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Search demand vs Apple's search popularity

This dated study compares search demand signal breadth with Apple popularity for 1,000 popular terms in five storefronts. The recorded rank correlation is 0.30. Read the sample and different observation periods with the result; it does not turn suggestions into exact search counts.

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By Updated: Data: 2026-08-09 – 2026-09-14 · archive · Last successful measurement: 2026-09-143 min read

Keyword tracking · rank history of the tracked keywords

The finding

StorefrontnSpearman ρ95% CINo suggestionsAt the cap (≥10)
United States (US)2000.160.02 … 0.290%83.5%
United Kingdom (GB)2000.270.14 … 0.391.5%64.5%
Germany (DE)2000.300.17 … 0.420%62%
Spain (ES)2000.360.23 … 0.481.5%56%
Turkey (TR)2000.250.12 … 0.360%65.5%
All10000.300.25 … 0.360.6%66.3%

Across the five recorded storefronts, Spearman rank correlation between Apple popularity (1–100) and search demand signal breadth is ρ = 0.30 (n = 1,000; 95% bootstrap interval 0.25 … 0.36). The recorded storefront coefficients range from 0.16 to 0.36. The interval stays above zero under the stated resampling calculation. These selected popular terms and different observation periods do not establish the relationship for all App Store searches or identify a cause.

0.6% of these popular terms show no search demand signal at all.

Limits of this comparison

Share of popular terms at the top of the demand scale, by storefront
US83.5% · n=200
GB64.5% · n=200
DE62% · n=200
ES56% · n=200
TR65.5% · n=200
The demand signal has a ceiling. 66.3% of the terms in this sample reached it — they are tied, and a rank correlation cannot order ties.
  • Ceiling. The recorded suggestion counts stop at 10. The 66.3% of terms at that cap cannot be ordered by suggestion count alone; the count is not a search-volume reading.
  • Restricted range. Recorded popularity runs from 56 to 100, with a median of 74. The study contains selected popular terms. It does not establish how the measures relate below that range or explain the observed coefficient.
  • Different periods. Popularity belongs to the week dated 2026-08-09; suggestions were observed on 2026-09-14. Those start dates are 36 days apart. This is not a comparison of measurements from the same day.
This sample compares two observations. It does not establish the demand for terms without a signal. Keep the recorded countries, dates and restricted sample with the finding.

Keep historical coverage separate from this sample

In a 2025 coverage report, aso.dev counted a fall from 165,875 to 39,254 US terms above the historical score of 5 (−77.4%). Its summary gives 29 September–3 October 2025, while its table extends to 7 October. Those are the vendor’s reported coverage counts, not a Storelift measurement or a definition of today’s API. Apple’s current Insights documentation describes a 1–100 popularity field and weekly or monthly reporting periods.

The lowest recorded popularity in this 1,000-term sample is 56. No term is at or below 5, so excluding such values leaves ρ = 0.30 unchanged. The file therefore provides no comparison of terms at that historical floor.

This corpus contains published popular terms, with a minimum observed popularity of 56. It does not represent the full long tail. A term outside this sample cannot be assigned zero demand from this study.

Method and data

  • Popularity: Apple's search-term popularity, week 2026-08-09.
  • Search demand observation: the search demand signal for the exact term, recorded on 2026-09-14. There were 0 failed checks among the 1,000; a failed check would be excluded rather than counted as zero.
  • Statistic: Spearman rank correlation with average ranks for ties; percentile bootstrap, 2,000 resamples, fixed seed — the interval is reproducible.
  • Recorded storefronts: US, GB, DE, ES and TR. The study describes these five samples and their recorded periods; it does not establish current availability in another country.
Raw data: demand-vs-popularity-2026-09-14.csv — 1,000 rows with storefront, term, genre, popularity, in-genre rank, suggestion count and whether the term appears in its own suggestions. The terms are a fixed sample of popular searches, not any user's keywords.

Frequently asked

So is the search demand signal a bad one?

Suggestions can help discover candidates. This sample compares suggestion counts with popularity among published popular terms; it does not prove exact search volume or that an omitted term has no demand.

Why not just use Apple's popularity value?

Read popularity with its metric name, country and reporting period. A relative popularity value helps compare terms in its recorded scope. Availability can differ by term and period, and a missing value does not mean zero searches. The search demand signal answers a different question.

Can I re-run this?

Yes. The CSV on this page has every row, and the correlation can be recomputed from it with any statistics tool.

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Competitor sets · the terms you share with one competitor and the ones only it has

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