The approval process is where you make the final decision about which AI-generated experiments move forward to testing. This human-in-the-loop step ensures quality control, brand alignment, and strategic fit before experiments enter your active backlog. Sometimes experiments are nearly perfect but need small adjustments—PressPlay allows you to modify experiments during the approval process to fine-tune them before deployment.
When reviewing an experiment, you're evaluating whether it meets your standards across several dimensions:
Visual Quality - For icons and screenshots, does the design meet professional standards?
Brand Alignment - Does this experiment align with your brand guidelines, tone, and visual identity?
Technical Correctness - Are there any technical issues like resolution problems, text cutoffs, or formatting errors?
Messaging Accuracy - For text experiments, is the messaging accurate and appropriate?
Hypothesis Alignment - Does this experiment properly test the intended hypothesis?
Testing Priority - Is this experiment worth testing resources relative to others?
Differentiation - Is this sufficiently different from control and other variants to generate meaningful learnings?
Market Appropriateness - Is this suitable for the target locale and cultural context?
Store Policy Compliance - Does this meet Google Play Store policies and guidelines?
Resource Requirements - Do you have the bandwidth to manage this test?
Timing - Is now the right time for this experiment, or should it wait?
PressPlay offers several ways to approve experiments depending on your workflow preferences:
For experiments that clearly meet your standards:
From the Review Dashboard, locate the experiment card
Click the "Quick Approve" button (checkmark icon)
Confirm the approval
The experiment immediately moves to your backlog
Quick approve is ideal when you're confident in the experiment based on the card preview alone.
For experiments requiring closer examination:
Click "View Details" on the experiment card
Review the full experiment details, including high-resolution assets
Check AI-generated rationale and expected impact
Verify store listing assignment and locale settings
Click "Approve Experiment" button
Optionally add notes about why you approved this
Confirm to move to backlog
For multiple related experiments:
Enable multi-select mode in the review queue
Select all experiments you want to approve
Click "Bulk Approve" in the actions toolbar
Review the summary of experiments being approved
Confirm batch approval
All selected experiments move to backlog simultaneously
Batch approval is particularly efficient when approving multiple experiments from a validated hypothesis.
Often, AI-generated experiments are 90% perfect but need small adjustments. Rather than rejecting these experiments, you can modify them during the approval process to fine-tune them to your exact specifications.
Modify experiments when:
The core concept is strong but execution needs tweaking
Small text changes would perfect the messaging
Minor visual adjustments would improve quality
Brand alignment requires slight modifications
Localization improvements are needed for specific markets
Reject experiments when:
The fundamental concept is flawed and can't be salvaged with edits
Major redesign is needed—it's faster to generate a new variant
The experiment doesn't fit your strategy regardless of execution
Store policy violations are present that modifications can't fix
To modify an experiment before approval:
Open the full experiment detail view
Click "Modify Before Approval" button
The experiment opens in the modification editor
The modification interface varies depending on the asset type:
For short descriptions, long descriptions, and title experiments:
Direct Editing - Edit the text directly in the interface
Character Counter - Real-time count showing remaining characters within store limits
Preview Mode - See how your text will appear in the store listing
Language Validation - Automatic checking for locale appropriateness
Keyword Highlighting - Shows which keywords are included in the text
Tone Adjustments - Shift from casual to professional or vice versa
Keyword Optimization - Add or emphasize specific keywords for ASO
Length Optimization - Trim or expand text to hit optimal character counts
Call-to-Action Refinement - Strengthen or soften CTAs based on your strategy
Brand Voice Alignment - Adjust language to match your brand guidelines
For app icon variants:
AI Re-generation - Request AI to regenerate specific elements while keeping others
Color Adjustments - Modify color palette to better match brand colors
Text Overlays - Edit or remove text on the icon
Element Adjustments - Request modifications to specific icon elements
Style Refinement - Adjust between more or less detailed, minimalist vs. complex
Describe what you want changed (e.g., "Make the background darker" or "Remove the text overlay")
AI processes your modification request
Review the updated icon
Iterate further if needed or proceed with approval
For screenshot sets:
Individual Frame Editing - Modify specific screenshots in the set
Caption Editing - Change text overlays on screenshots
Reordering - Adjust the sequence of screenshots
Replacement - Swap out specific screenshots while keeping others
Background Changes - Modify background colors or styles
Crop and Resize - Adjust framing of screenshots
When modifying screenshot experiments, maintain:
Visual Consistency - Ensure all screenshots in the set have a cohesive look
Narrative Flow - Screenshots should tell a logical story about your app
Brand Continuity - All frames should align with brand guidelines
Technical Specs - Meet Google Play's resolution and aspect ratio requirements
PressPlay's modification interface includes AI assistance to streamline the editing process:
Describe changes in plain language:
"Make the tone more professional"
"Emphasize security features more prominently"
"Use warmer colors in the background"
"Shorten this to 60 characters without losing the key message"
The AI interprets your instructions and applies appropriate modifications.
Based on your brand guidelines and past preferences, the AI may suggest specific improvements:
"This color doesn't match your brand palette—try [brand color]"
"Consider emphasizing [feature] based on your highest-performing experiments"
"This text exceeds the optimal length—suggested shortened version…"
Before committing to changes, preview how modifications will look:
Store Preview - See the modified asset in a realistic store listing context
Side-by-Side Comparison - View original AI generation vs. your modified version
Multiple Variations - Generate several modification options and choose the best
After making modifications:
Preview the modified experiment in the store listing context
Verify the changes address your concerns
Ensure no new issues were introduced
Check that the experiment still tests the intended hypothesis
Click "Approve Modified Experiment"
Optionally add notes documenting what you changed and why
Confirm approval
The modified experiment moves to your backlog
PressPlay maintains a record of modifications:
Original AI Version - The initial generation is preserved
Modification Log - What changes were made and when
Editor Attribution - Who made the modifications
Rationale Notes - Why modifications were necessary
This history is valuable for understanding what types of AI outputs typically need adjustment, helping train the system to better match your preferences over time.
Once you approve an experiment (whether as-is or after modification):
Experiment moves from review queue to backlog
Status changes to "Approved - Ready for Deployment"
The experiment is now available for assignment to store listings
Team members receive notifications if configured
After approval, you'll typically:
Assign to Store Listing - Connect the experiment to a specific CSL and locale
Set Priority - Determine testing sequence if you have multiple approved experiments
Schedule Deployment - Decide when to deploy the experiment
Configure Test Parameters - Set traffic allocation and duration if not using defaults
To maintain an efficient and effective approval workflow:
Document Standards - Create written guidelines for what gets approved
Brand Guidelines - Ensure all reviewers have access to brand standards
Quality Benchmarks - Establish minimum quality thresholds
Strategic Priorities - Clarify which types of experiments are high priority
Trust Your Instinct - If an experiment immediately feels right or wrong, act on that quickly
Use Quick Actions - Don't over-deliberate on clear-cut decisions
Batch Similar Reviews - Review related experiments together for consistency
Set Time Limits - Don't spend more than 2-3 minutes on any single experiment review
Minor Tweaks Only - If changes take more than 5 minutes, reject and regenerate instead
Use Templates - Save common modification instructions for quick reuse
Learn Patterns - Notice what types of modifications you make repeatedly and adjust generation prompts
Document Changes - Brief notes help train the AI to better match your preferences
Clear Queue Regularly - Don't let experiments pile up in review
Approve in Batches - Build a healthy backlog of approved experiments
Balance Quality and Speed - Perfect is the enemy of good—approve experiments that are good enough to test
Remember the Goal - You're testing to learn, not launching final assets—experiments don't need to be perfect
Monitor how your approval decisions impact results:
Approval Rate - What percentage of generated experiments do you approve?
Modification Rate - How often do you modify vs. approve as-is?
Approval to Win Rate - Do your approved experiments perform well in testing?
Category Performance - Which types of experiments (icons, screenshots, text) have the best approval-to-win ratio?
Use these insights to calibrate your approval process:
Too Strict - If you reject 80%+ and winners are rare, you might be over-filtering
Too Lenient - If you approve 90%+ but few experiments win, you might need higher standards
Well-Calibrated - Approving 50-70% with consistent winners indicates good judgment
The approval and modification process is where your strategic judgment shapes the AI's output into a high-performing testing program. By efficiently approving strong experiments and skillfully modifying good-but-not-perfect ones, you create a pipeline of quality tests that drive meaningful improvements to your app store performance.
