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ASO — 17 Apr 2026
What Is App Store Optimization (ASO)? The Most Actionable Guide for 2026
Ivan Žgela
Ivan Žgela 
App store optimization (ASO) is the practice of improving an app’s visibility in the Apple App Store and Google Play, and turning that visibility into downloads. ASO covers everything from the keywords in your app’s title to the screenshots on your product page.
Two goals – get found. Get installed.
Sounds simple. But 557,000 new apps hit the Apple App Store in 2025 alone. Global app spending is on track to reach $233 billion in 2026. Tha means the majority of App Store downloads still start with a search.
If your app doesn’t show up when someone searches for what it does, you’re invisible. And if it shows up but the product page doesn’t convince anyone to tap “Get,” you’re wasting the visibility you have.
ASO fixes both problems. It covers keyword strategy, visual optimization, ratings, localization, A/B testing, and how all of that connects to your paid acquisition. For teams with real UA budgets, it’s the layer that makes every other channel more effective.
This guide breaks down how ASO works, what’s changed in 2025 and 2026, and how to build an ASO operation that actually compounds over time.
You’ll learn:
The Apple App Store and Google Play both use ranking algorithms to decide which apps appear when someone searches. ASO is about aligning your app’s store presence with what those algorithms reward.
Four things matter most:
Download velocity. Not lifetime installs. What matters is the rate of recent downloads compared to other apps in your category. An app gaining 500 installs a day will outrank one with more total downloads but slowing momentum. On the Apple App Store, this is one the strongest signals.
Conversion rate. Think of it this way: if people searching “budget tracker” install your app more often than the competition, the algorithm treats that as a relevance signal. High impressions with low conversion actually hurts your app over time.
Ratings and reviews. These don’t affect rankings the way most people think. Star rating, review count, and recency all matter, but indirectly. Strong ratings improve conversion rate. Better conversion improves download velocity. That’s what moves rank. The bottleneck is usually conversion, not the rating itself.
Retention and engagement. This one has changed. Both app stores now weight what happens after the install more than they used to. In Google Play, Android Vitals (crash rate, ANR rate, battery usage) directly suppress rankings for unstable apps. In Apple App Store, uninstall rates and session frequency factor in. Day 1 retention around 25–35% and Day 7 around 10–15% are commonly cited benchmarks, though they vary significantly by category. [Note: these ranges are industry estimates, not official store thresholds]
The Apple App Store and Google Play reward similar signals, but the metadata systems you use to target those signals are different.
Apple gives you a set of fields to optimize for search. A 30-character title, a 30-character subtitle, and a 100-character keyword field that users never see. That keyword field is comma-separated, hidden from the product page, and indexed directly by the search algorithm. Keywords listed first carry more weight. Every character is a chance to add more relevancy to the algorithm.
Google gives you similar options to optimize your app’s meta data. A 30-character title and an 80-character short description, and a full 4,000-character long description that Google indexes using natural language processing. Context matters more than repetition here. Google reads semantic relationships between words, so writing three clear sentences around your target terms beats stuffing the same keyword 50 times.
The practical difference: ASO in Apple App Store is a precision game with limited characters and exact targeting. Google Play ASO is a content game where you have space to write naturally, but the writing needs to be structured around the terms you want to rank for.
Apple has never published the full algorithm, but through official statements and years of testing, the App Store ranking factors break down into three categories.
Metadata signals that you control directly:
Performance signals that the algorithm measures from user behavior:
External signals that are harder to control but still factor in:
What doesn’t affect ranking are things like your app’s launch date, its price point, and star rating alone without review volume.
Google Play’s algorithm and ranking factors work differently in one big way: it reads your entire long description. That makes Google Play ASO closer to traditional SEO than Apple.
Metadata signals on Google Play include:
Google uses skip-gram analysis on descriptions, meaning it understands relationships between words, not just exact matches. Writing naturally matters more than stuffing keywords.
Performance and quality signals include the following:
The biggest practical difference: in Apple App Store, your keyword field is hidden and you get 100 characters to work with. On Google Play, your description is public and indexed, so you’re writing for both the algorithm and the reader.
Since June 2025, Apple indexes screenshot caption text for keyword ranking. Captions used to be decorative. Now they count as metadata. If your captions don’t include target terms, you’re leaving an optimization surface empty.
Custom product pages got a bigger role in July 2025. Before that change, they only appeared through paid campaigns. Now they show up in organic search results for their assigned keywords. Apple also doubled the limit from 35 to 70 active pages per app in early 2026, which turns custom product pages from an advertising feature into an organic search tool.
The bigger shift is about engagement. Both app stores are moving weight from raw download counts toward retention and stability metrics in 2026. Apps with strong Day 7 retention are ranking above competitors that have more downloads but weaker engagement. For ASO teams, this means the ranking algorithm now cares about product quality, not just store listing quality.
One more change worth noting: Apple added more ad slots throughout search results in March 2026, pushing organic listings lower on the screen — but also made #1 organic position even more important than before. Organic click-through rates have dropped for apps without a paid presence.
It’s one more reason paid and organic can’t be managed as separate app marketing activities.
Optimized app keywords influence which searches your app shows up in. Get them wrong and it doesn’t matter how good your screenshots are or how many stars you have. The app won’t be found.
The keyword building process has four stages: research, prioritize, target, measure. Then you do it again.
Start by generating 30 to 50 candidates. Pull them from everywhere you can. Brainstorm terms based on what the app does, the problems it solves, and how users describe those problems in their own words. Look at what competitors rank for.
In Apple App Store, the keyword field is hidden from users, but ASO tools like App Radar can reverse-engineer which terms a competitor targets. Check autocomplete suggestions in both app stores – if you’re running Apple Ads, search term reports will show you the actual queries people type before installing.
That last source is underused. Apple Ads campaigns generate real conversion data on which search terms lead to actual installs, not just impressions. For apps operating in multiple markets, paid search data is especially useful in non-English locales where local search behavior is hard to predict from another location.
Not every keyword is worth chasing. Narrow the list to 15 to 20 terms based on four things.
Search volume. Apple uses a Search Popularity score on a 5 to 100 scale. It’s relative, not absolute, and it’s exponential. A score of 90 generates roughly three times the impressions of a score of 60. Half of all keywords score below 25, which makes them low-value targets. Nearly everything above 80 is a brand name.
If you want to take it to the next level, App Radar combines app keyword search volume with other important metrics.
Competition. How many other apps are actively targeting this term? Generic one-word keywords attract the most competition. Two-word combinations tend to hit the sweet spot between volume and achievable ranking. About 49% of the top 300 app store keywords are two-word phrases, according to public app store analysis.
Conversion potential. A keyword that generates impressions but not installs wastes a metadata slot. If you have Apple Ads data, tap-through rate and conversion rate by keyword give you the clearest picture of which terms actually drive downloads.
Relevance. The keyword has to match what the app does. Ranking for an irrelevant term might generate impressions, but the poor conversion rate that follows will drag down the app’s overall ranking strength.
Where you put a keyword matters as much as which keyword you pick.
On the Apple App Store, you’re working with a 30-character title (highest ranking weight), a 30-character subtitle, and a hidden 100-character keyword field. The keyword field is comma-separated with no spaces. Keywords placed earlier in the field carry more weight.
One rule to remember: don’t repeat keywords across title, subtitle, and keyword field. The algorithm already cross-references them. Repeating “budget” in all three wastes characters you could use for other terms.
On Google Play, the 30-character title still carries the most weight. But the 4,000-character long description is where the real work happens. Google indexes the entire thing using natural language processing.
Keyword frequency matters, but context matters more. Three well-written sentences that naturally include your target term will outperform a description stuffed with the same word over and over. Google’s spam filters are particularly aggressive with high-visibility apps.
One recent change: since Apple now indexes screenshot caption text for keyword ranking (a mid-2025 change), keyword-aware captions have become another metadata surface worth optimizing. Apps that run quarterly screenshot tests with keyword-aware captions see 20 to 30% higher conversion rates compared to those with static, untested captions, based on the data we see at SplitMetrics.
After a metadata update, watch three ASO KPIs and metrics per keyword: ranking position, impressions, and conversion rate.
Keyword ranking changes take 4 to 8 weeks to stabilize. Conversion changes from visuals show up faster, usually within 2 to 4 weeks.
The measurement phase should improve your research approach. Terms that underperform get swapped out. Terms that convert well in Apple Ads but rank poorly in organic become priority targets for the next cycle. Keywords that overperform get reinforced with stronger metadata placement.
This cycle doesn’t end. Keyword relevance shifts as competitors update their metadata and search patterns change with seasons. Strong apps with active marketing teams treat this as an ongoing process, not something they revisit once a quarter.
Before optimizing app store visuals and conversion rate, there is one key element – ranking for a keyword means nothing if nobody taps “Get.”
That’s the conversion side of ASO, and most teams underinvest in it. They spend months on keyword research, update metadata quarterly, then leave the same screenshots from 2022 sitting on the page.
The result: traffic goes up, installs stay flat.
Your app store listing is a landing page. Every element on it either moves someone toward downloading or gives them a reason to scroll past.
The first three screenshots do most of the work. In search results on the App Store, they appear right below the title. On Google Play, the icon and feature graphic load first. Visitors make a snap judgment before they read a single word of your description.
Icons set the first impression. Screenshots sell the experience. Preview videos can help, but low-quality ones actually hurt conversion. If you can’t produce something polished, skip video entirely.
The text elements matter too, just less than you’d think. Your subtitle (iOS, 30 characters) and short description (Google Play, 80 characters) are the only text most people read before deciding. The long description on Google Play gets indexed for search but rarely read by users.
The old way how app teams worked was that a designer made new screenshots, the team debated them in a meeting, someone picked a favorite, it shipped. Nobody measured what changed.
The better way known to almost any app team is A/B testing. But how you make it effective is a different thing.
Both app stores offer native testing. Apple has product page optimization, which lets you test up to three icon or screenshot variants against your current page. Google Play has store listing experiments, which supports up to three variants across icons, screenshots, short descriptions, and graphics. Google also lets you run five localized experiments at the same time.
The catch with native testing: you need real traffic to reach statistical significance. For apps with fewer than 50,000 monthly page views, a single test can take weeks. You also can’t test before launch or measure pre-tap behavior like scroll depth.
That’s where third-party tools come in. SplitMetrics Optimize, for example, runs tests using behavioral data before changes go live. You see how users interact with different page layouts, not just whether they downloaded.
Whichever method you use, test one variable at a time. Changing your icon and screenshots simultaneously tells you something changed, but not what.
Icon changes tend to produce the largest swings. A better icon can move conversion by a few percentage points, with some tests showing lifts of 5–10%. Screenshot improvements are more modest, usually 5–15%, but they compound across every visitor who lands on your page.
A common guideline for reliable results: roughly 400–500 conversions per variant at 95% confidence. Anything less and you’re likely reading noise.
How often should you test? For high-traffic apps, monthly screenshot tests are realistic. Icons are a bigger brand decision. Two to four icon tests per year is typical.
In Apple App Store, custom product pages let you create alternate versions of your store listing with different screenshots, preview videos, and promotional text. Each one gets its own URL. You can build up to 70 of them.
As of July 2025, custom product pages can rank organically for their assigned keywords. That means a user searching “pdf scanner” could see a custom product page tailored to that search term instead of your default page. The screenshots match their intent. The messaging speaks directly to what they searched.
Only 31% of apps were using custom product pages as of Q1 2025. Among those that did, SplitMetrics data showed conversion lifts up to 8.6%. That’s a gap worth highlighting.
Google Play’s equivalent of custom store pages are custom store listings, which let you customize by country, pre-registration status, or Google Ads link. Different mechanism, same principle: match the page to the visitor’s context.
App ratings affect both ranking and conversion. But there’s a specific threshold that matters most: 4.0 stars.
Apps that drop below 4.0 see a sharp decline in tap-through and download rates. SplitMetrics data suggests the drop can reach roughly 50%. Above 4.0, the differences between 4.2 and 4.5 are more gradual. Below it, the damage is steep.
Our advice is to respond to negative reviews. Use in-app rating prompts timed to positive moments (after a successful action, not during onboarding). Aim for 4.4+ as your operating target.
The most common question from anyone approving ASO budget: “What do we get back?”
The honest answer: ASO compounds. It’s not like paid ads where you spend $1,000 and get a predictable number of installs that week. Organic growth builds on itself. Better metadata brings more visibility, which brings more downloads, which sends stronger performance signals to the algorithm, which brings more visibility. The first month looks modest. Month four looks different.
But you can still model it.
Start with what you already have: monthly organic installs, your store page conversion rate, and lifetime value per install. Those are your baseline.
Based on SplitMetrics’ ROI framework, a conservative ASO improvement adds roughly 8% more organic installs and a 10% lift in conversion rate. Those are two levers you have under your own control at the same time, and they multiply.
For example, a simple math on a mid-size app: 10,000 organic installs per month, 12% conversion rate, $10 LTV. An 18% combined improvement means about 1,800 additional installs per month. At $10 LTV, that’s $18,000 in incremental monthly revenue. Against a $5,000/month tools and specialist cost, that’s a 3.6x return with payback in under five weeks.
The numbers scale with your baseline. Higher LTV apps see faster payback. Lower-traffic apps take longer to reach the same multiples but still benefit from the compounding effect.
On average, these are costs your app should consider if you want to invest properly in ASO:
Conversion changes (new screenshots, updated icon) tend to show results first. For apps with strong brand presence and steady traffic, that can mean visible movement within two to four weeks.
For smaller or challenger apps competing in crowded categories, it often takes longer. Keyword ranking changes are slower across the board: four to eight weeks before you can measure impact with confidence, sometimes more.
Most teams see compounding growth start around month two or three. By month four, the organic baseline has shifted enough that the investment is clearly paying for itself. The teams that see the strongest returns are the ones that treat ASO as an ongoing practice, not a one-time project.
If your category has extremely low search volume, there may not be enough organic demand to capture. If the app itself has quality issues (crashes, poor onboarding, low retention), fixing the store listing won’t fix the underlying problem.
Long story short – if your timeline is “we need results this week,” ASO isn’t the channel. It uses synergies, but it needs time.
App ratings and reviews affect both your ranking and your conversion rate, but not in the way most teams expect.
The average star score matters less than velocity. An app with a 4.2 rating that collects 100 new reviews per week will typically outrank a 4.5-rated app getting five reviews per week. Both stores interpret review velocity as a signal that the app is actively used and improving. Recency matters too: recent reviews carry more weight than old ones in both algorithms.
As covered in the visual optimization section, apps below 4.0 stars face steep conversion drops. Your operating target should be 4.4 or higher.
The biggest mistake teams make is asking for ratings at the wrong moment. Prompting during onboarding, after a crash, or while the user is stuck in a flow produces low scores. Those low scores then drag your average down and hurt conversion.
The right trigger is a positive moment. For a fitness app, that’s after completing a first workout. For a finance app, after a successful transaction. For a productivity tool, after creating or exporting something. The user just got value from your product – that’s when they’re most likely to rate it well.
Use the native system prompt (the iOS or Android rating dialog) rather than a custom-built one. It looks official, it’s less intrusive, and users trust it more. Gate it behind a positive action so you’re only asking people who’ve already had a good experience.
One hard rule: never ask users to “rate us 5 stars.” Both Apple and Google prohibit this, and it backfires with users who resent being told what to do.
Most teams treat review responses as a customer support task, but responding in the right way also adds ranking signals.
Responding to negative reviews shows both the algorithm and future visitors that someone is paying attention. A user reading a 2-star review that got a thoughtful, specific response (“We fixed the crash in v2.1, released yesterday”) reads the situation differently than one with no response at all.
Prioritize 1-2 star reviews first, especially recent ones and those mentioning specific bugs. Three-star reviews are opportunities – these users are on the fence and a good response can turn them around. Four and five-star reviews are nice to acknowledge but lower priority.
SplitMetrics clients often reveal that review management keeps coming up as a priority. Teams that came in asking about keywords and rankings often got more excited about AI-powered review management once they saw it in action.
One prospect described their current process: a customer success team handling reviews manually, with a library of templated responses built through ChatGPT. That works at small scale, but breaks when you’re managing reviews across dozens of markets and languages.
A new app needs momentum. Ask for reviews earlier (still after a value moment, but set the threshold lower). Respond to every single review. Target 10–50 reviews per week in the first months to build enough signal for the algorithm to rank you.
An established app needs consistency. Optimize your prompt timing for quality over quantity. Focus responses on the negative reviews that affect perception. Monitor sentiment trends monthly – if your average is slowly declining, something in the product needs attention before it hits your rankings.
The feedback loop between reviews and product is underused. Aggregating review themes monthly and briefing the product team turns your review data into a roadmap input, not just a support task.
Translating and localizing your app is the first thing teams try when expanding internationally. It’s also the first thing that fails.
Translating your English metadata word-for-word into German or Japanese doesn’t account for how people in those markets actually search. The keywords are different. The search behavior is different. The competitors are different. A direct translation of “lose weight” into German might be grammatically correct but nobody searches for it that way.
The industry term for what works is culturalization – adapting your entire store presence to regional market dynamics, not just swapping languages.
A global keyword strategy doesn’t work because search volume, competition, and user intent vary by region. “Fitness” might dominate in the US, while “Gewicht verlieren” (lose weight) performs better in Germany and “perte de poids” works in France. Different words, different volumes, different competitive density.
For each market you enter, you need local keyword research. Analyze the top 10–20 competitors in that specific market. Identify which keywords have real volume and manageable competition locally. Then build your metadata around those terms, not around translations of your English keywords.
This is where most teams hit a wall. Running separate keyword strategies for five or ten markets is a lot of work, especially when ASO is already a side task for someone on the UA or product team.
Not every market needs the same level of investment. A tiered model keeps the workload realistic.
Your top revenue markets get full localization: translated and adapted title, subtitle, description, keyword field, plus localized screenshots and potentially a localized preview video. Estimated time for the changes is approximately four to six weeks per market.
Secondary growth markets get selective localization: title, subtitle, and keyword field translated with local keyword research, but you keep your default screenshots. That takes two to three weeks per market.
Long-tail markets get translation only: metadata translated from your core keyword set, default creative assets. That is about a week per market.
The test-and-rollout approach works well here. Pilot full localization in one or two markets. If you see a 10–20% lift in organic installs, expand to the next tier. One new market per quarter is a sustainable pace for most teams.
Keyword demand fluctuates by season, and those patterns differ by market. Fitness apps spike in January (New Year’s resolutions) and again in May–June. Finance apps see surges around earnings seasons. Shopping apps peak October through December.
The window for most seasonal opportunities is four to six weeks. Teams that prepare seasonal keywords and creative assets two months ahead capture the traffic. Teams that react after the spike has started are too late for the algorithm to catch up.
TIP: Plan seasonal updates into your localization calendar. If you’re running in five markets, that means five sets of seasonal keyword adjustments, ideally deployed two to three weeks before each market’s demand spike.
Apps with localizations across ten or more markets see 35–50% higher conversion rates compared to single-market apps, according to Appalize’s 2026 benchmarks.
The App Store indexes keywords from multiple language localizations within a single market. Optimizing beyond your primary language locale is a step most US-focused apps skip entirely.
Localized screenshots alone can improve conversion 5–15%. Combined with localized keywords and descriptions, full localization can drive 10–25% organic install increases per market.
Most apps have what the data consistently shows as low-hanging fruit: based on what SplitMetrics sees across client accounts, 20–30% untapped potential sitting in secondary markets where nobody has bothered to do proper keyword research.
Getting featured on the App Store or Google Play means Apple or Google’s editorial team has selected your app for promotion on one of the store’s high-visibility surfaces.
On the App Store, featuring includes the Today tab, App of the Day, Game of the Day, and curated collections.
On Google Play, featuring means appearing in Editors’ Choice, curated collections, or promotional placements across the store’s browse surfaces.
Apps that receive an App of the Day placement on the App Store routinely see download spikes that are several multiples of their normal daily volume. The effect is temporary, usually lasting a few days to a week, but the ranking boost from that surge of installs can persist for weeks after the feature ends.
Your app’s rank in search results and category charts climbs because both stores weight recent download velocity heavily.
Here’s the catch: featuring is earned, not bought. You cannot pay for an App of the Day slot or an Editors’ Choice badge. Both stores have editorial teams that independently select apps based on quality, design, and user experience.
Apple maintains a formal featuring nomination process. You submit a self-nomination form that describes your app, what makes it distinctive, and why it deserves promotion. Apple’s editorial team reviews submissions and selects apps that meet their quality bar.
There is no quota or schedule that guarantees selection. Plenty of well-built apps never get featured, and some apps get featured repeatedly. The editorial team values apps that feel current and take advantage of new iOS capabilities.
Google Play’s featuring works differently. The Editors’ Choice program recognizes apps with strong design and user experience, similar to Apple’s curation. But Google also offers a self-service promotional content system (LiveOps) that gives developers more control over visibility.
Through the Google Play Console, developers can submit promotional content tied to in-app events, updates, offers, or milestones. These submissions go through a review process, typically approved within four days, and appear on the store listing, in personalized recommendations, and across browse surfaces. Google recommends submitting promotional content at least 60 days before the event date.
One practical difference from Apple: Google uses Android Vitals data as a gating factor. Apps that exceed the crash rate and ANR thresholds covered in the ranking factors section are less likely to receive featuring, and may lose visibility in browse results entirely. Technical health directly affects your eligibility.
Getting featured doesn’t replace ASO. Featured positions amplify whatever ASO foundation you already have. If your store listing converts well before a feature, the download surge translates into maximum installs. If your listing is weak, a lot of that traffic bounces.
The relationship works in the other direction too. Strong ASO performance, including high ratings, frequent updates, and well-tested visuals, makes your app more attractive to editorial teams in the first place. Apple’s documentation explicitly mentions “great app metadata” and “high-quality screenshots” as factors they consider.
For enterprise teams, the strategic play is treating featuring as a bonus that rewards consistent ASO execution rather than a growth strategy you can plan around. The teams that get featured most often are the ones already doing everything else right.
Most teams run ASO and Apple Ads as separate activities. One person handles metadata. Another manages paid campaigns. They rarely share data. That disconnect costs money.
Paid downloads might directly influence organic rankings, based on SplitMetrics experience. Download velocity is the strongest ranking signal on both app stores, and Apple Ads drive download velocity. When someone installs your app through a paid ad, the algorithm reads that as demand. Your organic ranking for keywords in your metadata gets a boost. Sometimes that boost persists after the paid campaign ends.
You can design your entire keyword strategy around this loop.
Apple Ads gives you something no ASO tool can: real conversion data by keyword. You can see exactly which search terms produce taps, which produce installs, and at what rate.
Run Discovery and Probing campaigns for two to four weeks. Discovery campaigns use Search Match to surface keywords you hadn’t considered. Probing campaigns test specific keywords at low budgets to measure tap-through rate and conversion rate before you commit metadata space to them.
The decision framework can be kept simple, for instance – keywords with strong tap-through (above 7%) and strong conversion (above 20%) earn a spot in your app name or subtitle. Keywords with high volume but weak conversion go to the keyword field or get dropped entirely. You’re using paid data to make organic decisions with real evidence instead of guessing.
This workflow comes up often when teams already run Apple Ads and already do ASO but have never connected the two. The data is sitting in their Apple Ads dashboard. They just haven’t used it to make organic decisions.
Custom product pages make this connection even tighter. You can create two or three custom product page variations with different screenshots or messaging, run Apple Ads campaigns pointing to each one, and measure which version converts best.
The winning creative then moves to your main store listing or gets deployed as an organic custom product page. You’ve validated the approach with paid traffic before touching anything that affects all your organic visitors.
And since custom product pages rank organically for their assigned keywords, that means a custom product page you originally built for an Apple Ads campaign can also appear in organic search results. The paid testing investment pays off twice.
There’s a risk to running paid and organic on the same keywords. If you already rank well organically for “budget planner,” bidding on that same term means your paid ad might capture users who would have found you for free. Your total installs stay the same, but now some of them cost money.
The fix: focus paid spend on keywords where you rank poorly organically (position 20 or worse). Use paid to build velocity for those keywords, which lifts your organic rank over time. For keywords where you already rank in the top five, keep the bids stable, but let organic carry the traffic.
Monitor this by tracking organic install volume 30 days before and during a paid campaign on the same keywords. If organic drops more than 5–10% while paid is running, cannibalization is happening.
Branded keywords (your app name, common misspellings) are the one case where bidding alongside strong organic rank makes sense. Competitors can bid on your brand name in Apple Ads. If they do, their ad appears above your organic listing. Bidding on your own brand prevents that.
The cost is usually low because your conversion rate on branded terms is high (60–80% compared to 10–20% for non-branded). It’s insurance, not acquisition spend.
Once you’re running your app in multiple markets, manage keyword strategies, coordinate metadata updates with Apple Ads campaigns, things get much more complex for enterprise level apps. App marketing teams try to find the right model based on their app growth goals.
Stage 1: Reactive. No dedicated ASO resource. Metadata gets updated when someone remembers. Keyword choices are based on intuition. There’s no testing cadence and no regular competitive monitoring. Based on patterns SplitMetrics sees across client engagements, most apps at this stage have 20–30% untapped organic potential sitting on the table. Getting from here to measurable results takes 8–12 weeks of focused work.
App teams tend to move through a predictable progression.
Stage 2: Structured. A dedicated person or agency partner owns ASO. There’s a keyword research process, a quarterly review cycle, and usually a single-market focus. The team uses an ASO platform for tracking and has started running tests. Based on SplitMetrics client data, improvement at this stage runs 15–25% on top of Stage 1 gains, and results come faster because the feedback loop is tighter — four to eight weeks.
Stage 3: Scaled. Multi-market operations. Continuous A/B testing. Custom product pages deployed by keyword and by market. Paid and organic data flowing between teams. Rapid competitive response when rankings shift. Possible improvement at this stage is 10–20%, but on top of a much higher baseline. Changes show results in two to four weeks because the infrastructure is already in place.
Most teams reading this article are somewhere around Stage 2.
The specific resources depend on your stage, but most teams that run ASO well have a few best practices in common:
For teams that don’t have the headcount to build this internally, managed ASO services fill the gap. SplitMetrics helps with keyword strategy, metadata execution, creative testing, and Apple Ads alignment for teams that need expert execution without hiring a full-time specialist.
The pattern that comes up most in conversations with growth teams is this one: ASO and paid UA operate in silos.
In the majority of companies running Apple Ads, the ASO and paid teams are separate. The ASO person doesn’t see Apple Ads conversion data. The paid team doesn’t know which keywords the ASO person is targeting organically.
The result is duplicate effort, missed signals, and rising blended CAC. Fixing this doesn’t require reorganizing your team. It requires a shared keyword map and a monthly sync where both sides compare what’s working.
SplitMetrics research shows that up to 50% of companies exploring ASO tools have never used a dedicated ASO platform before. 47% of our prospects mention lack of in-house ASO expertise as a blocker.
So the gap isn’t usually budget, but knowing where to start and having the right process.
In dozens of conversations with growth teams evaluating ASO, certain questions come up again and again. These aren’t theoretical, but actually the things real teams need answered before they commit.
This was the single most common question, raised in over half of all conversations.
The short answer: tie organic installs to revenue using your existing LTV data. Leadership doesn’t care about keyword rankings or search visibility scores. They care about cost per install and lifetime value.
Build a simple before-and-after model. Track organic installs and conversion rate for 30 days before any ASO work. Then track the same metrics after changes go live. Multiply the incremental installs by your average LTV. That’s the number your CFO wants to see. If you can also show that blended CAC dropped (because organic is picking up volume that paid used to carry), the case gets even stronger.
Yes, we strongly believe you need a dedicated ASO person, even you work with an external agencies.
Most teams start with ASO as a shared responsibility. A UA manager or product marketer handles it alongside other work. That’s fine for the start when running keyword research, updating metadata quarterly and monitoring ratings is the main work. But once you’re operating in multiple markets or coordinating with Apple Ads, the workload outgrows the side-job model.
The alternative to hiring is partnering with an agency or managed service that handles strategy and execution. About 15% of the companies we speak with are in the process of transitioning from agency to in-house, which suggests most teams start with external help and bring it internal once they understand the workflow.
It depends on what you’re changing and how established your app is.
Creative changes (new icon, updated screenshots) tend to move conversion rates within two to four weeks for apps with steady traffic. Smaller apps may need longer to collect enough data to see a clear signal.
Keyword ranking changes take longer. Four to eight weeks is typical before you can measure movement with confidence. A full metadata overhaul in a new market might take three months before you see compounding organic growth.
The common mistake is treating ASO like a campaign with a launch date and an end date. It’s a continuous practice. The teams that see the strongest results are the ones that update, measure, adjust, and repeat on a regular cycle.
For the App Store, every four weeks is a good cadence. Apple’s algorithm needs roughly three to four weeks to fully process metadata changes, so updating more frequently than that can actually hurt your ranking stability.
For Google Play, six to eight weeks between updates works better. Google’s indexing of long descriptions and short descriptions takes longer to settle.
Outside of that regular cadence, update reactively when:
Don’t change everything at once. Update one or two elements per cycle so you can attribute results to specific changes.
Yes, in a few specific ways.
Mobile games ASO relies more heavily on generic discovery keywords (“puzzle game,” “RPG,” “offline games”) than branded search. Most non-game apps get a significant share of traffic from brand searches. Games don’t, unless you’re a franchise title.
Video previews matter more for games. Showing gameplay footage can significantly improve conversion. For non-game apps, video is often optional or even counterproductive if the quality isn’t high.
Screenshot orientation is different. Games typically use landscape screenshots that show the actual game environment. Non-game apps almost always use portrait screenshots focused on UI and features.
And competition is fiercer in raw numbers. The gaming categories see more new submissions and faster ranking volatility than most other categories. That means your update cadence may need to be faster, and your creative testing budget should be higher relative to other verticals.
ASO at scale isn’t something you figure out once and forget. It’s keyword strategy across markets, creative testing every month, paid and organic data flowing between teams, and competitive monitoring that doesn’t stop. Smaller apps can follow this guide and build a strong foundation with the right tools and discipline. But for apps managing serious UA budgets across multiple markets, the complexity demands a system – and a team that’s done it before.
