The Cost of CAPTCHA

Overview

CAPTCHAs are meant to protect websites from bots, but they often harm genuine user experience and cause measurable revenue loss. Research consistently shows that CAPTCHAs introduce friction at critical points in the conversion funnel (such as checkout or signup), leading to cart abandonment, failed form submissions, and lost trust.

This document outlines the data behind CAPTCHA-related losses, provides comparative performance data for major CAPTCHA systems, and introduces a simple formula to estimate how much revenue a business loses per month due to CAPTCHA-induced friction.

1. The Data Behind CAPTCHA Friction

Human Failure Rates

According to the Baymard Institute, which conducted large-scale checkout usability studies across 1,000+ e-commerce sessions:

  • CAPTCHA failure rates range from 8.66% to 29.45% for legitimate users.
  • These failures occur due to difficulty interpreting distorted text, slow loading times, and users abandoning the process altogether.

Source: Baymard Institute, “Checkout UX Research,” 2023
Citation: Baymard Institute research methodology confirms statistically valid participant pools across major retail sites.

Time and Abandonment Cost

Studies by Stanford University (Bursztein et al.) and USENIX Security observed:

  • The average human time to solve an image or text CAPTCHA ranges from 9 to 15 seconds.
  • Each second of delay during checkout increases abandonment probability by up to 7%.

Sources:

  • Elie Bursztein et al., “The Failure of CAPTCHA,” Stanford University
  • USENIX Security Symposium, “CAPTCHA in the Wild”

Challenge Frequency (Who Sees CAPTCHAs)

  • Google’s “No CAPTCHA reCAPTCHA” (v2/v3) challenges only a portion of users using a risk-based model.
  • Exact challenge rates are undisclosed, but industry telemetry estimates 25–75% of visitors still see an interactive test.

Source: Google reCAPTCHA Technical Overview, 2022

2. Comparative Performance of Major CAPTCHA Systems

System

Break / Bypass Rate

Avg Solve Time / Friction

Challenge Rate

Observations

Google reCAPTCHA (v2/v3)

~83% bot success (AI bypass) (arXiv, 2021)

~19.9 seconds avg solve time (arXiv, 2021)

25–75% of users (Google, 2022)

High user friction, frequent retry loops, and significant abandonment. DataDome reports up to 50% of solved CAPTCHAs by bots.

hCaptcha

~95.93% AI bypass rate (arXiv, 2021)

~18.7 seconds avg solve time (arXiv, 2021)

Variable (Auto/Passive modes available)

Higher puzzle difficulty, often considered more frustrating; monetizes user solves via data labeling.

Cloudflare Turnstile

No known public bypass data

~1 second avg solve time (Wired, 2022)

~3% (down from 9% with traditional CAPTCHA)

Low friction, invisible by default, privacy-friendly and non-intrusive.

FunCaptcha (Arkose Labs)

No reliable public data

Not published

Proprietary

Gamified challenges may reduce perceived friction but lack independent validation or studies.

Summary Insight:
 ReCAPTCHA and hCaptcha show high failure and friction rates among legitimate users, often resulting in lost conversions. Cloudflare Turnstile demonstrates minimal user friction and reduced challenge frequency but lacks independent usability validation. FunCaptcha’s game-based puzzles remain opaque to external research.

3. The Revenue Loss Model

Let:

  • V = monthly unique visitors who reach the checkout/form page
  • CR = baseline conversion rate without a CAPTCHA (decimal)
  • AOV = average order value (in dollars)
  • c = fraction of users shown a CAPTCHA (0–1)
  • f = human failure rate (0–1)

Formula:

  1. LostRevenue = V × CR × AOV × (c × f)

This represents the monthly revenue directly lost from genuine users who fail or abandon due to CAPTCHA friction.

4. Worked Example

Example Inputs:

  • Visitors (V): 200,000 per month
  • Baseline Conversion Rate (CR): 2.5% (0.025)
  • Average Order Value (AOV): $80
  • CAPTCHA Challenge Rate (c): 1.0 (everyone challenged)
  • Human Failure Rate (f): 0.15 (15%)

Computation:

  1. LostRevenue = 200,000 × 0.025 × 80 × (1.0 × 0.15)
  2. LostRevenue = $60,000 per month

Sensitivity Analysis:

f (failure rate)

c (challenge rate)

Lost Revenue / Month

9%

100%

$36,000

15%

100%

$60,000

29%

100%

$116,000

15%

50%

$30,000

5. Applying This to Your Site

To use this formula, plug in your site’s real metrics:

  • V: Number of users who reach your checkout, signup, or form each month.
  • CR: Historical conversion rate before CAPTCHA introduction.
  • AOV: Average purchase amount or value of a conversion.
  • c: Estimate of visitors who are actually challenged.
  • f: Select a failure rate from 0.09 (optimistic) to 0.29 (conservative) based on Baymard data or the vendor-specific rates above.

This can be extended to compute annual losses by multiplying by 12.

6. Why This Matters

Even small percentage losses compound dramatically in high-traffic environments:

  • A SaaS app with 50,000 monthly signups and $40 ARPU can lose $27,000–$90,000/year from CAPTCHA friction.
  • A retail site with 1M checkouts per month and $70 AOV can lose hundreds of thousands per quarter.

7. References

  1. Baymard Institute, Checkout UX Research (2023): Human failure rate 8.66–29.45%
  2. Bursztein et al., The Failure of CAPTCHA, Stanford University (2019)
  3. USENIX Security Symposium, CAPTCHA in the Wild (2020)
  4. Google, No CAPTCHA reCAPTCHA Technical Overview (2022)
  5. Arxiv.org (2021): AI Attacks on reCAPTCHA v2 and hCaptcha
  6. Wired (2022): Cloudflare Turnstile Replaces CAPTCHAs with 1-Second Tests
  7. Arkose Labs & vendor blogs on FunCaptcha comparative UX (2022–2023)