PressPlay enables you to run A/B test experiments across multiple markets and languages, allowing you to optimize your app store presence globally. This guide explains how to create, manage, and analyze experiments for different locales effectively.
Multi-locale testing means running A/B experiments in different geographic markets and language combinations. Each locale (like "en-US" for United States English or "ja-JP" for Japanese in Japan) can have unique store listing experiments tailored to that specific market.
Different markets respond to different creative approaches:
Cultural preferences: Colors, symbols, and messaging that work in one market may not resonate in another
Competitive landscape: What differentiates your app varies by market
User expectations: Different regions have different standards for app store presentation
Language nuances: Direct translations often miss cultural context and local expressions
Device usage: Markets have different Android device types and screen sizes
Locales use the format language-region:
en-US: English language, United States
en-GB: English language, United Kingdom
es-ES: Spanish language, Spain
es-MX: Spanish language, Mexico
pt-BR: Portuguese language, Brazil
zh-CN: Chinese (Simplified), China
Even when sharing a language, different regions often need different approaches:
en-US vs en-GB: American vs British spelling, cultural references, and feature priorities
es-ES vs es-MX: European vs Mexican Spanish vocabulary and preferences
pt-PT vs pt-BR: Portugal vs Brazil Portuguese differences
fr-FR vs fr-CA: French France vs Canadian French
Test one locale at a time:
Start with your primary market (usually largest user base)
Create and run an experiment for that locale
Analyze results and determine winning variant
Adapt winning approach for next locale
Repeat for each important market
Advantages:
Learn from each market before moving to the next
Easier to manage and track
Can adapt strategy based on previous results
Disadvantages:
Takes longer to optimize all markets
Delayed insights for secondary markets
Test multiple locales simultaneously:
Identify priority markets for testing
Create similar experiments for each locale
Run all experiments at the same time
Compare results across markets
Deploy winning variants per locale
Advantages:
Faster global optimization
Discover cross-market insights
All markets benefit simultaneously
Disadvantages:
More complex to manage
Requires more resources to create locale-specific variants
Can't learn from one market before testing another
Determine which markets to test based on:
User base size: Prioritize markets with significant users
Growth potential: Target emerging markets
Revenue contribution: Focus on high-value markets
Strategic importance: Consider long-term expansion plans
Before creating variants, understand each market:
Analyze competitor store listings in that locale
Review top apps in your category for that market
Consider cultural color associations and symbols
Understand local user pain points and needs
Check for any regulatory or platform requirements
For each locale, create customized variants:
Icons: Consider local visual preferences and competitive context
Feature Graphics: Adapt messaging and imagery for cultural relevance
Short Descriptions: Translate and localize, don't just translate literally
Navigate to Create → Manual Experiment
Select asset type to test
Choose first target locale from dropdown
Upload locale-specific variant
Configure experiment settings
Create experiment
Repeat for each additional locale
Simply translating word-for-word without cultural adaptation:
Pros: Fast and inexpensive
Cons: Often misses cultural nuances, may not resonate
When to use: Initial launch only, plan to optimize later
Working with native speakers to adapt messaging:
Pros: Culturally appropriate, natural-sounding copy
Cons: More expensive and time-consuming
When to use: All important markets
Recreating messaging to achieve the same emotional impact:
Pros: Highest quality, market-specific optimization
Cons: Most expensive, requires deep cultural expertise
When to use: Primary markets and key campaigns
When localizing short descriptions (80 character limit):
Text expansion: Many languages require more characters than English
German: Often 30-40% longer than English
Romance languages: Typically 15-25% longer
Asian languages: May be shorter but require careful character selection
Tip: Create the short description for the language that requires the most space first, then adapt to other languages.
United States (en-US)
Focus on differentiation and unique value props
Test bold, attention-grabbing visuals
Direct, benefit-focused messaging works well
High competition requires strong positioning
United Kingdom (en-GB)
More understated approach often performs better
British spelling essential (localise, optimise)
Subtle humor and wit can work well
Less aggressive marketing tone preferred
Germany (de-DE)
Detailed, informative messaging preferred
Quality and precision highly valued
Technical specifications appreciated
Privacy and data security important themes
Japan (ja-JP)
Clean, minimalist design often performs best
Quality and craftsmanship emphasized
Cute ("kawaii") aesthetics work for consumer apps
Social proof and popularity indicators important
Brazil (pt-BR)
Vibrant colors and energetic design
Social and community features highlighted
Value and affordability important
Mobile-first considerations critical
India (en-IN, hi-IN)
Value proposition and free features emphasized
Data usage and offline capabilities highlighted
Multiple language support important
Local payment methods featured
Color meanings vary significantly across cultures:
Red: Luck and prosperity (China, Asia) / Danger or warning (Western markets)
White: Purity and cleanliness (Western) / Mourning and death (China, Japan)
Green: Nature and growth (Global) / Religious connotations (Middle East)
Blue: Trust and security (Generally universal)
Yellow: Happiness (Western) / Royalty and prestige (Asia)
Organize multi-locale experiments effectively:
Use consistent naming: "[Locale] - [Asset Type] - [Hypothesis]"
Group related locale experiments by hypothesis
Set priorities to control execution order across locales
Use tags or labels to track market regions
Track experiments across locales:
Compare performance metrics between markets
Look for patterns across similar cultures or languages
Identify which variants perform universally vs. locally
Note unexpected results for deeper investigation
Relative performance: Percentage lift vs. control (not absolute numbers)
Winner patterns: Do similar variants win across markets?
Cultural insights: Which creative approaches work where?
Statistical significance: Ensure sufficient sample size per locale
Universal winners: Some variants perform well globally (rare but valuable)
Regional preferences: Distinct patterns emerge by region (most common)
Language-based patterns: Similar results across same language, different regions
Outlier markets: One locale responds very differently than others
Start with your biggest market: Learn there before expanding
Don't assume translations are enough: Always adapt for cultural context
Test the same hypothesis across locales: Makes comparison easier
Allow for longer test duration: Smaller markets need more time
Document cultural insights: Build knowledge base of what works where
Consider seasonal differences: Markets have different holiday periods
Monitor exchange rates: Currency fluctuations affect conversion rates
Check local competitors regularly: Competitive landscape shifts by market
Challenge: Insufficient traffic in smaller locales
Solution: Extend test duration or combine similar markets
Challenge: Managing many simultaneous experiments
Solution: Use sequential testing or prioritize top 3-5 markets
Challenge: Conflicting results across locales
Solution: Accept market differences, deploy different winners per locale
Challenge: Translation quality concerns
Solution: Work with native speakers or professional localization services
Custom Store Listings (CSL) - Managing localized store listings
Creating Manual Experiments - Step-by-step experiment creation
Understanding Asset Types - All 7 asset types and what to test
AI-Generated Experiments - Generate locale-specific variants with AI
