What is page speed worth for insurance quote flow? A worked example.
An insurance quote is a long, branching form. Conversion is the product of surviving every step, so a slow screen anywhere in the flow drags the whole completion rate down. On this profile, going from 4s to 2.1s models about $68.4K a month in extra revenue.
assumptions
A planning profile for this kind of site. Every figure is yours to change in the calculator.
- Current load time (p75): 4s
- Target load time: 2.1s
- Monthly visits in scope: 80K
- Current conversion rate: 6%
- Value of a completed quote: $75.00
- Conversion lift per 100ms faster: 1%
revenue_uplift
+$68.4K/ month
$820.8K / year · +912 completed quotes / month
- Time shaved off
- 1.9s
- Relative conversion lift
- +19%
- Conversion rate
- 6% → 7.14%
- Each 100ms is worth
- $3,600/mo
- Revenue now → at target
- $360.0K → $428.4K
Computed by the Page Speed → Revenue model · planning estimate, not a guarantee
why_speed_pays_here
Why speed maps to money for insurance quote flow
Because completion depends on clearing several steps, small per-step delays multiply into a large total drop-off. Shaving the load time of each screen lifts the completion rate of the whole funnel, which is why quote flows reward speed work out of proportion to any single page.
Where the load time goes. Each step often posts back to a rating engine and waits for the response before rendering the next screen. Pre-render the next step's shell, call the rating engine in the background, and only block on it where the number genuinely changes what the user sees.
faq
Questions & answers
- How much revenue can faster page speed add for insurance quote flow?
- On this profile (4s to 2.1s at 80K visits a month), the model puts the gain at about $68.4K a month, or $820.8K a year, from a roughly 19% relative lift in conversion. Your real numbers will differ; tune them in the calculator.
- Is the 19% conversion lift realistic?
- It comes from one assumption you can change: a 1% relative conversion change per 100ms faster, applied to the 1.9s this profile shaves off. That sensitivity is in the range of widely cited retail studies; for lower-intent traffic use a smaller figure, for high-intent checkout flows a larger one. The model also caps the modeled lift so an extreme speedup can't imply a fantasy multiplier.
- What's the fastest way to speed up insurance quote flow?
- Each step often posts back to a rating engine and waits for the response before rendering the next screen. Pre-render the next step's shell, call the rating engine in the background, and only block on it where the number genuinely changes what the user sees.
That uplift is the business case. Hitting the target is the work.
I'll find where your real load time goes and what it takes to actually reach the target. Book a call, or leave your email and I'll reach out.
Prefer proof first? See how this plays out in real case studies →