What the Data Tells Us About ASO Experimentation — And Where Most Teams Leave Performance Behind

Not all store listing assets are created equal. Some drive more impressions. Others move conversion rates. Some require significant creative effort; others can be tested in an afternoon.

Over the past few years, Phiture has run ASO experiments across hundreds of apps, markets, and categories. This post summarizes what the data shows — by asset type, by platform, and by market.

Note: All figures are averages across multiple countries, app types, and categories. Variance is wide. What works for a casual gaming app in Brazil will differ from a fintech app in Germany.

If you want benchmarks specific to your vertical and market, get in touch with the PressPlay team.

Key Takeaways from Phiture’s ASO Experiment Data

1. Asset Impact Hierarchy

• Highest impact assets (both Android & iOS):
  • App icons
  • Titles (and subtitles on iOS)
• Next tier:
  • First 3 screenshots (core value communication)
• Lower, but still meaningful impact:
  • Feature graphics (Google Play only)
  • Full descriptions / long descriptions

Assets do not operate in isolation:

• Stronger icons → more tap-throughs.
• Better screenshots → higher CVR from that traffic.
• Strong metadata → more impressions to feed everything else.

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2. Metadata: High Success, Underused

• Success rate: ~25% (highest of all asset types).
• When successful:
  • Impression uplift: 13%–28%
  • CVR uplift: 8.24%–15.78%
• Includes: title, short description, keyword fields.
• Barrier to testing: almost zero (no design, no creative production).
• Short description (Google Play):
  • Appears directly under title in search results.
  • Critical for scanning users; often the deciding line for tap vs. scroll.
  • Rarely tested systematically.

Implication: Metadata should be in continuous test rotation, not a one-off setup task.

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3. Screenshots: High Impact, High Success, Rarely Updated

• Screenshot A/B tests:
  • Success rate: 10.7%
  • CVR uplift when successful: 4.92%–25%
• Reality in most teams:
  • Screenshots launched once, then left static for months or years.
  • Market, competitors, and visual norms evolve; assets don’t.
• First 3 screenshots:
  • Job: communicate core value in a single glance.
  • Increasingly hard as categories get crowded.
• Operational friction:
  • Requires design brief → iterations → experiment setup → weeks of cycle time.

Implication: Treat screenshots as a performance lever with scheduled refresh/testing, not a one-time branding exercise.

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4. App Icons: Modest Global Wins, Big Upside Outside the US

• Global icon tests:
  • Success rate: 3.13%
  • CVR uplift when successful: 1%–1.80%
• Outside the US:
  • Success rate: 12%
  • CVR uplift: 13.7%–150%
• Interpretation:
  • Non-US markets are less saturated with testing.
  • Larger opportunity surface and more dramatic wins.
• Common behavior:
  • Icons treated as fixed brand assets, rarely revisited.

Implication: Especially for apps with non-US audiences, icons should be treated as testable performance assets, not just brand artifacts.

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5. Custom Product Pages (CPPs): Strong with and without Paid

• Platform: iOS / App Store only.
• Baseline impact (CPP alone):
  • CVR uplift: 6%–8%
• With Apple Search Ads (ASA):

Are you actually testing the assets that move the needle?

Most ASO teams are running tests. Very few are testing the assets that matter most, often enough, across all the markets where the upside is largest.

Phiture’s multi-year dataset across hundreds of apps, markets, and categories shows a consistent pattern:

• Impact is concentrated in a few assets (icons, titles, first 3 screenshots, short descriptions/subtitles, metadata).
• Those same assets are the least systematically tested.
• The biggest gains come from continuous, parallel experimentation across all assets and markets, not from one-off, manual tests.

Use the numbers below as a directional map, not a guarantee. A casual game in Southeast Asia and a fintech app in Germany will behave very differently. The real opportunity is in how you structure experimentation, not in copying any single benchmark.

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1. Not all assets are created equal

Across both Android and iOS, Phiture’s data shows:

• Highest relative impact:
  • App icon

Ask most ASO teams how many store listing experiments they ran last quarter, and you’ll get a number somewhere between three and ten. Ask them how many they think they should have run, and the answer is usually much higher.

The gap between those two numbers is where installs are lost.

Over the past few years, Phiture have analyzed ASO experiments across hundreds of apps, markets, and categories — covering every major store listing asset, both platforms, and dozens of countries. What we found is that most teams already know which assets matter. The problem is rarely knowledge. It’s capacity.

This article shares what the data tells us about each asset type, how platform and market dynamics change the picture, and why the teams seeing the strongest results aren’t just testing smarter — they’re testing more, continuously, across everything at once.

One important note before we start: every figure here is an average across multiple countries, app types, and app categories. The variance is significant. A casual gaming app in Southeast Asia and a fintech app in Germany will behave very differently. Treat these numbers as a directional map, not a benchmark for your specific app. If you want to understand what the data looks like for your market and vertical, get in touch with the PressPlay team.

Every asset has impact. Most are untested.

Before looking at the numbers by asset type, it helps to understand the broader picture.

Your store listing is made up of many moving parts — icon, title, screenshots, short description, feature graphic, video, and more. Each one influences whether a user taps through to install or keeps scrolling. They don’t work independently: a stronger icon drives more tap-throughs, better screenshots convert that traffic, and tighter metadata brings more of it in the first place. The assets compound each other.

The table above summarizes Phiture’s research into the relative impact of each store listing element on both platforms. The pattern is clear: icons and titles carry the highest weight. The first three screenshots follow closely. Further down, assets like the feature graphic and full description still contribute, but their conversion impact in isolation is more limited.

What the table doesn’t show is the testing frequency gap. The assets with the highest impact are often the ones that get updated least often — not because teams don’t value them, but because updating them is operationally expensive. That gap is where most of the untapped conversion opportunity sits.

Metadata: the easiest win most teams aren’t taking

Success rate: 25% — the highest of any asset type

When successful: 13%–28% impression uplift / 8.24%–15.78% CVR uplift

Metadata — title, short description, keyword fields — is the highest-performing asset type in our dataset by success rate. When a metadata experiment works, it delivers meaningful gains in both impressions and conversion. And unlike screenshots or icons, it requires no design resource, no creative production, and no cross-team coordination.

You can generate a hypothesis, write a variant, and launch an experiment in the same afternoon.

Despite all of this, metadata is one of the least frequently tested assets we see in practice. Most teams set it up at launch, refine it once or twice for keyword reasons, and move on. It becomes part of the background — never quite urgent enough to prioritize.

The short description in particular is underutilized. On Google Play, it surfaces directly beneath the app title in search results. For users scanning quickly — which is most users, most of the time — it’s often the line that determines whether they tap through or keep scrolling. A short description that leads with your app’s clearest value signal converts differently from one written to fill a metadata field. Most teams have never run a single experiment to find out which one they have.

Metadata should be in permanent test rotation. The barrier to entry is almost zero. The upside, when it lands, is real.

Screenshots: high impact, high success rate — and rarely refreshed

Success rate: 10.7%

When successful: 4.92%–25% CVR uplift

Screenshot A/B tests have one of the highest success rates across all asset types. When they work, the conversion impact is significant. And yet, most store listings go live with a strong set of screenshots at launch — and then those screenshots don’t change for months or years.

The problem is that markets don’t stand still. Competitors update their listings. Visual styles evolve. What felt distinctive at launch starts to blend into the background six months later. The first three screenshots have a specific job: communicate your app’s core value in the time it takes someone to glance at a phone screen. That job gets harder, not easier, as the category gets more crowded.

For most teams, updating screenshots means briefing a designer, going through revisions, getting approvals, and setting up a new experiment. That cycle takes weeks. So it happens once or twice a year, if the backlog allows.

The result is a store listing that drifts quietly out of alignment with what actually converts — one missed experiment at a time.

Screenshots also don’t exist in isolation. A stronger screenshot set amplifies the impact of a well-tested icon. Better CVR from screenshots means more installs from the same impression volume that your metadata improvements are driving. The compounding effect is real, and most teams are leaving it on the table.

App icons: underexplored, especially outside the US

Global success rate: 3.13% / CVR uplift when successful: 1%–1.80%

Outside the US — success rate: 12% / CVR uplift: 13.7%–150%

At a global level, icon A/B tests show a modest success rate. But the picture changes substantially when you look outside the US. Success rate climbs to 12%, with CVR uplifts ranging from 13.7% to 150% when experiments succeed.

That’s not a small difference — and it reflects something important. We’ve run 92 icon experiments globally, 36 of them in the US. Non-US markets are less saturated with testing. The opportunity surface is larger, and the gains when experiments succeed tend to be more pronounced.

Icons are most often treated as brand assets. They go through a design and approval process tied to brand guidelines, and are revisited infrequently. That approach makes sense from a brand consistency standpoint — but it means one of the most visible elements of your store listing is rarely treated as a performance lever.

For publishers with meaningful audiences outside their core markets, icon testing is one of the highest-upside experiments you’re probably not running.

Custom Product Pages: a conversion lever most publishers are underusing

CVR increase (standalone): 6%–8%

With Apple Search Ads: up to 50% higher tap-through rate or 58% higher conversion

Custom Product Pages (CPPs) are App Store-specific, and the data on them is compelling. A 6%–8% CVR improvement from CPPs alone is meaningful. When combined with Apple Search Ads — where a CPP tailored to a specific search intent creates a fundamentally more relevant experience — the impact can be dramatically higher.

Most publishers have CPPs somewhere on their roadmap. Most haven’t moved them up. That’s an easy lever to pull, and for teams already running Apple Search Ads, the case for prioritizing CPPs is particularly strong.

In-App Events: the impression driver most teams treat as an afterthought

Per event: 0.40%–2.09% additional downloads

Impression uplift: 3.2%–18.67%

In-App Events deliver additional downloads — a mix of first-time installs and redownloads — alongside a meaningful impression uplift. That impression uplift is the underappreciated part. IAE surfaces your app in browse and search contexts beyond your standard store listing, reaching users who wouldn’t have found it otherwise.

That incremental visibility compounds with the conversion improvements you’re making through screenshots, metadata, and icons. You’re reaching more users and converting them better at the same time.

Most teams run In-App Events reactively — tied to product launches or seasonal moments. Teams that treat them as a systematic part of their ASO calendar see the impression benefits build over time.

Android and iOS are not the same optimization problem

Running the same ASO strategy on both platforms is one of the most common patterns we see — and one of the more costly ones.

The feature graphic is prominent on Google Play and carries meaningful first-impression weight. It’s not available to most App Store publishers. Subtitles carry significant weight on the App Store, sitting directly beneath the app title in search results. They don’t exist in the same form on Google Play. Custom Product Pages are App Store-only. In-App Event behavior differs between platforms in how events surface in browse and search.

Screenshot sequencing, short description prominence, icon display context — all of these show platform-specific patterns in the data. A creative strategy designed around Google Play’s layout won’t translate directly to the App Store. A metadata approach built for Google Play search won’t map to App Store search behavior.

The platforms reward platform-native thinking. Most ASO workflows aren’t structured to deliver it — not because teams don’t understand the difference, but because managing two separate hypothesis backlogs, two creative pipelines, and two experiment programs doubles the operational overhead.

Translating your store listing is not the same as localizing it

The non-US icon data points to something that runs deeper than icon testing: markets behave differently, and treating localization as translation consistently underperforms.

Screenshots that convert in the US often don’t resonate in MENA, where right-to-left layout expectations, color associations, and visual density norms differ. Icons that perform in Western markets may not land in Southeast Asia. Short descriptions lose their precision when translated without adapting the value framing for a local audience.

Every asset carries a cultural layer. Translation strips it out. Localization puts it back in — with variants designed around what actually converts local users, not what converts in your core market.

The publishers expanding most effectively into non-core markets don’t commit to full localization upfront. They run localized experiments first — testing culturally adapted variants in each target market, reading the conversion signal, and investing further where the data supports it. That approach changes the economics of expansion: instead of a large upfront commitment before you know if a market converts, you get real data from real users in weeks.

The pattern beneath all of this

Every section of this article describes a version of the same problem.

Teams know which assets matter. They understand that continuous testing produces better outcomes. The gap between knowing and doing is almost always capacity.

Manual experimentation sets a ceiling on what gets tested. When hypothesis generation, asset creation, experiment setup, and monitoring all require manual coordination, the number of active experiments at any given time is limited by your team’s bandwidth — not by the opportunity in your store listing.

Metadata, screenshots, icons, CPPs, In-App Events — running all of these in active rotation simultaneously, across platforms, across markets — isn’t a realistic manual workflow for most teams. So assets wait. Backlogs grow. The listing stays static while the market moves around it.

The teams seeing the strongest ASO results aren’t necessarily the ones with the sharpest hypotheses or the biggest design budgets. They’re the ones who can run the most experiments, learn from every result, and feed those learnings back into the next cycle — consistently, without burning out their team to do it.

This is what PressPlay is built for

PressPlay automates the full ASO experimentation loop — hypothesis generation, asset creation, experiment deployment, monitoring, and decisions — across every asset type, both platforms, and multiple markets simultaneously.

That means your metadata isn’t sitting untested for six months. Your screenshots are refreshed based on data, not design schedules. Your icon is treated as a performance asset, not a brand artifact. Your non-core markets get localized experiments, not copy-pasted EN-US listings.

Every asset stays in active rotation. Insights from one experiment inform the next. The learning compounds. And the conversion improvements that were sitting in your backlog start showing up in your numbers.

If you want to see what that looks like for your app and your markets, we’d like to show you.

Book a demo with the PressPlay team → https://www.pressplay.run/

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All figures in this article are averages across multiple countries, app types, and app categories. Results vary significantly by vertical, market, and competitive environment. For benchmarks specific to your app and market, talk to the PressPlay team.