Abstract

This paper examines the relationship between web performance — specifically Core Web Vitals — and commercial outcomes. We synthesise public industry data, our own client benchmarks, and a calculation framework that lets readers estimate the revenue impact of performance improvements on their own properties.

Why this paper exists

Performance work is undersold inside most organisations. Engineering teams know that a faster site converts better, but they struggle to justify the investment to leadership in terms of expected return. This paper provides the framing to have that conversation in dollars instead of seconds.

The headline numbers

Aggregated across industry studies and our own client data, the rough rules of thumb are:

  • A 100ms improvement in LCP correlates with a 0.5-2% conversion uplift in commerce contexts.
  • Sites that move from "poor" to "good" on all three Core Web Vitals see 5-20% conversion improvements.
  • Mobile traffic is more sensitive to performance than desktop. The same improvement is worth roughly 1.5x more on mobile.
  • SEO impact is real but smaller than commonly claimed — typically a single-digit percentage of organic traffic at most.

The mechanism

Performance affects revenue through three channels:

  1. Direct conversion lift — slow pages have higher abandon rates at every step of the funnel.
  2. Search rankings — Google uses Core Web Vitals as a ranking signal.
  3. Brand perception — fast sites feel high-quality; slow ones don't. Hard to measure, real impact.

The calculation framework

For any site, the expected annual revenue impact of a performance improvement is approximately:

(Annual revenue) × (Conversion sensitivity) × (Performance delta) × (Confidence factor)

Where:

  • Conversion sensitivity: 0.5-2% per 100ms LCP improvement, depending on industry.
  • Performance delta: the actual improvement in seconds.
  • Confidence factor: 0.5-0.8, to account for the noisy nature of this measurement.

Case studies

Three anonymised client studies that illustrate the framework:

D2C commerce client: Moved from 4.2s LCP to 1.8s LCP. Measured conversion uplift: 14% over the 90-day window post-launch, statistically significant.

B2B SaaS marketing site: Moved from 3.1s LCP to 1.4s LCP. Form submission rate improved 9%. Inbound lead volume up 22% over 6 months (partly compounding from search ranking improvements).

Content publisher: Moved from 2.8s LCP to 1.6s LCP. Page-views-per-session up 11%. Ad revenue up an estimated 8%.

The investment side

Performance work at a typical agency or in-house team costs $30-150K depending on the scope of changes. For sites with annual revenue above $5M, even modest performance improvements typically pay back the investment within 6-12 months.

Below that revenue threshold, the calculation is less clear. Performance still matters — but the investment should be sized accordingly.

Where the cost-benefit changes

Three factors shift the equation:

  • Industry. Travel, media, and commerce are most performance-sensitive. B2B SaaS is less so.
  • Geography. Users on slower networks (developing markets, rural areas) benefit more from improvements.
  • Device mix. Mobile-heavy traffic amplifies returns; desktop-heavy traffic dampens them.

Recommendations

For any site with material revenue:

  1. Measure your current Core Web Vitals using Chrome User Experience Report data, not lab data.
  2. Identify the largest single contributor to bad LCP (almost always images).
  3. Run a targeted improvement focused on that single contributor.
  4. Measure conversion before and after with a statistically valid window.
  5. Use the result to justify further investment.

Conclusion

Web performance is an underinvested-in lever for most organisations. The investment-to-return ratio is favourable, the measurement is straightforward, and the work is bounded. Engineering teams should be able to make this case to leadership in business terms — and leadership should be open to hearing it.