Inside the Audit (08/12): How Store Policies Quietly Kill Visibility
Updated 8 Oct 2026: we removed the prohibited-term counts, because the lists change without a release; replaced the review-delay estimate with what actually happens, a rejected update waiting until a corrected version passes review; and rewrote the severity section to match how the checker grades findings today, by how prominently a finding is shown and what can happen if it stays, never by removing a term.
Apptonomy is an ASO intelligence and execution platform. Paste an App Store or Google Play URL, and the platform runs a full audit across multiple specialized engines, delivering scored findings and prioritized recommendations in minutes. For the full picture of how an audit works, read What You Get From an Apptonomy Audit.
The Orchestrator
The Policy Checker is one of ten specialized engines that run during every Apptonomy audit:
- Keyword Engine
- Store Text Engine
- Screenshot Engine
- Icon Engine
- Sentiment Engine
- Competitor Discovery Engine
- Policy Checker
- AI Discovery Engine
- Intent Engine
- Localization Engine
This post covers the Policy Checker and what happens when it scans your listing for compliance risk.
The ASO Problem: Policy Violations Are the Fastest Way to Lose Rankings You Earned
Most ASO conversations focus on keywords, screenshots, and conversion copy. Policy compliance barely gets a mention until something goes wrong. And when it goes wrong, the consequences are disproportionate: a listing gets rejected, an update gets stuck in review, or worse, a live app gets flagged and pulled from search results while the team scrambles to figure out why installs dropped overnight.
The root cause is usually something mundane. A title that says “Free” when the store prohibits price references in metadata. A description that still claims “#1 fitness app” from a ranking the app held two years ago. A subtitle that mentions a competitor’s trademarked name, either on purpose (to draft off their search traffic) or by accident (a common word that also happens to be a brand name).
Or consider another common scenario: you update your fitness app description, add “#1 workout tracker” because your reviews say you are the best, submit the update, and three days later the update is rejected. No clear indication of which phrase caused it, and your time-sensitive feature launch is stuck in limbo.
These violations are easy to introduce and hard to catch at scale. Apple and Google each maintain separate sets of metadata rules, and those rules differ by field. What is tolerated in a description may get your title rejected. The prohibited term lists vary between platforms. Misleading claims that pass on Google Play may trigger a block on App Store. A term that is perfectly generic in English might be a registered trademark in another locale.
Now multiply by portfolio size. An ASO agency managing 30 client apps across both platforms has 60 sets of metadata to keep compliant. Add localization across 10 locales and the number hits 600 metadata sets. One stale superlative claim in one locale for one client can trigger a review rejection that delays a time-sensitive update. Manual compliance review across that volume is tedious, error-prone, and almost never prioritized until a rejection forces the issue.
A rejected metadata update holds your App Store release until a corrected version passes review. For time-sensitive seasonal promotions or feature launches, that delay costs real installs and revenue.
Our approach: policy compliance should be automatic, field-aware, platform-specific, and running on every piece of store text before it gets anywhere near a submission. Catching a prohibited term in a title before a human even reviews the draft is orders of magnitude cheaper than debugging a rejection after the fact. The Policy Checker runs that compliance layer across every metadata field the platform analyzes.
Want to see if your listing has any of these issues? Run a free Quick Audit at apptonomy.ai.
Under the Hood
The Policy Checker operates as a two-phase compliance scan. Phase one checks your text against known prohibited words and common misleading phrases that the stores flag. Phase two uses an AI model to catch competitor trademark references in context. Every flagged term gets a severity rating based on the combination of platform, field type, and violation category.
The platform’s prohibited term lists and claim patterns are maintained by the Apptonomy team and updated when Apple or Google revise their metadata guidelines, so the compliance layer stays current as store policies change.
Prohibited Term Detection
The problem: Both Apple and Google maintain lists of terms they prohibit in metadata fields. “Free” is prohibited on both platforms. “iPhone” and “iPad” are prohibited on App Store. “Android” and “Google Play” are prohibited on Google Play. These seem obvious, but they appear constantly in real listings, especially in localized versions where a translator adds platform references that were absent in the source language.
How the engine handles it: The Policy Checker keeps a prohibited-term list for each platform. On the App Store it covers price and promotional wording such as “free,” “discount,” and “sale,” and platform references such as “iphone,” “ipad,” and “itunes.” On Google Play it covers price wording such as “free” and “cheap,” and platform references such as “android” and “google play.” The Apptonomy team updates these lists without a release, so the exact terms can change. Generic words such as “app” (and, on the App Store, “new” and “latest”) are not on the list; the checker reports them as keyword-quality notes with no compliance risk.
Each term is matched using case-insensitive word-boundary detection, preventing false positives on partial matches. If the app’s own brand name contains a prohibited term (for example, an app called “FreeStyle”), the engine skips that term rather than flagging the app’s own identity.
What you get: A list of flagged terms, each tagged with the platform, the field, how prominently it is shown, and what can happen if it stays. Every flagged term stays in your text; you decide. On the App Store, price and promotional wording is flagged prominently in the title and subtitle, at standard emphasis in the keyword field (where Apple may edit the keyword itself), and not at all in the description, promo text, or what’s new.
| Term | Platform | Field | Shown as | Risk |
|---|---|---|---|---|
| free | App Store | title | Prominent | Store rejection |
| #1 | App Store | description | Standard | Store rejection |
| App Store | subtitle | Prominent | Rights-holder action |
Misleading Claim Detection
The problem: Superlative and unsubstantiated claims are among the most common reasons for App Store review delays. “#1 photo editor.” “Best app of 2024.” “Guaranteed results.” “World’s fastest.” These claims appear in store descriptions everywhere. Some were true once. Some were never verifiable. Either way, the stores increasingly flag them during review.
How the engine handles it: The engine runs 11 regex-based pattern detectors against the listing text, each calibrated for a specific claim type: “#1” rankings, “best” claims, “guaranteed” promises, “number one” variations, “top rated” assertions, “most popular” claims, “award-winning” references, “world’s best/first/leading/fastest” superlatives, “100% safe/secure/guaranteed/free” absolutes, “unbeatable” claims, and “No. 1” rankings in various formats.
Pattern matching is case-insensitive and handles common formatting variations (hyphenated “top-rated,” spaced “No. 1,” hashtag “#1”). Each detected pattern gets a label so the output clearly identifies what was flagged and why.
What you get: A list of flagged misleading claims with the specific pattern that matched, the field location, and how prominently it is shown. On the App Store, claims in titles, subtitles, and the keyword field are shown prominently; in descriptions, promo text, and what’s new they are shown at standard emphasis. On Google Play they are shown at standard emphasis in every field. The risk named is a store rejection.
AI-Powered Competitor Brand Detection
The problem: Competitor trademark usage in store metadata is a policy violation on both platforms, and the most difficult category to catch with static rules. The challenge is context. “Swift” could be an adjective meaning fast, or a reference to Apple’s programming language. “Spark” could describe energy, or reference Spark Mail. A static word list produces too many false positives to be useful, and missing an actual trademark reference creates real legal and compliance risk.
How the engine handles it: The engine sends each text field to a language model with a structured prompt that asks for competitor brand identification. The prompt provides the app’s own brand name (so the model knows what to exclude), the content locale (so the model applies locale-appropriate brand knowledge), and explicit instructions to distinguish between generic word usage and actual brand references. The model returns each detected brand along with the company that owns it.
This approach handles multilingual content natively. The language model knows that “WhatsApp” is always a brand, “spark” is usually generic, and “Photoshop” is an Adobe trademark regardless of the language the listing is written in. It also handles compound references and indirect brand mentions that static rules would miss entirely. The engine reports what it finds for your review; it never removes a term or decides for you.
What you get: A list of flagged competitor brands with the exact term as it appears in the listing and the brand owner. On the App Store, competitor brands in titles, subtitles, and the keyword field are shown prominently; in descriptions and promo text they are shown at standard emphasis. On Google Play they are shown at standard emphasis in every field. The risk named is action from the rights holder, not a store rejection.
Severity Grading and Field-Aware Enforcement
The problem: Not all violations carry the same risk. A prohibited term in your description might sail through review. The same term in your title will get rejected. Treating every flag with equal urgency leads to alert fatigue; treating them all as low priority leads to missed rejections.
How the engine handles it: The Policy Checker grades each finding on two things: how prominently it is shown (prominent, standard, or informational) and what can happen if the term stays (store rejection, Apple editing your keyword field, action from the rights holder, or nothing). App Store titles and subtitles get the most prominent treatment. Descriptions, promo text, and what’s new get standard treatment. Google Play uses standard treatment in every field. Generic words such as “app” (and, on the App Store, “new”) are informational keyword-quality notes with no compliance consequence. No grade removes a term: the checker reports, and you choose.
Bringing It Together
Policy compliance is the unglamorous side of ASO. Nobody builds a growth strategy around it. But a single prohibited term or stale superlative claim can delay an update, trigger a review rejection, or pull a listing from search results without warning. The damage is asymmetric: months of keyword optimization and creative testing can be undone by a compliance flag that takes five minutes to fix but weeks to diagnose.
Most teams rely on manual compliance checks before submission, or catch violations only after a rejection forces a scramble. Running policy checks automatically on every audit makes compliance proactive rather than reactive.
The Policy Checker catches these issues before they reach the stores. Its two-phase scan covers the three most common violation categories (prohibited terms, misleading claims, competitor trademarks), applies platform-specific and field-specific severity grading, and handles the brand detection problem that static rule systems cannot solve. The Policy Checker runs on existing live listings during an audit and on newly generated metadata during content creation, both trigger points covered, no manual step required. For an app localized into 20 markets on both platforms, that means 40 metadata sets checked automatically on every update cycle. Compliance violations surface at the same time as keyword gaps and creative recommendations, not as a surprise rejection three days after submission.
The combination of platform-specific rule sets, pattern-based claim detection, AI-powered contextual brand identification, and a severity matrix calibrated to actual store enforcement behavior is the kind of layered compliance system that would take significant effort to build and maintain internally. Here, it runs automatically on every audit.
Run a free Quick Audit now Paste your App Store or Google Play URL at apptonomy.ai and see what the Policy Checker finds.