Quote Agent - From seeing a price
to finding a better one.

A driving score is only valuable if people can use it.

DriveScore helps drivers use their driving behaviour to access car insurance discounts. The app measures how they drive and builds a score they can share when comparing insurance.

That creates a compelling promise: drive well, and it could help you pay less. My role as Lead Product Designer was to help turn that promise into an experience people understood, trusted and acted on.

More quote starts weren’t translating into more purchases.

When I joined, the growth hypothesis was straightforward: get more people to start a quote, and more people would buy. But as we brought more people into the journey, purchases weren’t increasing in the way we expected.

I wanted to look further down the funnel. What happened between starting a quote, seeing a price and deciding to buy? Getting people through the door was only part of the problem.

The opportunity was to help more people reach a decision once they were already in the journey.

“They just want to see a price.”

One idea from leadership was to use AI to make getting a quote quick. The reasoning was understandable: if all someone wants is a price, why make them answer so many questions?

I tested that assumption with the team, using ChatGPT to see what it would produce. In the examples we tried, the answers were consistently wrong. A quick answer wasn’t enough if we couldn’t rely on it.

That test changed the question I wanted us to answer. Instead of asking how AI could generate a price, I wanted to explore how we could intelligently help people find a better one.

AI wasn’t the answer to that quoting problem.
But perhaps intelligence was.

Start with purchases. Then work backwards.

I worked with the team to agree what success meant: insurance purchases per month. That gave us a shared business outcome to work towards, beyond increasing the number of people who started a quote.

Working backwards brought the decision point into focus. If people reached their quotes but still weren’t ready to buy, what did they need to see or understand before they could act?

A discount label didn’t prove a saving.

The commercial team wanted to test a discount tag on the policy results page. The hypothesis was that making the discount more visible would lead to more conversions.

I tested the idea with users. It didn’t land as intended: the tag created frustration and weakened trust. People wanted to see what the discount actually meant. They were looking for an original price crossed out beside a lower one.

The finding gave us an important distinction: telling someone they have a discount isn’t the same as showing them the difference it makes.

An older insight became useful in a new way.

I revisited research carried out before I joined. Users had responded positively to MoneySavingExpert-style tips for reducing insurance costs, such as exploring alternative job titles that accurately describe their work.

Seen alongside the discount-tag research, that suggested a new direction. Could we help people explore relevant changes to their quote, then show the price difference those changes made?

Three ways to help people see more value.

I ran an ideation workshop with the team, using AI to help generate and develop ideas. We explored different ways to connect practical savings guidance with a more tangible price comparison.

Quote Agent: a practical way to explore a better price.

The MVP brought that direction into the quoting experience. Quote Agent surfaced suggestions that could lower a user’s insurance price and let them review the changes before returning to their quotes.

The core sequence was simple: review a suggestion, choose whether to apply it, then see the updated result. The user stayed in control of the information in their quote.

This gave us a way to make a potential saving tangible without manufacturing a “before” price. We could compare a quote with the result of changes the user had chosen.

Find the changes worth someone’s attention.

I researched ways users could reduce their insurance price, then tested which changes had the greatest impact. That work helped us decide which suggestions to include and which to make most prominent.

The aim was to turn a broad set of savings tips into a focused experience: useful suggestions, in a meaningful order, with a clear choice about whether each one applied.

Make each suggestion easy to understand and act on.

The design developed through iteration and user testing. At the heart of the experience was a repeated decision: understand the suggestion, decide whether it fits, and apply it or move on.

The flow introduced what Quote Agent would do, showed progress during the review, and presented suggestions one at a time. Users could apply or skip a change before seeing their updated quote.

Explore the design history

Give people a reason to try it.

The feature needed a story around it. I worked on the narrative and the entry points that would introduce Quote Agent: full-screen takeovers, emails and universal cards.

The central idea was to help users explore whether they could get a better price within DriveScore. That connected the invitation to the feature itself: suggestions they could review, followed by a result they could compare.

Carry the promise into the product.

Once inside, the sequence needed to remain clear. The introduction set expectations, progress showed the review taking place, and the suggestions led to an updated price. Together, those moments made the work behind the result more visible.

An animation language for the journey.

I also developed an animation design language for the experience. This sat alongside the narrative and interface work, connecting the moments of introduction, progress and result.

A shared loading behaviour across different assets

I used a rotating animation as a consistent signal that Quote Agent was working. The same motion idea carried across different asset types, giving the loading moments a shared visual language even when the artwork or format changed.

The aim was to make the movement feel connected throughout the experience. Rotation became a repeatable behaviour I could refine across the animation assets and the in-product loading sequence.

Refining the handoff in code.

I built HTML prototypes to communicate the animation behaviour to developers and refine the details in code. Being able to play, adjust and replay the experience gave us a concrete reference for how the finished interaction should feel.

For the savings celebration, the prototype exposed controls for the confetti’s duration, particle count, launch speed, gravity and settling behaviour. I could fine-tune those settings in context, checking how the animation worked around the saving and policy details.

That made the handoff more precise. Alongside the visual design, developers had a working example and the settings behind it—a shared reference for maintaining the quality of the animation through implementation.

Get value into people’s hands, then build on it.

We delivered Quote Agent in three phases, guided by one principle: get as much value into users’ hands as quickly as possible. The core journey came first, followed by the work to help more people discover it, then the animation polish.

Phase 1 · The core journey

We prioritised the experience that delivered the benefit: reviewing suggestions, choosing which changes to apply and seeing the updated quote. This let people start using Quote Agent without waiting for every supporting element to be ready.

Phase 2 · Go-to-market

With the core journey in place, we focused on helping people discover and understand it. The go-to-market work brought together the proposition, full-screen takeovers, emails and universal cards to give users a clear reason to try Quote Agent.

Phase 3 · Animations

We then added the animation layer: the shared rotating loading behaviour and the celebration when a saving was found. This phase brought greater consistency and polish to an experience that was already delivering its core value.

Helping more drivers move from a quote to a purchase.

45% → 58%

Quote completion

Wider quote-journey improvements · +13 percentage points

18% → 23%

Quote-to-purchase conversion

+5 percentage points · contributing to 21% revenue growth

Best-ever month

DriveScore revenue

August 2026 · highest monthly revenue since launch

Across the wider quote-journey work, completion increased from 45% to 58%—13 percentage points more users reaching their quotes. Completion subsequently settled at around 55%.

Quote Agent built on that foundation by helping drivers explore relevant changes, see the effect on their price and decide whether to buy. Quote-to-purchase conversion increased from 18% to 23%—a gain of 5 percentage points—contributing to 21% revenue growth.

The work also contributed to August 2026 becoming DriveScore’s highest-revenue month since launch.

Together, these outcomes reflect the two parts of the challenge: help more people reach a price, then give them a useful next step towards purchasing a policy.

What did I learn in this project?

A better question led to a more useful product.

The project began with a push for more quotes and a proposal to make quoting faster with AI. Research helped us find a more specific opportunity: help people explore a better price and understand what had changed.

For me, the most valuable work was connecting those pieces—challenging an assumption, revisiting earlier research, making the trade-offs visible and carrying the idea through to the details of the experience.