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Market/Categories/Research & Analysis/Supascout

Independent product intelligence · Worldwide · 2026-05-01 → 2026-07-31

Supascoutsupascout.cc

Supascout is an AI-powered tool that analyzes app store reviews to help developers and businesses understand user needs and frustrations through actionable insights.

Research & Analysissupascout.ccTracked since 2026-08-211 discovery source
1KBacklinks
79Referring domains
17Domain authority
+3.0%Referring domains · 90 days

Authority & distribution

How strong is Supascout’s web footprint?

Backlinks measure accumulated distribution; referring domains show how broadly that authority is spread. Both are observable on the open web, so both are published for every product here.

Backlinks

1KCurrent snapshot

Referring domains

79Current snapshot

Authority

17Domain authority · category median 21

90-day move

+3.0%Referring domains, last 90 days
At 17, Supascout sits below the 21 median for its category.

Trajectory · Pro

The counts above are the latest of 370 daily observations. The curve through them is on the full profile.

See the trajectoryOpen the full profile

AI referral visibility

Which AI assistants are already sending Supascout traffic?

Measured · Pro

Measured for this product: AI referral traffic.

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Product profile

What Supascout is selling.

Traffic makes more sense when you can connect it to the actual product promise.

“Supascout”

Supascout is an AI-powered tool that analyzes app store reviews to help developers and businesses understand user needs and frustrations through actionable insights.

Research & AnalysisSeen 2026-08-21 → 2026-08-21Coverage tier C

What we have measured

4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.

Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.

Canonical website: https://www.supascout.cc

Observed across 1 discovery source.

Research questions

What the evidence answers about Supascout.

Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.

What problem does Supascout solve for app developers and businesses?

Supascout helps developers and businesses understand user needs and frustrations from app store reviews so they can address them with confidence. It uses AI to analyze reviews and provide actionable insights.

What does Supascout offer or include in terms of features?

Supascout offers features for sentiment analysis, review analysis, and generating actionable insights from app store reviews. It is an AI-powered tool focused on feedback analytics and market research for mobile apps.

What is Supascout used for, and in what situations?

Supascout is used to analyze app store reviews. It is employed when developers or businesses need to gain actionable insights from user feedback, such as sentiment analysis and understanding user frustrations.

Who is Supascout designed for?

Supascout is designed for developers and businesses who manage mobile apps. It targets professionals who need to analyze user feedback from app stores.

4 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.

Behind the lock

Unlock full Supascout intelligence.

  • Backlink trajectoryThe figures above are the latest observation. The daily curve behind them holds 370.
  • AI referral detailHow much traffic assistants send.
  • Search structure and ranking depthOrganic against paid, brand against non-brand, and how many keywords rank in the top three against the long tail — which is what decides whether the demand is portable or tied to the name.
  • The rest of the marketPage through every leaderboard 100 rows at a time, put any five products side by side, and query the whole dataset over API / MCP.

Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured