App Keyword Research Store Queries the App Can Honestly

App keyword research is store-query inventory: terms people type in App Store and Play search, gathered from store autocomplete, competitor listings, and review language as evidence, then passed through an honesty gate. The deliverable is a list plus refusals. It is not field placement and not a title formula.

Web SEO research answers “what do people type on Google.” Store research answers “what do people type in the store search box.” Pasting SEO or Search Console queries into an app title is the failure mode this page exists to stop. Volumes are not interchangeable indexes.

The ASO hub says store demand is in the program. This child builds the list. Metadata decides where a surviving term lives. Title and description children write those fields. Apple Connect packs the hidden field. Play indexes description. None of those jobs are this H1.

Tools can assist. The object remains queries the binary can match.

Competitor brand terms and trademark risk are honesty and policy, not a growth hack. We will not recommend stuffing another company’s name because an autocomplete suggested it.

Store autocomplete is evidence of how the store completes a seed, not a ranking report. We capture the language; we do not treat the dropdown as a promised position.

Competitor listings (titles, subtitles, visible descriptions) are a gap list: jobs they claim that you also ship, jobs they claim that you do not. The second column is a refusal, not a stuffing target. Reading listings is research. Rewriting your title is the title child.

It is not ratings operations. Prompt timing, public replies, and bought-review refusals live on app ratings and reviews. This page only mines phrasing.

Category browse language and editorial featuring copy, when used, are weak signals. They do not become a “get featured” product.

If the app cannot complete the job a query implies, the term is refused even when autocomplete is loud. Stuffing a job you do not ship is a listing-policy and trust failure, not a metadata trick.

Ambiguous queries (the user might mean a different category of app) are marked as risky. Metadata may still choose a cautious visible phrase; research must label the risk instead of hiding it in a packed field.

Seasonal or newsy queries are dated. The list notes when a term is event-bound so allocation does not treat it as a permanent title occupant. We will not print a timeline SKU for “when ranks move.”

Paid search query reports from PPC can suggest language. They are not store volume.

Each shipped storefront gets its own query set. Translating an English list word-for-word is not research. Native autocomplete and native review phrasing are the sources again.

Both-store ASO owns which locales enter the dual-console calendar. This page only produces sets for locales already on that ship list (or explicitly in research scope).

Script and diacritic variants are locale facts. They are not extra keyword-field packing tricks on this H1.

The artifact is a list: query, locale, source, honesty pass/fail, notes. Refusals stay visible so metadata cannot quietly revive a failed term. That is the whole product of this page.

We will not output “put these five in the title and these in the keyword field.” That sentence is app metadata optimization. We will not output “Brand + job + modifier in 30 characters.” That sentence is app title optimization.

Apple packing and Play description indexing consume the list; they do not author it. If research is asked to deliver a comma-separated 100-character string, the request is routed to the Apple child after allocation exists.

No invented ranks, CPI, conversion percentages, or organic-mix targets attached to a query row. A row is language plus honesty, not a forecast.

Seed terms come from product jobs and support tickets, then are tested in store search boxes - not in GSC.

Misspellings that stores still complete are logged as store language, not as web-typo research.

Competitor gap rows that fail honesty are kept as refusals so sales cannot reopen them as “just metadata.”

Review n-grams that describe crashes or pricing are product evidence, not install-query candidates.

We will not score “difficulty” as a productized 0 - 100 SKU. Competition notes stay qualitative unless a tool’s number is labeled as that tool’s number.

Category-only queries with no job match are refused even if the category is where you sit.

Locale sets are versioned with the ship list.

Play search-term reports, where visible, are Play evidence feeding this list - they are not Play Console configuration.

Connect search reports, where visible, are Apple evidence feeding this list - they are not keyword-field packing.

Synonym clusters are research structure. Which synonym occupies title is allocation.

We will not clone a web keyword spreadsheet tab and rename it ASO.

If the binary changes jobs, the list is re-gated. Metadata is not allowed to keep orphan terms.

Trademark counsel can veto a row. Research records the veto; it does not hide it.

The hub may say “store demand is first.” This page is the demand file, not the program map.

Store query inventory is the object of this page: which App Store and Play queries the shipped binary can honestly match, by locale, including competitor gap lists. It is not field placement. It is not title formulas.

They are not proof you should occupy a query. Honesty is the filter. ASO already decided store-search is in the program.

Phrases people use in reviews can seed queries. Prompt APIs and public replies stay on app ratings and reviews.

Unstaffed locales do not get a fake query list. App store optimization may keep those locales off the dual-console calendar.

Placement is app metadata optimization. Research produces the list. Allocation decides title versus subtitle versus hidden field versus Play descriptions. Mixing those jobs is how Wave 8 uniqueness dies.

App title optimization writes the brand-plus-job string people see in search chrome. Composing the visible name is not research and stays off this H1.

App description optimization writes listing copy objects as readable prose. It does not draft those objects or treat character limits as a research product.

Google Play optimization runs Play-native tests on default and custom listings. A query list can inform what a listing claims on Android. The experiment UI stays on Play.

Apple App Store optimization implements the hidden box. Filling it is Connect work. Listing the queries is research.

Creatives may depict a job a researched query named, but drawing is not discovery. Screenshot optimization owns frames, preview, and icon as assets.

Conversion tests measure whether a listing that uses researched terms earns installs after a product-page view. App conversion optimization owns that protocol and the freeze rules.

SEO owns documents people find on the open web. Store autocomplete is a different surface.

Search-term reports in ads live on frozen PPC when that is the paid job. Mobile app growth sequences paid UA against store-organic. This page does not run Apple Search Ads or Play ads as research theater.

Gap lists in store query inventory name queries competitors occupy that you will not match because the binary cannot. That refusal is a research deliverable. We will not invent a count of terms as a SKU, a quarterly calendar product, or a rank you do not have.

The deliverable is a locale-scoped list of queries the app can honestly serve, with competitor gaps named as refusals, and every placement, copy, and experiment job left on its sibling door.