Do your users detect bugs before you? The problem of not having QA in production

There is a question that few teams ask explicitly, but that directly defines the quality of their user experience: who discovers bugs first?

If the answer is “the customer”, the computer has a detection problem. And it's not technical: it's a problem of designing the QA process.

56% of organizations consider their QA process to be underautomated. [1]

In practice, this means that more than half of companies discover bugs through the most expensive way: user reporting. Not because they don't care about quality, but because they don't have an early detection system that allows them to anticipate.

Why late detection multiplies the cost of the bug

The cost of correcting a bug detected in production is between 10 and 100 times greater than that of detecting it during the development or testing phases. [2]

That difference isn't just explained by the engineering time needed to fix it. When a bug reaches production, it carries with it a chain of additional costs: the support ticket it generates, the diagnostic time in a real environment, the communication with the affected customer, possible compensations if there was a committed SLA and, above all, the impact on the perception of the product.

That impact is difficult to measure, but very real. In B2B SaaS applications, a fault detected by the customer has a 3 to 5 times more negative effect on the NPS than the same fault detected and proactively reported by the team. [3]

It's important to understand that we're not talking about the existence of the bug — bugs are inevitable — but about the moment and the actor who discovers it. A bug that is identified internally and resolved before the customer experiences it barely leaves a trace. The same bug, exposed to the user, does.

The difference, therefore, is not in the absolute quality of the software, but in the system's ability to detect errors before they escape.

Three signs that your users are spotting bugs before you

1. Support tickets that describe production problems
When the support team receives incidents that reproduce critical behaviors that should have been detected before deployment, there is a clear gap in QA coverage. Not all bugs are predictable — there will always be borderline cases — but essential flows (login, checkout, form submission, key integrations) should be systematically covered.

2. Reviews or public mentions that indicate technical flaws
Around 77% of consumers leave an online retailer after finding errors. [4] Not everyone goes silent: some people report it publicly. If the first signs of a bug appear on Twitter, in a G2 review, or on any external channel, the internal detection system is arriving hours or even days late.

3. Churn in renovations associated with technical instability
In SaaS, many technical problems don't generate immediate churn, but rather accumulated wear and tear. For months, small frictions erode customer trust until, at the time of renewal, they emerge as the main reason for cancellation. It's one of the most invisible types of churn, precisely because it doesn't manifest itself in real time.

The key metric: How long it takes you to find out

One of the most critical variables is not how many bugs you have, but how much time elapses between them appearing and the team becoming aware of them.

In SaaS companies with between 50 and 200 employees without automated QA, critical bugs in production are detected, on average, between 4 and 8 hours after their appearance. [6] With continuous automated monitoring, that time is reduced to minutes.

That difference—of several hours versus a few minutes—is not trivial. It can be the distance between an incident affecting 10 users and another that impacts 1,000.

A user who reports a bug is not necessarily a particularly attentive user.

It is, almost always, an indicator that the internal detection system has arrived late.

The key question isn't whether you have bugs — all systems have them — but who finds them first.

References

1. Capgemini/ OpenText. (2024). World Quality Report 2024-25. https://www.capgemini.com/insights/research-library/world-quality-report-2024-25/ — 56% of organizations consider their QE process to be underautomated. Many bugs make it to production and the poor user experience continues to cause abandonment. This shows a gap between the effort invested in QA and the results obtained.

2. Tricentis. (2024). Fail Watch 2024 software. Tricentis Research. https://www.tricentis.com/resources/software-fail-watch-annual-report — Tricentis documents that the cost of correcting a bug detected in production is between 10 and 100 times greater than the cost of detecting it in the development or testing phases. In B2B SaaS, bugs detected by customers generate support tickets, compensation and, in the most serious cases, customer loss.

3. Gartner. (2024). Market Guide for AI-Augmented Software Testing Tools. Gartner Research. — In B2B SaaS applications where uptime is part of the SLA, a critical bug detected by a customer can result in contractual penalties in addition to the cost of support. The impact on NPS of a fault detected by the user is 3 to 5 times more negative than the same fault detected and proactively reported by the team.

4. SiteQuality. (2025). The True Cost of Website Downtime in 2025. https://siteqwality.com/blog/true-cost-website-downtime-2025/ — Around 77% of consumers leave an online retailer after finding errors. In SaaS, abandonment after poor technical experience is a major factor in churn in annual renewal contracts.

5. AtestLab. (2024). AI In Test Automation: From Costs To Benefits. https://blog.qatestlab.com/2024/11/27/test-automation-and-ai-benefits/ — Organizations that don't have automated QA have a late alarm signal: the user detects the fault before the team. That signal comes through support tickets, negative reviews, or churn. The time between when the bug exists and when it is detected externally depends directly on internal monitoring coverage.

6. Evil. (2026). The State of Quality Assurance in SaaS Companies. MABL Research. — In SaaS companies with between 50 and 200 employees without a dedicated QAEngineer, critical bugs in production are detected on average 4 to 8 hours after their appearance. With continuous automated monitoring, that time is reduced to minutes. The difference between 4 hours and 5 minutes of detection can be the difference between a minor incident and an interruption visible to all customers.