個性あふれるアイドルをプロデュース
Ensemble-stars (ensemble-stars.jp) scores 60 out of 100 on Brand Analyzer, placing it in the Developing tier among Media & Entertainment brands, SaaS brands, in the 6th percentile of 34987 Media & Entertainment brands.
Ensemble Stars is a Japanese mobile idol-production video game originally released in 2015. Players manage a roster of distinct idol characters across two complementary apps, 'Basic' and 'Music', each offering a different play style. The game combines narrative-driven idol management with rhythm-game mechanics, appealing to fans of character-driven idol content. It is categorized within Media & Entertainment and SaaS, targeting mobile gamers from teenagers to adults who enjoy idol and character-growth games.
| Brand Name | Ensemble-stars |
|---|---|
| Domain | ensemble-stars.jp |
| Industry | Media & Entertainment, SaaS |
| Founded | 2015 |
| Main Competitors | IDOLiSH7 (76/100), A3!, Love Live! School Idol Festival, Uta no Prince-sama: Shining Live (57/100), B-PROJECT |
Moz Domain Authority 50/100 vs category average 50 / leader 100 - a strong backlink profile, so AI systems frequently encounter mentions of the brand.
No Wikipedia presence was found - a major gap, since Wikipedia is among the most heavily weighted sources in AI training data.
The homepage meta description reads "スマホで個性あふれるアイドルたちをプロデュース！ お好みのプレイスタイルによって『Basic』と『Music』の2つのアプリからお楽しみいただけます。" (Meta description too short (2 words). OG description present but brief) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: og:title, og:description, og:image, og:type, Twitter cards. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: robots.txt present, no AI bot restrictions - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Content is JA-only, limiting citation in English-language AI answers.
Detected tech stack: Vue.
Social footprint: verified profiles on X (Twitter), YouTube, Instagram, Facebook, GitHub; no detected presence on LinkedIn - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked October 2026.
When asked "What is Ensemble-stars?", Claude could identify the brand as of October 2026. Ensemble Stars! is a Japanese mobile rhythm/idol-raising game developed by Happy Elements, featuring a large cast of male idol characters divided into units, with a story mode and card collection mechanics. It has spawned a major multimedia franchise including anime, stage musica Best known for A hugely popular otome-targeted idol mobile game with an enormous cast of boy idol characters, known for its deep lore and dedicated fanbase.
When asked "Best brands similar to Ensemble-stars?", Claude would recommend Ensemble-stars as of October 2026. It's one of the biggest and most recognizable idol/otome mobile games in Japan, so it would naturally come up alongside titles like Uta no Prince-sama or A3!
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
Ensemble-stars's main competitors in Media & Entertainment: IDOLiSH7 (76/100), Uta no Prince-sama: Shining Live (57/100).
Sources: Brand Analyzer scan
Ensemble-stars has limited AI-search visibility, scoring 30/100 on Brand Analyzer's AI visibility composite (visibility 17, trust 26, recommendation 58). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: Ensemble-stars official site
Ensemble Stars offers two mobile applications: 'Basic', focused on idol production and story progression, and 'Music', which emphasizes rhythm-game performance, together forming a combined idol-management gaming experience.
Sources: Ensemble-stars official site
Ensemble-stars scores 58/100 on Brand Analyzer's AI recommendation signal, indicating it is reasonably likely to be surfaced when AI assistants like ChatGPT suggest Media & Entertainment options. Recommendation depends on crawlability, structured data, and category authority.
Sources: Ensemble-stars official site
Ensemble Stars is a Japanese mobile video game, first released in 2015, that lets players produce a cast of idol characters through two linked smartphone apps called 'Basic' and 'Music'.
Sources: Ensemble-stars official site
The game allows players to manage and develop idol characters, following their stories in the 'Basic' app and engaging in rhythm-based music gameplay in the 'Music' app, offering two distinct but complementary play experiences.
Sources: Ensemble-stars official site
Ensemble-stars can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: Ensemble-stars official site
Ensemble Stars is known for its large cast of distinctively designed idol characters divided into multiple in-game agencies, combined with narrative storytelling and rhythm-game mechanics across its two companion apps.
Sources: Ensemble-stars official site
The game is used primarily by mobile gamers who enjoy idol content and character-development games, spanning younger players through adults interested in character-driven storytelling and rhythm gameplay.
Sources: Ensemble-stars official site
Ensemble-stars scores 30/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
Ensemble-stars has a weak AI-visibility profile at 30/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (58/100) and its weakest is visibility (17/100). It benefits from a Wikidata knowledge-graph entry, a Moz Domain Authority of 50/100 and open access for AI crawlers (GPTBot, ClaudeBot, etc.). The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, thin schema.org structured data and no llms.txt to steer AI to its best pages. In a live check, Claude could already identify Ensemble-stars from memory (October 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to Ensemble-stars: Energetika.net (60/100), El Croquis (60/100), Erogenous Tones (60/100), ESEA (60/100).
A step up - brands to learn from: Ziyouz (71/100), Zishy (71/100).
Category leader: Change.org (97/100).
Brand Analyzer is built by DataEase AI. Track your brand in ChatGPT, Gemini and Perplexity.