# The True Cost of Fake Signups: What We Found Analyzing 10,000 Accounts

> We analyzed 10,000 SaaS signups to measure the real impact of fake accounts. The results: 23% used disposable emails, they cost 4.7x more in infrastructure per dollar of revenue, and they destroyed conversion metrics.

**Author:** Matt King | **Published:** January 22, 2026 | **Category:** Email Security

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We analyzed 10,000 signups across multiple SaaS products to quantify the real impact of fake accounts. The headline numbers: **23% used [disposable emails](/disposable-emails)**, they consumed **4.7x more infrastructure per dollar of revenue** than real users, and they made conversion metrics unreliable for product and fundraising decisions.

Here's what we found.

## The Data

We retroactively validated the email addresses of 10,000 accounts created over a six-month period across three B2B SaaS products with free tiers. We cross-referenced email validation results with account activity, conversion data, and infrastructure usage.

### Signup Breakdown

| Category | Percentage | Count |
|----------|-----------|-------|
| Legitimate email | 73% | 7,300 |
| Disposable email | 23% | 2,300 |
| Invalid DNS | 4% | 400 |
| **Total fake/suspect** | **27%** | **2,700** |

Nearly three in ten signups were from addresses that either couldn't receive email or were designed to be thrown away.

### Conversion Impact

Of the 2,300 disposable email signups:

- **0 converted to paid** — Zero. Not one disposable email account ever upgraded.
- **94% never returned** after the first session.
- **6% used the product** for 2-5 sessions before disappearing — likely extracting value from the [free tier](/use-cases/free-tier-abuse-prevention).

This means the products' reported free-to-paid conversion rates were systematically understated. A product reporting a 3.2% conversion rate actually had a **4.4% real conversion rate** when fake accounts were excluded.

### Infrastructure Cost

Fake accounts consume real resources:

- **Database storage** — User records, associated data, session logs
- **Email sending** — Welcome sequences, onboarding drips (all bouncing)
- **Compute** — API calls, background jobs triggered by account creation
- **Support tooling** — CRM records, analytics events, segment membership

We estimated the infrastructure cost per account type:

| Account Type | Avg. Monthly Cost | Revenue Generated | Cost/Revenue Ratio |
|-------------|-------------------|-------------------|--------------------|
| Legitimate (free) | $0.42 | $0 (but 4.4% convert) | Pipeline investment |
| Legitimate (paid) | $1.85 | $47.00 | 0.04x |
| Disposable email | $0.38 | $0 (0% convert) | Infinite (pure waste) |

The $0.38 per fake account looks small, but multiplied by 2,300 accounts over six months, that's **$5,244 in pure waste** — for just three products.

### Email Deliverability Damage

The 2,700 fake/invalid accounts triggered thousands of bounced emails from onboarding sequences. This damaged sender reputation scores and caused deliverability issues that affected real customers:

- **Bounce rate increased** from 1.2% to 8.7%
- **Spam folder placement** rose by 15% for one product
- **Email provider warnings** triggered for all three products

The irony: fake signups don't just waste resources, they actively damage your ability to reach real customers.

## The Metric Distortion Problem

For startups raising funding or making product decisions, fake signups create dangerous distortions:

### What Investors See vs Reality

| Metric | Reported (With Fakes) | Actual (Fakes Removed) |
|--------|----------------------|------------------------|
| Monthly signups | 1,667 | 1,217 |
| Free-to-paid conversion | 3.2% | 4.4% |
| 30-day retention | 18% | 25% |
| Activation rate | 31% | 42% |

The "real" metrics tell a much healthier story. But you can't show investors real numbers if you don't know which accounts are fake.

### Product Decisions Based on Bad Data

Fake accounts distort every funnel metric. A/B tests, onboarding optimization, feature adoption analysis — all become unreliable when a quarter of your data comes from users who never intended to use the product.

One product in our analysis spent three months optimizing an onboarding flow to improve activation. They eventually realised that the low activation rate was mostly caused by disposable email signups who never opened the app a second time. The onboarding flow was fine — the users were fake.

## The Fix: What Happened After Adding Validation

All three products implemented email validation at signup using Fidro's API. The results after 90 days:

- **Fake signups dropped by 91%** (from 27% to 2.4%)
- **Reported conversion rates increased by 28%** (because the denominator got cleaner)
- **Email bounce rate dropped** from 8.7% to 1.1%
- **Infrastructure costs decreased** by approximately $1,800/month across the three products

The implementation took less than an hour per product. One API call at signup, blocking disposable emails and invalid DNS.

## How to Audit Your Own Signups

You can run this analysis on your own user base:

1. Export your user email list
2. Validate each email through [Fidro's API](/docs) or the [free email checker](/tools/email-checker)
3. Segment results: legitimate, disposable, invalid DNS
4. Cross-reference with conversion and activity data
5. Recalculate your metrics excluding fake accounts

The gap between your reported and real metrics might surprise you.

## Getting Started

[Fidro's free plan](/register) includes 200 validations per month — enough to validate your existing user base and test the integration. Add email validation at signup to stop the bleeding, then back-validate existing accounts to clean your metrics.

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## Frequently Asked Questions

### What percentage of SaaS signups are fake?

Based on our analysis of 10,000 accounts across multiple SaaS products, approximately 23% of signups used disposable email addresses. An additional 4% used emails with invalid DNS records. The combined fake/suspect signup rate was 27%.

### How much do fake signups cost a SaaS business?

Fake signups cost SaaS businesses in four ways: infrastructure costs (compute, storage, bandwidth consumed by non-paying users), skewed metrics (bad data leading to wrong product decisions), email deliverability damage (bounces from expired addresses), and support overhead (onboarding emails, ticket handling). For a mid-stage startup, this typically amounts to 15-25% of infrastructure budget being wasted on fake accounts.

### Do fake signups affect conversion rate calculations?

Yes, significantly. If 23% of your signups are fake and will never convert, your true free-to-paid conversion rate is approximately 30% higher than reported. For example, a reported 3% conversion rate is actually 3.9% when fake accounts are excluded. This matters for fundraising, growth planning, and product decisions.

### What is the fastest way to reduce fake signups?

Email validation at signup is the single highest-impact step. Blocking disposable emails and addresses with invalid DNS eliminates approximately 70% of fake signups with zero friction for legitimate users. It can be implemented in under an hour with an API like Fidro.