Datasets:

crawlora-net
/

search-vs-store

license: cc-by-4.0
pretty_name: >-
  Search vs. Store: AI App Search Demand vs App Store Rank vs Web Traffic (US,
  2026)
tags:
  - ai-apps
  - google-trends
  - app-store
  - similarweb
  - search-demand
configs:
  - config_name: default
    data_files:
      - split: snapshot_2026_07_26
        path: data/search-vs-store-ai-apps-2026-07-26.csv
      - split: snapshot_2026_07_01
        path: data/search-vs-store-ai-apps-2026-07-01.csv

Search vs. Store — AI app search demand vs App Store rank vs web traffic (US, 2026)

One row per major AI app joining three independent popularity signals. Two cross-sectional US snapshots (2026-07-01 and 2026-07-26), 40 curated AI apps each. Aggregate/derived only — no raw records, no PII. License: CC BY 4.0.

Study + methodology: https://crawlora.net/blog/search-vs-store-2026?utm_source=huggingface&utm_medium=referral&utm_campaign=search-vs-store
Dataset page: https://crawlora.net/datasets/search-vs-store?utm_source=huggingface&utm_medium=referral&utm_campaign=search-vs-store
Living index (updated panel + lead/lag test): https://crawlora.net/search-vs-store?utm_source=huggingface&utm_medium=referral&utm_campaign=search-vs-store
Source repository: https://github.com/Crawlora-org/search-vs-store-data

Why it's interesting

Rank the 40 apps by web visits and you get roughly the search order (ChatGPT, Gemini, Canva, Claude, DeepSeek…), not the App Store order. Search demand and web traffic agree; the App Store rank is the outlier, scrambled by distribution deals and platform push. The dataset makes that divergence explicit in the divergence column.

Columns

app — display name.
trends_term — the exact query measured on Google Trends (hand-picked to disambiguate brand names, e.g. "Claude AI", "Google Gemini").
search_tier — coarse Google search-demand tier: Dominant > High > Moderate > Low > Minimal. Coarse on purpose: ChatGPT dwarfs the field ~30–1000x, so the sub-Minimal tail is below Google Trends' resolution and is never individually ranked.
trends_interest_chatgpt100 — trailing-12-month mean Google Trends interest, anchor-normalized so ChatGPT = 100. Directional, not precise for the small tail.
best_app_store_rank — best current US chart position across iOS/Play, Top Free + Grossing (null = off the top-100). ios_rank / android_rank — per-store best.
chart_score — 101 - best_app_store_rank (higher = better standing; null if off-chart).
ratings_count — combined iOS+Play ratings count (install proxy), where matched.
sw_monthly_visits — SimilarWeb estimated monthly web visits (absolute; blank = below SimilarWeb's floor). sw_global_rank — SimilarWeb global site rank.
divergence — downloads_not_search | search_not_downloads | aligned (tier gap between store standing and search demand).

Files

File	What
`data/search-vs-store-ai-apps-2026-07-26.csv`	Snapshot 2026-07-26, one row per app
`data/search-vs-store-ai-apps-2026-07-26.json`	Same rows + metadata + the search x store tier matrix
`data/search-vs-store-ai-apps-2026-07-01.csv`	Snapshot 2026-07-01, one row per app
`data/search-vs-store-ai-apps-2026-07-01.json`	Same rows + metadata + tier matrix

The .json files are a metadata wrapper (method, columns, tier_matrix, data[]), so the CSVs are the flat, directly-loadable tables.

Caveats worth reading before you cite this

Do not compute a cross-app search correlation from trends_interest_chatgpt100. Google Trends rescales 0-100 against the largest term in each request, and ChatGPT's volume collapses the tail into noise. The tiers and the divergence label are robust; a correlation coefficient over these values is an artifact. See the study for the full writeup.
sw_monthly_visits / sw_global_rank are joined from the nearest available panel date when the daily SimilarWeb panel had no row for the snapshot date (see similarweb_as_of in the JSON metadata) — a few days off from the rest of the row.
App Store chart history behind these snapshots is only ~2-3 weeks deep upstream; these are point-in-time snapshots, not a long rank history.

Citation

Crawlora (2026). Search vs. Store: AI App Search Demand vs App Store Rank vs Web Traffic (US, 2026). CC BY 4.0. https://doi.org/10.5281/zenodo.21987676

Collected with Crawlora.