Our first beta program was a disaster disguised as a success.
We recruited 50 customers, gave them early access to our new analytics product, and asked for feedback. Forty-three of them said they loved it. Seven didn't respond. Product declared victory and we shipped to general availability.
Three months later, usage data revealed the truth: only 9 of those 50 beta users were actively using the product. The rest had tried it once, said nice things to be polite, and never came back.
We'd shipped a product that beta users praised but didn't actually want to use. Our beta program had given us false confidence instead of real insight.
I ran seven more beta programs over the next two years. They all failed in different ways. Some recruited the wrong users. Some asked the wrong questions. Some created feedback loops that product ignored. Some generated so much conflicting feedback that we couldn't separate signal from noise.
After eight failures, I finally figured out what makes beta programs work. It's not about recruiting enthusiastic users and collecting their opinions. It's about creating conditions where users have to rely on your product for real work, then watching what breaks.
Most beta programs fail because they're designed to make product feel validated, not to discover what's actually broken.
Why Most Beta Programs Are Theater
The typical beta program looks like this:
Recruit users who are excited about the product. Give them access. Ask them to "try it out and share feedback." Send a survey after two weeks. Compile the results. Present to product.
This process generates data, but not useful data.
Problem 1: You recruit fans, not representative users
You advertise the beta to your friendliest customers. The ones who love your company and want to help. The ones who are excited about every new feature.
These users will say positive things because they like you, not because the product is good. They'll overlook problems because they want you to succeed.
On one beta, 85% of participants said the product was "ready to launch." Post-launch, 60% of general availability users couldn't figure out how to complete the core workflow. Our beta users had been too polite to tell us it was confusing.
Problem 2: You ask for opinions, not behavior
Typical beta survey questions:
- "Do you like this feature?"
- "Would you use this in your daily workflow?"
- "What would make this better?"
These questions generate opinions about hypothetical future behavior. People are terrible at predicting what they'll actually do.
Users will say "I'd definitely use this" because it sounds like a useful feature. Then they won't use it because it doesn't fit their actual workflow.
Problem 3: Product uses beta to validate, not to discover
Product has already decided what to build. The beta exists to confirm that decision, not to challenge it.
When beta users surface problems, product explains why those problems aren't really problems. "They're using it wrong." "They don't understand the value prop." "They're not the target user."
The beta becomes a rubber stamp instead of a reality check.
I've watched product teams dismiss critical beta feedback because it contradicted what they wanted to hear, then scramble to fix those exact issues post-launch when real users complained.
What Changed When I Fixed My Beta Program
After my eighth failed beta, I completely rebuilt the program with different goals.
Instead of recruiting fans to validate the product, I recruited skeptics to break it.
Instead of asking for opinions, I gave users real work to do and watched where they got stuck.
Instead of treating beta as a validation phase, I treated it as a discovery phase—the last chance to find critical problems before launch.
The results were uncomfortable but valuable.
Beta users complained that the onboarding flow was confusing. Product initially pushed back, but we watched session recordings and saw users struggle at the exact same step. We redesigned it. Post-launch onboarding completion went from 42% (internal testing) to 73% (after beta fixes).
Beta users said the product was "too technical" for their team. Product wanted to dismiss this as "wrong audience," but we dug deeper and discovered our documentation assumed knowledge that most users didn't have. We rewrote it. Support tickets dropped 40% post-launch.
Beta users couldn't figure out how to integrate with their existing tools. Product said "that's advanced use cases," but we found that 70% of users needed integration to get value. We prioritized building integration guides. Activation rate improved from 31% to 58%.
The beta program stopped being a celebration of how great our product was and started being an honest assessment of what would break when real users tried to use it.
That shift—from validation to discovery—is what makes beta programs valuable.
The Beta Program That Actually Works
After running fifteen betas using this approach, I've settled on a structure that consistently surfaces problems product can fix before launch.
Week 1: Recruit Users Who Will Tell You the Truth
Most betas recruit enthusiastic early adopters. I now recruit three types of users:
Type 1: Skeptical power users (40% of beta cohort)
These are sophisticated users who have high expectations and won't hesitate to complain.
They're using competitive products. They have strong opinions about what good looks like. They're not afraid to say your product is confusing or incomplete.
These users will surface problems that fans overlook. They're annoying to work with, but they'll tell you the truth.
Type 2: Novice users who match your target persona (40% of beta cohort)
Not your friendliest customers—users who match your ICP but have never talked to your company before.
They don't know your product's quirks. They haven't been trained on your mental models. They'll try to use your product the way they naturally think it should work, and they'll get stuck when it doesn't match their expectations.
These users reveal onboarding problems, documentation gaps, and UX confusion that your internal team is too close to see.
Type 3: Extreme use cases (20% of beta cohort)
Users who will push your product to its limits.
They have 10x more data than typical users. They have complex workflows. They need to integrate with unusual tools.
These users will break your product in ways you didn't anticipate. Better to discover those edge cases in beta than in production.
I used to recruit only Type 1 users—fans and early adopters. They gave glowing feedback and the product still failed at launch.
Now I deliberately recruit users who will be critical. The feedback is harder to hear, but it's infinitely more useful.
Week 2: Give Users Real Work, Not Test Scenarios
Most betas say "Please try the product and let us know what you think."
That's too vague. Users will click around, say "looks good," and never actually stress-test anything.
I now give beta users specific jobs to complete using the product:
For an analytics beta: "Create a dashboard tracking your team's three most important KPIs. Use this dashboard in your weekly team meeting. Report back on what worked and what was frustrating."
For a collaboration tool beta: "Manage your next project entirely in this tool—no Slack, no email, no spreadsheets. Use it for 10 business days. Document every time you had to work around a limitation."
For an integration beta: "Connect this to your existing CRM and automate your current manual workflow. Try to eliminate at least 2 hours of manual work per week."
These aren't test scenarios—they're real jobs that users need to accomplish. If they can't do it with your product, you've learned something valuable.
Most beta users will resist this level of commitment. That's fine. I'd rather have 10 users doing real work than 50 users casually clicking around.
The 10 who commit will surface real problems. The 50 who browse will tell you everything looks great.
Weeks 3-4: Watch What They Do, Not What They Say
I used to collect beta feedback through surveys and interviews. Users would tell me what they thought, and I'd compile their opinions into a report.
This approach missed the most important data: actual behavior.
Now I instrument everything and watch what beta users actually do:
Activation tracking: How many users complete core workflows? Where do they drop off?
Session recordings: Where do users hesitate? What do they try to click that doesn't work? What workflows do they attempt that we didn't anticipate?
Support tickets: What are users getting stuck on? What questions do they ask repeatedly?
Time-to-value metrics: How long does it take users to achieve their first success? Is it faster or slower than our target?
Feature usage: Which features do users actually use? Which ones do they ignore?
This behavioral data is far more valuable than survey responses.
On one beta, 80% of survey respondents said the product was "easy to use." But session recordings showed that 65% of users failed to complete the primary workflow on their first attempt.
We fixed the UX based on behavior, not opinions. Post-launch, first-time success rate improved to 78%.
Week 5: Synthesize Patterns, Not Individual Complaints
Beta programs generate overwhelming amounts of feedback. Every user has opinions. Every user wants different features.
Most PMMs try to address every piece of feedback. This is impossible and leads to scope creep.
I now spend week 5 looking for patterns, not responding to individual requests.
I ask:
- What problems did 30%+ of beta users experience?
- What workflows broke for multiple user types?
- What assumptions did we make that proved wrong?
- What features did nobody use, despite us thinking they were critical?
Patterns indicate systemic problems. Individual complaints might just be edge cases.
On one beta:
- 38% of users couldn't figure out how to invite team members (pattern → fix before launch)
- 41% of users wanted dark mode (pattern → add to roadmap but don't block launch)
- 8% of users wanted Salesforce integration (edge case → defer to post-launch)
- 2 users wanted a feature we'd never heard of (edge case → ignore)
We fixed the invitation flow. We acknowledged but didn't prioritize dark mode. We ignored requests from fewer than 10% of users.
This ruthless prioritization is what separates useful betas from ones that generate noise.
Week 6: Fix What Matters, Ship What Works
The final week is about decision-making: which feedback requires fixes before launch, and which feedback can wait?
I categorize all beta feedback into four buckets:
Blockers (must fix before launch):
- Broken core workflows that 30%+ of users encountered
- Data loss or security issues
- Onboarding flows that most users can't complete
Important but not blocking (fix in first 30 days post-launch):
- Feature requests from 20-30% of users
- UX friction that slows users down but doesn't stop them
- Documentation gaps
Nice-to-have (add to backlog):
- Feature requests from 10-20% of users
- Polish improvements
- Edge case handling
Ignore:
- Requests from fewer than 10% of users
- Requests that contradict product strategy
- Scope creep disguised as feedback
Most beta programs try to fix everything. This delays launch and creates a worse product by adding features nobody needs.
I fix only the blockers, acknowledge the important issues, and ship.
Post-launch data always proves this is the right call. The blockers would have caused real problems. The nice-to-haves almost never get requested by GA users.
The Uncomfortable Truths About Beta Programs
After running dozens of betas, a few uncomfortable truths have become clear:
Truth 1: Most beta feedback is noise
80% of feedback is either wrong, contradictory, or based on misunderstanding the product.
Your job isn't to address all feedback. It's to separate signal from noise and act only on patterns.
Truth 2: Beta users are not representative of your broader market
Even if you recruit carefully, beta users are self-selected enthusiasts. They're more forgiving, more technical, and more patient than general availability users.
If beta users struggle, GA users will struggle more. If beta users say "this is confusing," GA users will be completely lost.
Beta feedback is a floor, not a ceiling. If betas surface problems, assume they're worse than they appear.
Truth 3: Product will resist negative beta feedback
Product has spent months building this. They believe in the vision. They don't want to hear that core workflows are broken or that users don't understand the value prop.
Your job is to present behavioral data that product can't dismiss. Not "users said it's confusing" but "67% of users failed to complete the core workflow on their first attempt."
Show the session recordings. Show the time-to-value data. Show the support ticket volume.
Make the problems impossible to ignore.
Truth 4: Most beta problems don't get fixed
Even when betas surface critical issues, many don't get fixed before launch.
Product runs out of time. Engineering says it's too risky to change. Leadership decides to ship anyway and "iterate post-launch."
Your job is to document what you learned and make sure product makes informed trade-offs, not blind ones.
Sometimes shipping with known problems is the right call. But product should make that decision explicitly, not accidentally.
What Good Beta Programs Accomplish
A well-run beta program doesn't guarantee a successful launch. But it dramatically increases the odds.
Good betas accomplish four things:
1. Surface usability problems before launch
Issues that would generate support tickets, negative reviews, and churn.
On one beta, we discovered that 40% of users couldn't figure out how to export data—a core workflow. We fixed it. Post-launch support tickets for exports: nearly zero.
2. Validate (or invalidate) your activation hypothesis
You think users will get value from X workflow in Y time. Beta tells you if that's true.
On one beta, we thought users would get value from building their first dashboard in 10 minutes. Reality: it took 35 minutes, and most users gave up.
We redesigned onboarding to get users to value in 8 minutes. Activation rate tripled.
3. Create social proof for launch
Beta users who successfully got value become your launch testimonials, case studies, and references.
On one beta, three users built workflows that saved them 5+ hours per week. Those stories became the core of our launch campaign.
4. Build a cohort of engaged early adopters
Users who stuck with beta through problems are invested in your product's success. They become champions, not just customers.
These users evangelize your product, provide ongoing feedback, and help other users get started.
What I'd Tell a PMM Running Their First Beta
If you're planning your first beta program, here's what I wish someone had told me:
Recruit skeptics, not fans. You need users who will tell you what's broken, not users who will tell you it's great.
Give users real work to do. Casual testing generates casual feedback. Users who rely on your product for real work will surface real problems.
Watch behavior, not opinions. Session recordings and usage data beat survey responses every time.
Focus on patterns, not individual requests. Fix problems that 30%+ of users experience. Ignore feedback from fewer than 10%.
Product will resist negative feedback. Bring data they can't dismiss. Show them the session recordings.
You can't fix everything before launch. Prioritize blockers. Ship. Iterate.
Most importantly: the goal of beta is discovery, not validation.
If your beta generates only positive feedback, you recruited the wrong users or asked the wrong questions.
Good betas are uncomfortable. They surface problems you didn't know existed. They challenge assumptions product believed were true. They delay launch while you fix critical issues.
But they prevent disasters. And in product marketing, preventing one disaster is worth more than celebrating ten successful launches.