GEO COMPETITOR GAP · GEO競合ギャップ

AIが生成する回答で、競合他社はあなたの代わりに名前を挙げられています。その理由を解明します。

Ignited Nepal's GEO Competitor Gap Analysis is a systematic competitive intelligence study designed for enterprise B2B organisations operating in the Japanese market. We test 30 industry queries across five AI engines, measure citation frequency against three to five named competitors, audit the authority signals driving those citations, and deliver a prioritised action plan grounded in evidence rather than assumption.

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This is for you if

このサービスは、以下の四つのタイプのエンタープライズ企業に最適です。

**Large enterprise companies with complex B2B sales cycles** operating in Japan — manufacturers, logistics firms, financial institutions, and professional services organisations — where procurement teams and business unit leaders are beginning to use AI tools such as ChatGPT, Perplexity, and Gemini to generate initial vendor shortlists. In enterprise B2B contexts, AI citations can influence the consideration set before any human-to-human contact occurs. If your competitors are appearing in those AI-generated shortlists and your organisation is not, you are absent from the earliest stage of the buyer's process.

**Japanese subsidiaries of global organisations** that have strong AI visibility in English-language markets but have not yet determined whether their Japanese-language web presence generates comparable AI citations in Japanese-language queries. The signals that drive English-language AI citations and Japanese-language AI citations are not always the same, and the competitive landscape differs significantly by language and market context.

**Technology and SaaS firms** selling into Japanese enterprise accounts who understand that their primary competition is not only other vendors but the information environment that surrounds those vendors — editorial coverage in Japanese business media, structured data on Japanese-language pages, presence in Japanese-language knowledge sources that AI engines draw on when constructing responses to Japanese-language queries.

**Corporate marketing and digital strategy teams** at mid-to-large Japanese companies that have been asked by senior leadership to assess and improve AI visibility, and who need a concrete, measurable baseline — denominated in actual citation frequencies against named competitors — before committing to a strategic GEO programme.

What's broken

エンタープライズB2B市場におけるAI可視性の問題は、四つの構造的な課題に集約されます。

The competitive intelligence gap is acute in the Japanese market.

Most organisations competing for enterprise contracts in Japan have invested heavily in traditional SEO, corporate communications, and trade media coverage. What they have not done is measure how those investments translate into AI citation frequency — because the tools and methodologies for doing so are only now becoming available. The result is that decisions about GEO investment are being made without the competitor benchmarks needed to prioritise effectively.

Japanese-language query testing is absent from most GEO programmes.

The majority of AI visibility audits conducted by global agencies are run in English. This is a significant gap for businesses competing in Japan, because AI engines construct Japanese-language responses using different source signals than English-language responses — the editorial sources they trust, the knowledge bases they draw on, and the schema signals they weight can differ materially. A business that appears prominently in English-language AI responses may have near-zero presence in Japanese-language AI responses on the same topic.

The enterprise buyer's journey is changing faster than most corporate marketing functions realise.

Senior procurement managers and business unit leaders at Japanese enterprise companies are adopting AI tools for preliminary market research and vendor identification. The initial shortlist they bring to a formal procurement process is increasingly influenced by AI-generated responses. By the time a formal RFP is issued, competitors who have been consistently cited in AI responses have already built a familiarity advantage that is difficult to overcome.

Seniority and reputation do not automatically transfer to AI citations.

A company that has operated in Japan for decades, has extensive media coverage in traditional business publications, and has strong relationships with industry associations may still have a weak AI citation profile — because the specific signals AI engines use to identify authoritative entities in a given category are not always captured by legacy corporate communications. Understanding precisely which signals are missing is the first step toward addressing the gap.

What we engineer

GEO競合ギャップ分析は、六つの具体的な成果物を提供します。

A 30-query test set in Japanese

constructed around the actual language patterns your target buyers use when consulting AI tools for vendor research. The set covers awareness-stage queries (「〇〇サービスを提供する企業を教えてください」), consideration-stage queries (「〇〇の選定基準とは何ですか」), and comparison queries (「〇〇と△△の違いを教えてください」) — all reflecting the natural-language phrasings that Japanese-language AI engines respond to, rather than keyword-formatted search strings.

A five-engine citation sweep

running each Japanese-language query through ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude, with full response logging, competitor mention extraction, and citation frequency calculation for each competitor-query-engine combination.

A competitor citation matrix

documenting which of your three to five named competitors was cited for which queries on which engines, with frequency scores and response context summaries for each major citation event.

A gap signal audit

examining the Japanese-language authority signals that differentiate cited competitors from non-cited ones — including presence and quality of Japanese-language Wikipedia articles, coverage in major Japanese business and trade media, structured data implementation on Japanese-language pages, backlink profiles from Japanese academic and journalistic sources, and content breadth on key topic clusters.

A prioritised action plan

ranking each identified gap by citation opportunity, competitor weakness in that signal area, and implementation effort — structured for assignment to in-house corporate communications, content, and technical teams.

An executive brief in English and Japanese

translating all findings into business language for presentation to senior leadership and relevant business units.

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What changes

このサービスの完了後、四つの重要な変化が生まれます。

Before
After
Before Before this analysis, your organisation's understanding of AI competitive dynamics is based on anecdote, general industry commentary, and partial observation.
After Your competitive intelligence on AI visibility becomes concrete. After it, you have a citation frequency score for each named competitor, a documented map of which queries they dominate, and a specific list of the signal gaps driving their advantage. Decisions about GEO investment can now be made on the same evidence-based basis as decisions about any other area of competitive strategy.
Before Many global organisations discover through this analysis that their AI visibility profiles differ substantially between languages — that a competitor with weaker English-language AI presence has built a strong Japanese-language citation footprint, or vice versa.
After Japanese-language and English-language AI visibility are assessed separately. This separation is strategically important: it allows your team to prioritise language-specific investments rather than applying a uniform global programme that may address the wrong gap in the wrong market.
Before The gap signal audit identifies which specific content types, source categories, and structured data elements are most consistently associated with competitor citations in your category.
After Content and communications investment is redirected toward citation drivers. This redirects your team's investment from general brand-building activity toward the specific asset types that the evidence identifies as citation drivers in the Japanese enterprise market.
Before When your team reports on AI visibility progress to leadership, the conversation moves from qualitative observation to quantitative comparison against a documented starting point.
After A measurable baseline is established for ongoing programme management. The citation matrix and frequency scores become the benchmark against which all subsequent GEO activity is measured.
Common questions

FAQ

What is a GEO Competitor Gap Analysis in the context of the Japanese market?

A GEO Competitor Gap Analysis is a structured measurement study that determines which of your named competitors are being cited in Japanese-language AI-generated responses, how frequently they appear across five major AI engines, and which specific authority signals are driving those citations that your current Japanese-language web presence does not yet have. It is a quantitative competitive intelligence exercise, not an advisory framework — the output is a citation frequency matrix and a gap-specific action plan.

Why does AI competitive analysis require a different approach than traditional SEO competitive analysis in Japan?

Traditional SEO competitive analysis in the Japanese market measures organic ranking positions on Google Japan, backlink profiles from Japanese domains, and search volume data from tools calibrated to Japanese search behaviour. AI citation analysis measures something structurally different: whether a large language model, when processing a Japanese-language query, has sufficient confidence in your entity's relevance and authority to include you in its generated response. The sources AI engines draw on for Japanese-language content — including Japanese Wikipedia, Nikkei coverage, schema-annotated pages, and Japanese-language entity databases — do not map one-to-one onto the signals that drive Japanese Google rankings.

What kinds of queries are included in the 30-query test set for Japan?

The 30-query test set for Japan is constructed entirely in Japanese, using natural-language phrasings calibrated to how enterprise decision-makers and their research staff actually use AI tools. Approximately ten queries represent category-awareness intent — open questions about providers or approaches in your space. Ten represent consideration intent — questions about evaluation criteria, methodology differences, and vendor selection factors. Ten are comparison queries naming competitors directly or asking AI engines to compare specific providers. All 30 are reviewed with your team before the sweep begins.

What is the delivery timeline for Japanese engagements?

Standard GEO Competitor Gap Analysis engagements for Japan are delivered within twelve to sixteen working days from the briefing session, reflecting the additional time required for Japanese-language query construction, Japanese-language response logging, and Japanese-language editorial source auditing. The executive brief is delivered in both English and Japanese. Accelerated timelines are available for organisations with specific deadline requirements and are quoted during the briefing.

What does this service cost for Japanese engagements?

Japanese-market GEO Competitor Gap Analysis is priced at ¥420,000 for three competitors and ¥560,000 for five competitors. Both tiers include the full Japanese-language 30-query sweep across all five engines, the competitor citation matrix, the gap signal audit covering Japanese-language authority signals, the prioritised action plan, and the bilingual executive brief. Ongoing monthly retainer options for citation tracking and action plan implementation are quoted separately.

What does an organisation do with the analysis after it is delivered?

The action plan delivered at the close of the engagement is a specific, ranked list of implementable steps assigned to functional owners. Most Japanese enterprise clients distribute the plan across their corporate communications department (for media and editorial coverage gaps), their digital team (for structured data and content format gaps), and their external PR agency (for third-party source development). A structured retest at the 90-day mark is available at a reduced rate and is recommended as part of the ongoing GEO programme management cycle.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Niraj Raut

Founder — Ecommerce SEO
Keshab Joshi

Keshab Joshi

PPC Expert
Hawrry Bhattarai

Hawrry Bhattarai

Google Ads Expert
Arogya Rijal

Arogya Rijal

SaaS SEO Expert
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Start here

AIが生成する競合比較において、あなたの組織が何位に位置づけられているかを正確に把握してください。

The GEO Competitor Gap Analysis gives your team the citation data and gap-specific action plan needed to compete in AI-generated responses with the same rigour you already bring to traditional competitive strategy. Engagements are taken in a limited number each month to preserve analytical quality.

Run an AI Visibility Audit →

Run an AI Visibility Audit