You run tests when you need them. No overhead, no bureaucracy, just nimble testing on demand. Major release? Run a beta. Big feature? Quick usability test. New market? Recruit some users.
But here's what typically happens. Tests often start with minimal reusable infrastructure. You rebuild workflows, re-recruit users, re-negotiate with stakeholders, reconfigure tools. Insights often don't carry forward. By the time you're done, the "lightweight" test consumed more time and coordination than teams realize.
Teams running ad-hoc tests think they're being scrappy. They're often accumulating waste. The alternative (a programmatic approach) has upfront costs everyone can see, but it eliminates hidden expenses that compound over time.
This post explains the six most common hidden costs of ad-hoc testing (whether you're running betas, usability studies, or other validation tests), shows when programs pay off, and offers a simple model for calculating your break-even point.
The Hidden Costs Of Ad-Hoc User Testing
Ad-hoc testing feels cheap because teams don't track what it actually costs to rebuild infrastructure for each test.
When teams calculate testing costs, they count tester incentives, tool subscriptions, and maybe a PM's time. They miss invisible costs: recreated processes, repeated stakeholder negotiations, cold recruitment, lost institutional knowledge, and quality inconsistency.
These costs don't appear on budget spreadsheets, but they compound with every test.
Hidden Cost #1: Process Recreation
Many teams run tests without documented workflows. You figure out the timeline, create recruitment criteria, draft screeners, build feedback mechanisms, coordinate stakeholders, plan analysis. Even if you ran tests before, you're rebuilding from memory.
The real cost:
- Commonly 10-20 hours recreating workflows per test (in our experience working with PM teams)
- Decision fatigue on questions you've already answered
- Inconsistent quality because process isn't refined
- Higher error rates from forgotten steps
Example - TechFlow SaaS: TechFlow is a composite example based on common patterns we see across PM teams. TechFlow (a B2B project management platform) ran four betas in 2025. Each time, their PM spent 1-2 weeks figuring out logistics. That's 6-8 weeks of PM time annually just recreating process. With documented workflows, they could spend that time on analysis instead.
Hidden Cost #2: Stakeholder Buy-In Battles
Tests often require re-selling stakeholders. You justify the timeline, defend the budget, explain why testing matters this time, negotiate scope. Even if leadership approved testing last quarter, you might be starting the conversation fresh.
The real cost:
- Stakeholder meeting time (your team plus executives, multiplied across tests)
- Delayed launches while waiting for approvals
- Reduced scope from incomplete justification
- Political capital spent repeatedly instead of securing stakeholder buy-in
Example - TechFlow SaaS: Engineering leadership approved TechFlow's Q2 beta but questioned the six-week timeline. The PM couldn't point to historical data showing optimal test length. They compromised on four weeks and missed late-emerging integration issues that surfaced post-launch, requiring a patch release two weeks later.
Hidden Cost #3: Recruitment Delays
Without a community or pipeline, recruitment starts cold. You source, screen, and onboard brand-new participants for each test without proven recruiting strategies.
The real cost:
- Often 2-4 weeks to recruit (vs. days with a pipeline)
- Higher recruitment costs (agency fees can reach five figures)
- Lower quality match when rushing to hit timelines
- Higher drop-off rates without established relationships
Example - TechFlow SaaS: TechFlow paid a recruitment agency about $11,000 to find 50 beta testers for their Q3 test. A competitor with an established community recruited from existing users for $0 direct cost (standard incentive budget only). The competitor filled slots in one week vs. TechFlow's three weeks.
Hidden Cost #4: Tools and Infrastructure Setup
Teams sometimes re-evaluate tools, set up new feedback mechanisms, configure integrations, and train stakeholders for each test.
The real cost:
- Tool evaluation time (comparing options repeatedly)
- Setup and configuration
- Training stakeholders on test-specific tools
- Lost efficiency from not mastering any single toolset
Example - TechFlow SaaS: TechFlow used Google Forms for their Q1 beta, Typeform for Q2, and Google Forms again for Q3. Each switch required setup time and prevented cross-test comparison because data formats differed. They never mastered any tool.
Hidden Cost #5: Lost Institutional Knowledge
Without documentation, insights from one test don't inform the next. You can't track trends or compare results.
The real cost:
- Repeated mistakes
- Can't answer "how does this compare to last time?"
- No baseline or benchmarks
- New team members have no reference point
Example - TechFlow SaaS: In Q1, TechFlow discovered users struggled with technical jargon in onboarding. In Q3, a different PM tested a new feature and hit the same issue. No documentation existed, so they rediscovered the problem instead of designing around it.
Hidden Cost #6: Inconsistent Quality and Rigor
Without refined processes, quality varies test to test. Some tests are thorough, others cut corners.
The real cost:
- False confidence from poorly executed tests
- Missed issues that emerge post-launch
- Inconsistent data quality prevents comparisons
- Support costs from issues you should have caught
Example - TechFlow SaaS: TechFlow ran their Q4 beta with only 18 testers from existing customers. They found few issues, shipped with confidence, then discovered critical usability problems when new trial users struggled. Support tickets spiked roughly 40% in the first two weeks post-launch.
When Ad-Hoc Testing Makes Sense
Not every situation warrants a program. Some scenarios favor lightweight, one-off testing.
Ad-hoc testing works well for:
- One-off exploratory concept tests early in discovery
- Early-stage startups validating problem-market fit before establishing patterns
- Emergency regression checks before a hotfix (speed over process)
- Rare product categories where you test less than 2-3 times per year
The inflection point: If you run even a few tests per quarter, most teams hit break-even within the first 1 to 2 quarters.
Why Programs Win: The Economics
Programs have higher initial setup costs but dramatically lower per-test costs. The economics flip quickly.
Understanding the ROI of testing programs requires looking at total cost of ownership, not just upfront costs. While ad-hoc testing has low initial costs, it has high per-test costs that compound over time. Programs reverse this: higher setup investment, but substantially lower costs for each subsequent test.
The key insight: programs typically break even after 2-3 tests. Beyond that, efficiency gains compound.
Simple break-even calculation:
Ad-hoc cost per test = (planning hours + recruiting hours + alignment hours + setup hours + analysis hours) x fully loaded hourly cost
Program cost year one = setup hours x fully loaded hourly cost + (program per-test hours x test count x fully loaded hourly cost)
Break-even test count = setup hours / (ad-hoc per-test hours minus program per-test hours)
Example: if setup is 90 hours, ad-hoc is 60 hours/test, and program is 17 hours/test, break-even is about 2 tests.
For teams that want to quantify this precisely, Centercode's Beta Testing ROI Kit provides calculators for individual tests and annual programs. The framework uses Cost of Quality principles: appraisal costs, internal failure costs, and external failure costs avoided.
Efficiency Gain #1: Process Refinement
After 3-4 tests, you know how long recruitment takes for your user base, which feedback formats yield insights, which stakeholders need what updates. Each subsequent test runs smoother.
The gain: Planning drops from days to hours, fewer surprises during execution, faster analysis with established templates.
Efficiency Gain #2: Recruitment Pipeline
With ongoing community building or recruitment partnerships, you have warm prospects ready to test.
The gain: Recruitment measured in days instead of weeks, lower direct costs, higher quality participants, better retention.
Efficiency Gain #3: Stakeholder Trust
After delivering reliable insights consistently, stakeholders trust your process. Trust replaces skepticism.
The gain: Faster approvals, broader scope permission, more influence.
Efficiency Gain #4: Institutional Knowledge
You track what works, document patterns, avoid repeating mistakes.
The gain: Compounding learning, better decision-making from historical data, faster team onboarding.
Efficiency Gain #5: Tool Mastery
Consistent tooling means you get better at using it and extract more value.
The gain: Faster setup, richer insights from advanced features, better data consistency for trend tracking.
What A "Program" Actually Means
A user testing program is just building processes, infrastructure, and relationships you reuse.
When teams hear "user testing program," they picture dedicated headcount and enterprise overhead. That's not required.
A program just means:
✓ Repeatable processes you refine over time
✓ Established infrastructure (tools, templates, workflows that persist)
✓ Ongoing relationships with users or recruitment channels
✓ Institutional knowledge that compounds
✓ Stakeholder alignment on why/when/how you test
It doesn't require:
✗ Full-time dedicated team (though mature programs often grow into one)
✗ Testing constantly (you still test when needed)
✗ Rigid processes (good programs evolve)
✗ Enterprise platforms costing six figures
A program can be lightweight. You build once, refine continuously, and eliminate waste.
How To Scope Your Program Requirements
Most teams get stuck on: "What does our program actually need?"
The answer depends on your situation. How often do you test? What types of products? Who needs involvement? What tools make sense? What does rollout look like?
A company shipping consumer hardware quarterly needs different infrastructure than a SaaS team testing continuously. A startup with two PMs has different constraints than an enterprise with dedicated research teams.
Most teams either over-build (copying Google's approach), under-build (minimal infrastructure that doesn't solve the problems), or never start (paralyzed by questions).
Questions to answer before scoping:
- What testing goals are we achieving repeatedly?
- Which audiences do we need ongoing access to?
- What project types will we test?
- Who across the organization needs involvement?
- What tools would we use multiple times?
- What integrations with existing systems matter?
- What's a realistic rollout timeline?
- How do we think about ROI for our business?
You can scope this work several ways:
- Run a scoping workshop with stakeholders to align on requirements and constraints
- Create a lightweight spreadsheet tracking your current testing costs and projected program costs
- Use a structured tool like Program Planner that walks through these questions systematically and generates a program overview
The key is doing the scoping work before you build. Understanding actual requirements prevents both over-engineering and under-delivery.
Making The Shift From Ad-Hoc To Program
You don't have to build everything at once. Start small and expand.
You don't need a perfect program on day one. Start solving the biggest problems, then build from there. Most successful teams follow proven program strategies but adapt them to their specific constraints.
First: Document your current testing process. Write down what you do: recruitment steps, stakeholder touchpoints, feedback collection, analysis methods.
Then: Identify the 2-3 biggest inefficiencies. Recruitment taking weeks? Stakeholder negotiations delaying launches? Lost insights between tests? Pick the pain points costing the most.
Next: Build infrastructure to solve those specific problems first. If recruitment hurts most, invest in a community. If stakeholder battles slow you down, document your approach and get one-time buy-in. If lost knowledge costs you, create simple documentation.
Finally: Refine and expand based on what works. After solving your top problems, address the next biggest inefficiencies.
Common first steps:
- Create recruitment pipeline or community
- Standardize tooling
- Build stakeholder templates and reporting
- Document process and learnings
Pick the one or two that hurt most and build infrastructure to eliminate those costs first.
Wrapping Up
Ad-hoc user testing feels flexible, but hidden costs compound: process recreation, stakeholder battles, recruitment delays, tool switching, lost knowledge, and inconsistent quality.
Programs have visible setup costs but eliminate hidden expenses. Based on typical time investments, programs often break even by the second or third test and deliver increasing ROI thereafter.
The challenge is figuring out what your program needs. Not a generic best practice, but infrastructure that solves your team's inefficiencies given your testing frequency, product types, and constraints.
Start by scoping your requirements. Think through your goals, organizational needs, and constraints. Then build infrastructure that solves your specific inefficiencies. With clear requirements, you can build a program that eliminates waste.
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If you’re starting to think about a program approach and want to see how it compares to what you’re doing today, a Centercode demo can help make it concrete. Use the button below to get started.
