The Problem With App Store Search Data
Apple doesn't give you raw search counts. Google doesn't either, at least not for the Play Store. Unlike web SEO where tools like Google Keyword Planner show you "10K-100K monthly searches," app store keyword research is built on relative scores and educated guesses.
For the iOS App Store, the primary metric is Apple's Search Popularity (SP) score — a number from 5 to 100 that represents relative search volume. But what does an SP of 45 actually mean in terms of real searches? How many people are actually typing that keyword into the App Store every day?
This is the question that matters for making practical ASO decisions, and it's answerable.
The SplitMetrics Formula
The most rigorous public research on this comes from SplitMetrics, who analyzed data from Apple Search Ads campaigns across thousands of keywords. They found that the relationship between SP and actual search impressions follows an exponential curve:
Max Daily Impressions = 254.4443 exp(0.0615 SP)
This formula estimates the maximum daily impressions a keyword generates — meaning the total number of times apps could appear in search results for that keyword per day. Your actual impressions will be a fraction of this based on your ranking position.
The exponential nature is critical: each 10-point increase in SP roughly doubles the search volume. An SP 60 keyword doesn't get 50% more searches than an SP 40 keyword — it gets roughly 3.5x more.
Breaking Down the Math
The formula has two components worth understanding. The constant 254.4443 is the baseline — it means a hypothetical keyword with SP 0 would generate roughly 254 daily impressions. The exponent 0.0615 SP is the growth rate. Multiply 0.0615 by any SP score and raise e* to that power, then multiply by 254.4 to get estimated max daily impressions.
Why does this matter? Because it tells you the rate of the exponential curve. A growth factor of 0.0615 means each single point of SP increases volume by about 6.3%. Over 10 points, that compounds: e^(0.0615 10) = 1.85, which is the "roughly doubles" rule of thumb. Over 20 points, you get e^(0.0615 20) = 3.42 — a 3.4x increase. This compounding is why the difference between SP 50 and SP 70 (a 20-point gap) represents a jump from ~5,600 to ~18,000 daily impressions.
It's also worth noting that the formula estimates maximum daily impressions — the total search demand for the keyword, not what any single app receives. Position matters enormously. Data from Search Ads campaigns suggests the #1 organic result captures roughly 35-45% of taps, #2 gets 15-20%, and by position #5 you're down to 3-5%. So if a keyword has 5,000 daily impressions (SP ~50) and you rank #3, you might see 400-600 of those impressions translate into actual profile views.
SP Score Lag and Seasonality
One subtlety that catches people off guard: SP scores are a 7-day moving average, which means they lag real-time search behavior by about 4 days. If a keyword spikes due to a news event or seasonal trend, the SP score won't fully reflect that spike until nearly a week later. By the time you see it, the surge may already be fading.
Seasonality is real in app search. Tax-related keywords surge in January through April. Fitness keywords peak in January and fade by March. Back-to-school apps spike in August. Holiday-themed keywords can jump 10-20 SP points during their peak season. When you see a keyword with SP 55 in January, it might sit at SP 38 for the other 11 months. Always check whether a keyword's current SP is its baseline or a seasonal peak before building your strategy around it.
The Complete Reference Table
Here's the full breakdown across the SP spectrum, with estimated daily impressions, monthly volume (daily x 30), and practical volume labels:
| SP Score | Daily Impressions | Monthly Volume | Label |
|---|---|---|---|
| 16 | ~400 | ~12,000 | Very Low |
| 18 | ~450 | ~13,500 | Very Low |
| 20 | ~870 | ~26,000 | Very Low |
| 22 | ~1,000 | ~30,000 | Low |
| 25 | ~1,200 | ~36,000 | Low |
| 28 | ~1,400 | ~42,000 | Low |
| 30 | ~1,600 | ~48,000 | Low |
| 32 | ~1,900 | ~57,000 | Low-Medium |
| 35 | ~2,300 | ~69,000 | Medium |
| 38 | ~2,700 | ~81,000 | Medium |
| 40 | ~2,800 | ~84,000 | Medium |
| 42 | ~3,400 | ~102,000 | Medium |
| 45 | ~4,100 | ~123,000 | Medium |
| 48 | ~4,800 | ~144,000 | Medium-High |
| 50 | ~5,600 | ~168,000 | Medium-High |
| 55 | ~7,600 | ~228,000 | High |
| 60 | ~10,000 | ~300,000 | High |
| 65 | ~13,600 | ~408,000 | High |
| 70 | ~18,000 | ~540,000 | Very High |
| 75 | ~25,000 | ~750,000 | Very High |
| 80 | ~33,000 | ~990,000 | Very High |
| 85 | ~60,000 | ~1,800,000 | Ultra High |
| 90 | ~80,000 | ~2,400,000 | Ultra High |
| 95 | ~110,000 | ~3,300,000 | Ultra High |
| 100 | ~200,000+ | ~6,000,000+ | Ultra High |
These numbers are for the US App Store. Other markets are proportionally smaller — the UK is roughly 25-30% of US volume, Germany about 15-20%, and so on.
What the Volume Labels Mean in Practice
Very Low (SP 16-20, <1,000/day): These keywords exist but barely register. Even ranking #1 would only bring a handful of downloads daily. Worth including in metadata if highly relevant, but don't optimize specifically for them.
Low (SP 21-34, ~1,000-2,000/day): The long tail. Individually they won't move the needle, but collectively a dozen low-volume keywords in your metadata can compound into meaningful traffic. Many of these are now invisible in Apple's API since the October 2025 cutoff at SP 35.
Medium (SP 35-49, ~2,300-5,000/day): The goldmine for indie apps. These keywords have enough volume to drive real downloads (ranking in the top 3 for an SP 45 keyword could mean 50-200 downloads/day) but typically aren't dominated by massive incumbents. This is where smart ASO pays off the most.
High (SP 50-69, ~5,600-16,000/day): Competitive territory. You'll need strong ratings, good download velocity, and likely a well-optimized title to rank here. Achievable for established indie apps with 4.5+ ratings and thousands of reviews.
Very High (SP 70-85, ~18,000-60,000/day): Dominated by top apps. Think "photo editor," "weather app," "music player." Only realistic if your app is already a category leader or you're targeting a very specific variant.
Ultra High (SP 86-100, ~70,000-200,000+/day): Brand keywords and category-defining terms. "Instagram," "TikTok," "Spotify." Not viable targets for ASO unless it's your own brand name.
iOS vs. Android: Two Different Volume Estimation Problems
On iOS, you have the SP score and the SplitMetrics formula. It's imperfect but grounded in real campaign data. On Android, the situation is fundamentally different — and in some ways better, in some ways worse.
Google Play has no equivalent of Search Popularity. There's no official relative score for keyword search volume within the Play Store. Instead, Android ASO relies on two indirect sources:
Google Ads Keyword Planner provides monthly search volume ranges for web searches on Google. "QR code scanner" might show 1M-10M monthly web searches. This correlates loosely with Play Store searches — people searching for something on Google often also search the Play Store — but the mapping is noisy. A keyword with high web volume might have low Play Store volume if it's not an "app-intent" query (nobody searches "what time is it" in the Play Store).
Autocomplete and search suggest on Google Play work similarly to iOS. You can scrape autocomplete results to gauge which keywords Google considers popular enough to suggest. But unlike iOS, Google Play doesn't return priority scores — you just get a list of suggestions in ranked order.
Where Android has a genuine advantage is in install data. Google Play exposes minInstalls and maxInstalls for every app. This means you can look at the top 10 results for a keyword, see their install counts, and directly estimate the market size. If the top 3 apps for "habit tracker" on Google Play have 10M, 5M, and 1M installs respectively, you know there's real demand. On iOS, you have to infer this from review counts (which are a very rough proxy — roughly 1 review per 50-200 downloads depending on the category).
For developers targeting both stores, the practical approach is: use iOS SP scores as your primary volume signal (it's the most calibrated data available), then cross-reference with Google Ads web volume and Play Store install counts to validate demand on Android.
The Sweet Spot: SP 35-55, Difficulty Under 40
After analyzing thousands of keyword-app combinations, a clear pattern emerges for indie app success:
Target keywords where SP is between 35 and 55 and keyword difficulty is below 40. See real examples like gif maker or body measurement tracker on our difficulty pages. These keywords have enough volume to meaningfully impact downloads (2,300-7,600 daily impressions) while being achievable for apps without massive install bases.
A keyword with SP 45 and difficulty 30 is worth far more to an indie developer than a keyword with SP 75 and difficulty 80. You'll never rank for the latter, but you can realistically reach the top 5 for the former with good metadata optimization and decent ratings.
Worked Example: Estimating Downloads From SP
Say you're building a sleep tracking app and you've identified the keyword "sleep sounds" with SP 48. Here's how to turn that into a download estimate:
- Calculate max daily impressions:
254.4443 exp(0.0615 48)= ~4,800 daily impressions. - Estimate your share based on target rank. If you're aiming for position #3 (realistic for a well-optimized indie app in a medium-difficulty keyword), expect roughly 8-12% of impressions, so ~400-575 impressions reaching your listing.
- Apply a tap-through rate. Not everyone who sees your result will tap it. With a good icon and title, expect 30-50% tap-through, giving you ~120-290 product page views per day.
- Apply a conversion rate. A well-designed product page converts at 25-40% for relevant searches. That's ~30-115 downloads per day from this single keyword.
Even at the conservative end, 30 daily downloads from one keyword is meaningful for an indie app. Stack 5-8 keywords like this through good metadata coverage and you're looking at 150-500 organic downloads daily — enough to sustain a real business.
This is why the SP-to-impressions formula matters. It turns an abstract score into a concrete download forecast you can use to prioritize keywords, estimate revenue, and decide whether a market is worth entering.
When SP Data Isn't Available
Since Apple's October 2025 change, keywords with SP below 35 no longer return popularity data through the Search Ads API. For these terms, you need alternative signals.
Autocomplete priority is the best free proxy. When you search in the App Store, Apple returns suggestions with priority scores from 0 to 10,000. Higher priority correlates with higher search volume:
| Autocomplete Priority | Approximate SP Equivalent |
|---|---|
| 0-2,000 | SP < 20 |
| 2,000-4,000 | SP 20-30 |
| 4,000-6,000 | SP 30-40 |
| 6,000-8,000 | SP 40-55 |
| 8,000-10,000 | SP 55+ |
This isn't a precise mapping — autocomplete priority is influenced by factors beyond pure search volume, including trending behavior and editorial curation. But for keywords below the SP 35 cutoff, it's the most accessible signal available.
Using This Data for Keyword Selection
Here's a practical framework for choosing keywords:
- Start with relevance. No amount of volume matters if the keyword doesn't match what your app does. Users who search "meditation timer" and find a workout app will not download it.
- Check volume viability. Use the SP-to-impressions table above. If the keyword is below SP 30 (~1,600 daily impressions), it needs to be extremely relevant or part of a larger long-tail strategy.
- Assess competition. Competitor keyword analysis helps here. Look at the top 10 apps ranking for the keyword. If they all have 100,000+ reviews and 4.7+ ratings, you're unlikely to break in regardless of volume.
- Calculate opportunity. The ideal keyword has medium volume (SP 35-55), low-to-medium difficulty (<40), and high relevance to your app. These are the keywords worth putting in your title and subtitle.
- Fill remaining metadata slots with long-tail variants. Once your title and subtitle target your primary keywords, use the keyword field for lower-volume variants and related terms.
The goal isn't to find one perfect keyword. It's to build a metadata strategy where your title targets 2-3 medium-volume opportunities, your subtitle reinforces those while adding 2-3 more, and your keyword field covers 15-20 long-tail variations. That's how you maximize organic search coverage on the App Store.