Case Study | Personal Auto and Commercial Vehicle Insurance Quotes
Conversion Optimization Program
An iterative, UX-led optimization program across revenue-critical insurance quote experiences. Built to reduce abandonment, protect lead quality, and improve estimate trust so more users convert into agent-ready opportunities and bound policies.
View wins
At a glance
Conversion impact
The optimized quote flow delivered 14x more completions in the last A/B test window, taking completions from 10 to 146 and raising estimated value from $27,150 to $341,000.
Friction removed
Reduced early abandonment by removing unnecessary upfront data capture, simplifying steps, and re-sequencing the flow around buyer intent rather than internal data needs.
Trust and revenue signal
Introduced an accuracy framework to connect digital estimates to final agent pricing, creating clearer measurement of quote integrity and downstream close potential.
Context
Quotes are a revenue product, not a form.
Quote experiences sit at the intersection of acquisition, qualification, and conversion. If the first screen creates hesitation, the revenue pipeline collapses early. The work below shows a deliberate progression: first remove friction to increase completions, then add controlled signal to improve lead traceability and quote trust.
Iteration timeline
Phase 1. Conversion lift (A/B test)
Addressed a major abandonment problem right after the first step by reducing friction, cutting steps, and improving clarity and SEO alignment to capture more high-intent demand.
View Phase 1Phase 2. Quote integrity and revenue confidence
Added a scalable measurement layer to close the expectation gap between digital estimates and agent pricing, while protecting conversion performance with a phased data strategy.
View Phase 2Phase 1
A/B testing a lower-friction quote flow
The objective was to reduce severe abandonment after the first step and increase completed quote journeys. This work treated drop-off as a revenue leak, then removed the highest-friction requests that were not essential to show an estimate.
Primary KPI
Reduce drop-off after step 1, where the funnel showed roughly 77% abandonment.
Secondary KPIs
Completion rate to results, time-to-complete first step, interaction by screen, calls initiated, and SEO performance for high-intent keyword alignment.
Test design
Optimizely A/B test with a 50/50 audience split, monitored until sample size supported confidence in outcomes.
Phase 1. UX changes tied to business impact
Reduced steps
Cut the flow from 5 steps to 4 to reduce perceived effort and increase completion probability, translating into more sales opportunities.
Removed early PII
Eliminated upfront data capture, including redundant zip entry, to remove a major early barrier and reduce abandonment.
Removed qualification friction
Temporarily removed qualification checkboxes to reduce uncertainty and unblock forward momentum, increasing the volume of completed flows for sales follow-up.
SEO clarity
Aligned headline and supporting copy with high-intent search language to support long-term organic growth and reduce CAC.
Phase 2
From conversion to quote integrity
After establishing a high-performing conversion baseline, the next iteration focused on revenue confidence. The goal was to keep completions strong while introducing a scalable way to measure how closely the estimate matches agent-provided pricing, improving trust, transparency, and close potential.
Risk-aware rollout
Adopted a phased optimization approach so improvements could be validated without jeopardizing a strong-performing funnel.
Minimal signal first
Reintroduced only one lightweight data point first (phone number) to connect sessions to agent outcomes while protecting conversions.
Accuracy as a product metric
Added an estimate-to-final comparison model to detect edge cases, monitor quote reliability, and support leadership reporting.
Phase 2. System design
Session-level identity
A unique session ID is generated early to support consistent tracking across the journey and downstream operations.
User-facing code reveal
The same ID can be surfaced later as a “special code” to encourage calls and improve handoff clarity at the moment of action.
Accuracy calculation
Compare the estimate shown in the flow with final agent pricing, producing a per-user accuracy signal and an aggregate benchmark.
Edge case detection
Flag sessions beyond a defined variance threshold so pricing logic gaps can be identified and corrected with targeted QA.
Scale
Built once, applied across quote products
The same conversion-first framework and sequencing logic was applied beyond commercial insurance, including personal auto quote forms. The intent was to create repeatable patterns for reducing friction, improving clarity, and increasing downstream sales opportunities, while keeping the experience consistent across product lines.
Reusable conversion patterns
Step reduction, friction trimming, clarity-first content, and controlled qualification were designed as a repeatable system rather than a one-off page redesign.
Revenue language baked in
Every UX decision maps back to a business lever. Conversion rate, lead quality, call initiation, CAC reduction via SEO, and tighter alignment between digital estimates and final pricing outcomes.
Business impact & Validated outcomes
Funnel drop-off insight
The early funnel showed severe abandonment after the first step, which drove the Phase 1 strategy and test plan.
Old vs optimized flow
Step reduction and removal of early PII and qualification friction, designed to increase forward momentum and completions.
Quote integrity system
Session identity and code reveal designed to support traceability, call conversion, and estimate-to-final measurement.
Disclosure
This case study highlights decisions, not internal data.
For regulated environments and confidentiality, screenshots and metrics are selectively abstracted for public viewing. Full details, live walkthroughs, and deeper instrumentation discussion are available in interviews.