabs and pill filters. To decide between them, I designed and moderated a first-impressions and preference test with 5 participants. Each participant viewed the static design for 30 seconds then answered questions about purpose, first action, takeaways from the visual. Then they were shown both prototypes and answered a few more structured questions about purpose and preference.

• 4 of 5 participants preferred Version B
• Both versions clearly communicated the calculator's purpose at a glance
• Version B's category pills and tabs invited more interaction than Version A's single dropdown
• Leading with "savings" instead of "cost" resonated strongly with participants
• Version A's graphic tested as dated, and its single dental-cleaning example felt too narrow

The two short prototypes below were shown to participants. Both with the same savings-forward framing, tailored per pet type.

I worked closely with the visual and UX designers to translate the testing signal into this final, refined component: Version B's categorized structure and savings-forward framing, with polished visuals and a left-side navigation pattern that replaced Version B's dated graphic and tightened up the interaction from the tested prototype.

Research only changes decisions if the people making them see it firsthand. I brought stakeholders, engineers, and designers into research sessions whenever their schedules allowed, and shared full session recordings with anyone who couldn't attend live, so the findings landed as direct evidence, not a secondhand summary. Rather than push for a full launch off a 5-person impressions test, I recommended we ship the component behind an A/B test focused specifically on quote-start lift, giving the client a lower-risk path to validate the decision with real traffic before committing to a wider rollout.

The categorized, savings-forward direction validated in testing became the shipped component, and it delivered. Quote starts rose 47% on the Coverage and Exclusions page, the first page it launched on, and quote completes increased 9% overall. On the strength of those results, the client adopted the calculator as a reusable pattern, rolling it out across four product pages: Coverage & Exclusions, Dog Insurance, Cat Insurance, and Is Pet Insurance Worth It.

• Quote start rate up 47% on the Coverage and Exclusions page
• Quote completes up 9% overall
• Rolled out to 4 product pages: Coverage & Exclusions, Dog Insurance, Cat Insurance, and Is Pet Insurance Worth It