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First Class Media B.V.

Entertainment

Team website
Website
18Apps
4.1Avg Rating
317.9KTotal Ratings
2Categories
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)
Product page (App Store)

Developer portfolio analysis

Automatically summarizes public App Store metadata such as categories, rating volume, star ratings, release dates, and pricing for competitor research and product planning. The DeepSeek section is generated on demand when the API is configured; otherwise only rule-based analysis is shown.

DeepSeek AI analysis

Runs on demand so the initial page load stays fast.

Review the rule-based cards first, then generate the AI readout. It usually takes 10-40 seconds.

Product direction

Category and portfolio mix

Entertainment is the main category at about 94.4% of apps, while the portfolio spans 2 categories. This is a highly concentrated portfolio strategy.

Entertainment
17 apps · 94.4%
Games
1 apps · 5.6%
  • The primary category Entertainment accounts for 94.4%. The category structure is relatively narrow.
  • The diversification score is about 0.10 (0=highly concentrated, 1=highly diversified), useful for comparing developers.

Portfolio data

Scale and monetization structure

The full portfolio has about 317.9K public ratings, a mid-sized public review base. It includes 18 free apps and 0 paid apps.

Apps
18
Total ratings
317.9K
Free / paid
18 / 0
  • By review volume, the leading apps include: "Call Santa Claus! create video", "Ghost Camera Detector Radar", "Spirit Board (very scary)".
  • The visible list is free or priced at 0. Monetization may rely on subscriptions, ads, or in-app purchases; verify on the store page.

Ratings and reputation

Weighted average and head effects

Weighted by rating volume, the portfolio averages about 4.55 stars; the simple average is 4.15 stars. The gap shows whether high-volume apps lift or drag the overall reputation.

Weighted avg
4.55 ★
Simple avg
4.15 ★
  • The weighted and simple averages differ meaningfully, indicating that high-review apps strongly shape perception.
  • 0 apps have no public rating data, so long-tail performance is underrepresented.

Reputation risk proxy

Low ratings and thin samples (API has no review text)

The iTunes Search API does not provide review text. This card uses low star ratings with enough review volume as a proxy, and separately highlights apps with thin samples.

  • Low-rating watchlist (at least 80 ratings and below 4.0 stars): "Ugly Test" 2.5★; "Naughty or Nice Scan" 3.8★; "Naughty or Nice finger scanner" 3.8★. Use App Store Connect exports or third-party data for semantic review analysis.
  • Thin rating samples (under 50 ratings): "Night Panic: Scary AR Horror", "Bellen met Sinterklaas!". Star ratings can swing heavily until more feedback arrives.

Release and lifecycle

Listing age span

The earliest listed app is "Mars Quest : 8-bit", while the newest or most recent listing signal is "Night Panic: Scary AR Horror". The span is a proxy for long-term iteration capacity.

Span
14 years+
  • Earliest record in this dataset: 2012 (Mars Quest : 8-bit).
  • Newer app signal: 2026 (Night Panic: Scary AR Horror).

Reference points for builders

Reusable research angles

These takeaways are based on public metadata and are useful for competitor research or portfolio planning. Deeper conclusions require downloads, revenue, and review text.

  • Study the category mix: 2 categories show whether a main-category plus extension structure matches your resources.
  • Compare reputation structure: weighted 4.55 stars vs. the leading app "Call Santa Claus! create video" and its review volume.
  • For similar tools, prioritize review mining on lower-rated apps to find functional gaps; this requires store reviews or third-party data.

Rule-based cards use iTunes Search API data. DeepSeek generates its readout from the same public data and should be treated as directional research.

Team / product news

Runs on demand and avoids external calls by default.

Aggregates news about the team and core products, up to 20 items.