version: "1.0.1" name: beta-program-management description: "Running closed and open betas that produce real signal. Beta participant selection, structured feedback collection, beta-to-GA decision criteria, and the difference between soft-launch (no structure, no signal), kitchen-sink (everyone in, no actionable feedback), and structured beta (calibrated cohort, intentional feedback loops, clear graduation criteria). Triggers on beta program, alpha test, beta cohort, beta participant, beta feedback, beta to GA decision, design partner, early access program, closed beta, open beta, RC release. Also triggers when a feature is approaching launch and the team needs structured pre-GA validation, when prior betas produced noise rather than signal, or when the team has soft-launched before but wants more structured feedback this time." category: product catalog_summary: "Running betas that produce real signal. Participant selection, structured feedback, beta-to-GA decisions. Distinguishes soft-launch (no structure) from kitchen-sink (everyone in) from structured-beta (calibrated cohort with intentional feedback loops)" display_order: 13

Beta Program Management

A senior product leader's playbook for running betas that produce real signal. Closed and open betas, alpha programs, design partner programs, early access. Participant selection, structured feedback collection, beta-to-GA decision criteria, and the difference between soft-launch (no structure, no signal), kitchen-sink (everyone in, no actionable feedback), and structured beta (calibrated cohort, intentional feedback loops, clear graduation criteria).

Most betas underperform. Teams ship a beta because they think they should run a beta; participants are recruited loosely or open-flooded; feedback is collected ad-hoc through whatever channels exist; the decision to graduate to GA happens on calendar rather than on signal. The beta produced activity but not learning; the team launches with the same uncertainty they had before the beta.

This skill is the discipline that turns betas into decision input. Calibrated cohorts who match the post-launch user profile. Structured feedback that captures what the team needs to know. Mid-beta triage that uses what is being learned. Graduation criteria that distinguish "ready" from "we are tired of running the beta." The discipline is not bureaucratic; it is the difference between a beta that informs the GA launch and a beta that produces noise.

The voice is the senior product leader who has run betas with real signal and watched plenty of betas produce nothing. Concrete, opinionated about what produces signal, willing to call out where beta programs slide into ceremony.

When to use this skill: planning a beta for an upcoming launch, auditing why prior betas have not produced actionable signal, designing the beta participant experience, or deciding whether a feature is ready to graduate from beta to GA.

What this skill is for

This skill spans beta program design and execution. The PM and engineering distinction:

• feature-flagging is rollout mechanics; the technical layer for controlling who gets which features.
• `beta-program-management` (this skill) is participant management and feedback discipline; the human layer.
• feature-launch-playbook is the full launch (post-GA); this skill is what happens BEFORE GA.
• experiment-design is rigorous A/B testing; betas are softer, qualitative-leaning, smaller-N.
• user-feedback-aggregation is ongoing feedback streams; beta feedback is bounded to the beta period.
• discovery-research-synthesis is one-off discovery research; betas are validation-stage rather than discovery-stage.

The audience: senior PMs, product directors, engineering leads coordinating with product, customer success and support running beta cohorts, anyone planning a closed or open beta.

What is not in scope: the broader feature launch (covered by feature-launch-playbook); the technical rollout mechanics (covered by feature-flagging); the rigorous experimentation methodology (covered by experiment-design); the discovery-stage research that informs whether to build the feature in the first place.

Soft-launch vs kitchen-sink vs structured-beta

The keystone framing.

Soft-launch. "We will just turn it on for some users." No structured participant selection, no defined feedback collection, no graduation criteria. The beta runs because the team wanted to ship the feature without the full launch ceremony. Output: the feature is in production for some users; the team has no organized way to learn from their experience; signal accumulates through whatever channels happen to surface it; mid-beta course-correction does not happen because there is no structure to surface what should be corrected.

Kitchen-sink. Everyone gets in. The beta opens to whoever signs up. 5,000 beta users; 50 useful pieces of feedback; 4,950 silent users who provide no signal. Volume drowns signal. The team cannot tell which users matched the target post-launch profile. Feedback channels overflow; useful patterns get lost in noise; mid-beta triage cannot keep up. Output: a sense of "we ran a big beta" without the actionable feedback that smaller calibrated cohorts produce.

Structured-beta. Calibrated cohort selected by participant criteria. Intentional feedback loops the cohort knows to use. Clear graduation criteria that distinguish "ready for GA" from "tired of the beta." Mid-beta triage that uses what is being learned. Output: the beta produces decision-grade signal; the GA launch ships with confidence; problems that would have surfaced in production get caught and addressed in beta.

The litmus test. After the beta concludes, ask: what specifically did we learn from this beta that changed the GA launch? If the team can name 3-7 specific lessons, the beta was structured. If the team can only generally say "the beta went well," the beta was soft-launch or kitchen-sink.

Beta type decisions

Several axes of beta-type choice. The right combination depends on the launch context.

Closed vs open.

• Closed: invite-only. Participants are selected by criteria. Cohort is bounded.
• Open: anyone can join. Cohort is self-selecting.
• Closed produces calibrated signal; open produces volume signal that may not match the target user profile.

Alpha vs beta vs RC.

• Alpha: very early, internal or trusted-partner only, expectation of bugs.
• Beta: more polished, broader cohort, expectation of feedback rather than crash discovery.
• RC (release candidate): essentially launch-ready, last validation, expectation of production-grade quality.

Internal vs external.

• Internal: only employees use the feature.
• External: real customers use the feature.
• Internal betas catch only what employees would experience; external betas catch the full user-context complexity.

Time-bounded vs open-ended.

• Time-bounded: 4-week beta, 8-week beta, with a defined end.
• Open-ended: beta runs until the team decides to graduate.
• Time-bounded forces the graduation decision; open-ended risks beta-purgatory.

The combination decision. A typical structured beta might be closed + beta + external + 6-week time-bounded. A design partner program might be closed + alpha + external + open-ended. An open early access might be open + beta + external + time-bounded. The combination should match the kind of signal the team needs.

Detail in `references/beta-type-decisions.md`.

Participant selection criteria

The discipline that makes calibrated cohorts possible.

The criteria that work.

• Match the post-launch user profile. If the feature is for enterprise admins, beta participants should be enterprise admins, not curious individual users. The beta participant profile should resemble the target GA audience.
• Variety across relevant dimensions. Not all participants identical. If the feature has segment-specific behavior, the cohort spans segments. If usage volume varies, the cohort includes high-volume and low-volume users.
• Feedback willingness. Participants who agree to provide feedback through the structured channels. Soft commitment ("I will give feedback when I have time") is weaker than explicit commitment ("I will respond to weekly check-ins and complete the structured survey").
• Existing relationship strength. Customers with strong existing relationships are more likely to engage substantively. Customers in churn-risk are less likely to engage; their feedback may also be less representative.

The criteria that fail.

• Self-selection only. Open beta sign-ups skew toward enthusiasts and tinkerers; their feedback may not represent the broader target user.
• Highest-paying customers only. Skews toward enterprise patterns that may not generalize; misses smaller-team use cases.
• Internal employees only. Misses the customer-context complexity; signals "we tested" without "real users tested."

The cohort size question. Calibrated cohorts are usually 20-200 participants for closed external betas. Smaller (5-20) for design partner programs. Larger (200-2,000) for open early access. Beyond 2,000 the program is a soft-launch with beta branding.

Detail in `references/participant-selection-criteria.md`.

Beta cohort sizing

How big is enough; when does signal saturate.

Saturation patterns.

• Critical feedback (bugs, crashes, broken flows) saturates quickly. 20-30 participants surface most critical issues in the first 2 weeks.
• Behavioral feedback (how users actually use the feature) saturates more slowly. 50-100 participants needed to see usage patterns clearly.
• Edge case feedback saturates slowly. 100+ participants needed; some edge cases never surface in beta.

Sizing decisions.

• For betas focused on bug discovery: 20-50 participants for 2-4 weeks. Beyond this, returns diminish.
• For betas focused on behavioral signal: 50-200 participants for 4-8 weeks.
• For betas focused on validating product-market fit assumptions: 100-500 participants over 8-12 weeks.
• For betas focused on at-scale infrastructure validation: 500-2,000 participants over 4-8 weeks.

The "beta size matches signal need" principle. Cohort size follows from what the team needs to learn. Larger is not always better; calibrated is.

Detail in `references/cohort-sizing-patterns.md`.

Onboarding beta participants

How participants enter the beta and what they know going in.

The setup.

• Welcome communication that sets expectations: what the beta is, what feedback is expected, how long it runs, what happens at graduation.
• NDAs where relevant (for unannounced features, design partner programs).
• Feedback channel access: where participants give feedback, what format, what cadence.
• Support escalation path: who participants contact when things break.
• Compensation or incentive disclosure: free access to the feature post-GA, gift cards, swag, named recognition, etc.

The expectations contract.

• What the team commits to participants: communication cadence, response to feedback, transparent about graduation criteria.
• What participants commit to the team: feedback through the structured channels, not sharing externally during NDA, willingness to engage in interviews if requested.

Common onboarding failures.

• Vague expectations. Participants do not know what feedback is wanted; ad-hoc venting fills the channels.
• No NDA where appropriate. Beta features get screenshotted on social before the team is ready.
• Missing support path. Participants hit issues, do not know who to contact, churn out of the beta.
• No incentive clarity. Participants feel underrecognized; engagement decays.

Detail in `references/beta-onboarding-templates.md`.

Feedback collection patterns

Structured channels that produce signal rather than noise.

Channels that work.

• Structured surveys. 5-15 question surveys at defined points (week 1, week 4, end of beta). Specific questions tied to the team's learning goals.
• Async feedback forms. Participants submit specific feedback through a defined form. Fields prompt for use case, severity, expected vs actual behavior.
• Structured interviews. 30-60 minute interviews with a subset of participants (5-15 per beta). Focused on usage patterns, decision moments, and qualitative depth.
• In-product feedback widgets. Contextualized to the moment. The feedback is timestamped to the user's actual experience.
• Support tickets routed to beta-aware support. Beta participants get faster, more contextualized support; the support interactions surface usage friction.

Channels that fail.

• Slack channels for venting. Beta participants vent in real time; signal mixes with noise; nobody synthesizes.
• "Reply to this email with feedback." Returns long unstructured emails; synthesis is hard; useful patterns get lost.
• "Tell us what you think in the survey at the end." End-of-beta surveys catch only what participants remember; in-the-moment friction is forgotten.

Channel mix discipline. Most structured betas use 3-5 channels. Each channel surfaces different kinds of signal. The team synthesizes across channels.

Detail in `references/feedback-collection-patterns.md`.

Mid-beta triage and iteration

How the team responds to feedback during the beta.

The principle. Betas where the team responds to feedback during the beta produce stronger signal than betas where the team waits for the end.

The triage cadence.

• Weekly: review feedback across all channels. Categorize: critical bug, friction issue, feature request, positive signal, edge case.
• Bi-weekly: surface patterns. What recurring feedback are we seeing? What signal is converging?
• As-needed: critical issues get same-day response. Bugs that prevent core flows are not allowed to sit.

The iteration discipline.

• Critical bugs fixed during the beta. Beta participants experience the fixes; the post-fix experience informs the GA decision.
• Friction issues prioritized for fixes during the beta where feasible; documented for the GA decision where not.
• Feature requests captured for post-GA roadmap; not added during the beta unless they are graduation-blocking.
• Positive signal validated; surfaces what works, informs marketing copy and onboarding for GA.

The communication discipline. Participants are kept informed: "We received your feedback on X; we are addressing it in next week's beta update." Silence makes participants feel ignored; over-communication signals overhead. Calibrate.

Detail in `references/mid-beta-triage-and-iteration.md`.

Beta-to-GA decision criteria

Graduation gates that distinguish "ready" from "tired of running the beta."

The criteria.

• Critical bugs cleared. No known crashes, data loss, or core-flow failures.
• Friction issues addressed or accepted. Friction the team will not address by GA is documented and accepted as known limitation.
• Behavioral validation. Beta participants are using the feature in the patterns the team expected. Unexpected patterns are understood (either incorporated into the GA experience or addressed).
• Performance under load. The feature performs adequately at the scale GA will produce. (For infrastructure betas, this is the central criterion.)
• Documentation and support readiness. Help docs reflect actual usage; support team is trained on common issues; escalation paths work.
• Positive signal sufficient. Feedback is net-positive enough to launch with confidence. Not all participants delighted, but a substantial majority finding value.

The "we are tired of running the beta" anti-pattern. Beta has run long enough that the team wants to graduate regardless of signal. The graduation decision happens on calendar rather than on criteria. Resist this; either the criteria are met (graduate) or they are not (extend or reset).

The "perpetual beta" anti-pattern. Beta runs indefinitely because no firm graduation criteria were set. The team avoids the GA commitment by keeping the feature in beta. Force the graduation decision; if the feature is not ready for GA, identify what would make it ready or reconsider whether to ship at all.

Detail in `references/beta-to-ga-graduation-criteria.md`.

Beta wind-down and participant communication

How the beta ends.

The graduation announcement. Participants are told the feature is graduating. Specific date. What changes for them: continued access (typically yes), pricing changes (often beta participants get free access for some period), feature stability commitments (the GA version is what they will use going forward).

The transition.

• For most participants: nothing changes operationally. The feature stays available; the "beta" label drops.
• Pricing transitions communicated explicitly if applicable.
• Beta-only features that did not make GA are flagged. Participants who relied on those features are given alternatives or transition timelines.

The thank-you. Beta participants invested time providing feedback. Recognition matters: named in changelog (with consent), gift cards or swag, advance access to future betas. The recognition strengthens future-beta recruitment.

The postmortem. Internal review of what the beta produced. What was learned, what changed in the GA launch, what would be done differently in future betas. This feeds the team's beta program practice.

Detail in `references/beta-wind-down-communication.md`.

Common failure modes

Rapid-fire. Diagnoses in `references/common-beta-failures.md`.

• "Our beta produced no signal." Soft-launch pattern; no structured selection or feedback collection.
• "Our beta has 5,000 users and we cannot synthesize feedback." Kitchen-sink; cohort too large for the structured signal the team needs.
• "Our beta participants never gave feedback." Onboarding contract was unclear or feedback channels were friction-laden.
• "We graduated to GA on schedule but launched with critical bugs." Graduation criteria not enforced; calendar-driven graduation.
• "Our beta has been running for 8 months with no graduation." Perpetual beta; no firm graduation criteria; graduation decision avoided.
• "Beta participants vented on Twitter before the launch." NDA missing or unclear; trust violation early erodes participant willingness.
• "The beta surfaced patterns we did not act on." Mid-beta triage missing; feedback collected but not used during the beta.
• "The GA launch surprised us." Beta was not actually validating the GA experience; cohort or feedback structure was wrong.
• "Beta participants felt ignored after providing feedback." Communication discipline missing; participants commit time and want to know it mattered.
• "We ran a closed beta that was effectively employees only." Internal-only beta; missed customer-context complexity.

The framework: 12 considerations for beta program management

When designing or auditing a beta program, walk these 12 considerations.

1. Structured-beta, not soft-launch or kitchen-sink. Calibrated cohort, intentional feedback, clear graduation.
2. Beta type matches signal need. Closed/open, alpha/beta/RC, internal/external, time-bounded/open-ended.
3. Participants match the post-launch profile. Match the user the GA serves.
4. Cohort size calibrated to signal need. Smaller for bugs; larger for behavioral signal.
5. Onboarding contract clear. Expectations on both sides documented.
6. Feedback channels structured. 3-5 channels; signal not noise.
7. Mid-beta triage active. Feedback acted on during the beta.
8. Communication keeps participants engaged. Updates on what was heard and what is changing.
9. Graduation criteria explicit. Critical bugs cleared, friction addressed, behavioral validation, performance under load, support readiness, positive signal.
10. Wind-down treats participants well. Transition clarity, recognition, thank-you.
11. Postmortem feeds practice. Each beta improves the next.
12. Honest decisions on graduation. Either criteria met (graduate) or not (extend or reconsider). No tired-of-the-beta graduation.

The output of the framework is a beta program that produces decision-grade signal, supports the GA launch, and treats participants well enough to recruit them for future betas.

If required data is unavailable

This skill's output depends on data, measurements, or tool results it cannot generate on its own. When a required input, tool, or data source is unavailable or unverifiable, the sanctioned output is the deliverable with the gap stated: what was needed, what was actually obtained or verified, and which parts of the output are affected. Fabricating, estimating, or interpolating a required number to complete the deliverable is never sanctioned. A stated gap is a complete answer.

Reference files

• `references/beta-type-decisions.md` - Closed/open, alpha/beta/RC, internal/external, time-bounded/open-ended. The combination decision and how it matches signal need.
• `references/participant-selection-criteria.md` - Criteria that produce calibrated cohorts. Common selection failures. The cohort size question.
• `references/cohort-sizing-patterns.md` - Saturation patterns by feedback type. Sizing decisions for different beta goals. The size-matches-signal principle.
• `references/beta-onboarding-templates.md` - Welcome communication structure. NDAs, feedback channels, support paths, incentives. The expectations contract.
• `references/feedback-collection-patterns.md` - Structured surveys, async forms, interviews, in-product widgets, beta-aware support. Channels that work and fail. Channel mix discipline.
• `references/mid-beta-triage-and-iteration.md` - Triage cadence. Iteration discipline (what to fix during, what to defer). Communication with participants.
• `references/beta-to-ga-graduation-criteria.md` - The six graduation criteria. The "tired of the beta" anti-pattern. Perpetual beta anti-pattern.
• `references/beta-wind-down-communication.md` - Graduation announcement, transition, recognition, postmortem. Treating participants well.
• `references/common-beta-failures.md` - 10+ failure patterns with diagnoses and cures.

Closing: betas earn their keep with structure

A structured beta is one of the most useful product activities available. The cohort experiences the feature; the team learns from their experience; the GA launch ships with reduced uncertainty. The discipline pays back many times its cost.

A soft-launch or kitchen-sink beta is one of the least useful. The team spends weeks running an activity that produces ambient activity without converting to learning. The GA launches with the same uncertainty the beta was supposed to address.

The teams that earn returns on betas are the ones that take the structure seriously: cohorts calibrated to the GA profile, feedback channels designed for signal, mid-beta triage that uses what is being learned, graduation criteria that distinguish ready from tired, wind-down communication that treats participants well enough to recruit them again.

When in doubt about whether a beta is ready, ask: are participants matched to the GA user, are feedback channels structured, is the team responding to feedback during the beta, are graduation criteria explicit and being applied honestly, will participants want to join the next beta? If yes to all of those, the beta is real. If no to any, the gap is where the beta will fail to convert participation into learning.