crawlora-net/search-vs-store

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?utmsource=huggingface&utmmedium=referral&utm_campaign=search-vs-store>
• —Dataset page: <https://crawlora.net/datasets/search-vs-store?utmsource=huggingface&utmmedium=referral&utm_campaign=search-vs-store>
• —Living index (updated panel + lead/lag test): <https://crawlora.net/search-vs-store?utmsource=huggingface&utmmedium=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 - bestappstore_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

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.