Experiment velocity is how many validated learnings your team generates per week—not how many tests you run. A team shipping 10 inconclusive tests learns nothing. A team shipping 3 tests with clear winners learns everything they need to iterate.
Most mobile teams have terrible experiment velocity. Not because they’re slow, but because the system is broken.
Key takeaways:
- The bottleneck isn’t engineering speed—it’s the handoff chain
- You can cut 80% of experiment cycle time without changing your tech stack
- The best teams treat experiments like hotfixes, not features
Why Mobile Experiments Take So Long
The average mobile A/B test takes 4-6 weeks from idea to results. Here’s where that time actually goes:
| Phase | Time | What’s Happening |
|---|---|---|
| Backlog | 1-2 weeks | Waiting for sprint planning |
| Development | 3-5 days | Engineer builds variant |
| QA | 2-3 days | Manual testing |
| App Store | 1-3 days | Review process |
| Ramp-up | 3-7 days | Reaching statistical significance |
| Analysis | 1-2 days | Interpreting results |
Look at that breakdown. Only 3-5 days is actual development. The rest? Waiting.
The experiment itself takes less than a week. The process around it takes a month.
The Real Bottleneck: Handoffs
Every handoff in your experiment pipeline adds delay:
- PM → Jira — Writing the ticket (30 min of work, 3 days in backlog)
- Jira → Engineer — Sprint planning, prioritization
- Engineer → QA — Waiting for test cycle
- QA → App Store — Waiting for review
- App Store → Users — Phased rollout
Five handoffs. Each one adds 2-5 days of wait time.
The math is brutal: 5 handoffs × 3 days average = 15 days of pure waiting.
This is why your competitor ships 20 experiments while you ship 2.
The Experiment Velocity Framework
Here’s how high-velocity teams cut cycle time by 80%:
1. Separate Experiment Code from Feature Code
Most teams treat experiments like features. Big mistake.
Features need:
- Architecture review
- Full test coverage
- Documentation
- Long-term maintenance plan
Experiments need:
- Quick implementation
- Easy removal
- Minimal footprint
- Fast iteration
Action: Create a dedicated “experiments” module in your codebase. Lighter review process. Faster shipping.
2. Decouple Deploy from Release
The App Store bottleneck kills experiment velocity. But you don’t have to ship a new binary for every test.
Options:
- Feature flags — Ship the code, toggle remotely
- Server-driven UI — Backend controls what users see
- OTA updates — React Native/Expo can push JS without App Store (check Apple’s guidelines)
Action: If you’re not using feature flags, start today. Statsig, LaunchDarkly, Unleash, or even a simple Firebase Remote Config.
3. Pre-Approve Experiment Patterns
Most experiments follow patterns:
- Button color/copy changes
- Layout variations
- Pricing display tests
- Onboarding flow tweaks
Action: Create a “pre-approved experiments” list with your engineering lead. These patterns skip architecture review. PM writes ticket → engineer implements same day.
4. Batch and Prioritize Ruthlessly
Not all experiments are equal. Some teach you a lot. Most teach you nothing.
High-value experiments:
- Test a specific hypothesis
- Have clear success metrics
- Can influence a real decision
- Target high-traffic areas
Low-value experiments:
- “Let’s just see what happens”
- No clear metric
- Low-traffic areas
- Already know the answer
Action: Score every experiment idea on (Impact × Confidence × Ease). Only run the top 20%.
5. Kill the Jira Ticket
Controversial take: Jira tickets are where experiments go to die.
The ticket creates a handoff. The handoff creates a wait. The wait kills momentum.
Alternative approaches:
- Slack thread → immediate discussion → same-day decision
- Weekly experiment review (30 min) → batch decisions
- PM/Eng pairing sessions → real-time implementation
Action: Try one week without experiment tickets. Just conversations. See what happens.
The 5-Day Experiment Cycle
Here’s what a high-velocity cycle looks like:
Day 1 (Mon) PM identifies opportunity, writes hypothesis
Day 1 (Mon) Quick sync with engineer, implementation starts
Day 2 (Tue) Variant complete, QA spot-check
Day 2 (Tue) Feature flag enabled for 10% of users
Day 5 (Fri) Statistical significance reached
Day 5 (Fri) Decision: ship winner or iterateFive days. Not five weeks.
The difference? Minimal handoffs, pre-approved patterns, feature flags, and ruthless prioritization.
Common Mistakes
1. Testing too many things at once
You don’t need to test everything. Test the things that matter. A/B testing your footer is not going to move the needle.
2. Waiting for “statistical significance” on everything
Some decisions don’t need p-values. If the change is low-risk and directionally positive, ship it. Save your statistical rigor for high-stakes decisions.
3. Treating experiments as engineering projects
Experiments are disposable. They should be easy to add and easy to remove. If your experiment code is as complex as your feature code, you’re doing it wrong.
FAQ
How do I convince my engineering team to prioritize experiments?
Frame it as less work, not more. Pre-approved patterns mean less review. Feature flags mean less App Store wrestling. Faster experiments mean fewer “can you build this variant” tickets cluttering the backlog.
What’s a good experiment velocity target?
Depends on your stage. Early-stage: 5-10 experiments/week. Growth-stage: 10-20. Enterprise: 3-5 (higher stakes, more rigor). But velocity without learning is meaningless. Track insights generated, not just tests run.
Should every experiment go through QA?
No. Create a risk matrix. High-visibility changes (checkout, payments) need full QA. Low-risk changes (button copy, colors) need a spot-check. Some experiments need no QA at all.
How do I measure experiment velocity?
Track: (1) Days from idea to live, (2) Experiments shipped per week, (3) Percentage with conclusive results. The third metric matters most—inconclusive experiments are wasted cycles.
Start This Week
You don’t need new tools to improve experiment velocity. You need fewer handoffs.
This week:
- Audit your last 5 experiments—where did time actually go?
- Identify one handoff you can eliminate
- Create a “pre-approved patterns” list with your engineer
- Run one experiment without a Jira ticket
The teams shipping 20x more experiments than you aren’t 20x faster. They just have 80% fewer handoffs.
Building a mobile experimentation practice? Clayva helps PMs run experiments without waiting for engineering sprints—AI writes the variant code, your dev just merges the PR.
