---
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](https://crawlora.net?utm_source=huggingface&utm_medium=referral&utm_campaign=search-vs-store).