learn
intermediate
Claude
ChatGPT
Gemini
Multi-platform
How to Use AI for Competitor Analysis and Market Intelligence
A practical workflow for using AI to track competitors, extract market signals, and build intelligence briefs across languages and markets.
9 min read21 February 2026
competitor analysis
market intelligence
research
multilingual
Asia-Pacific
A repeatable process for using AI to gather, analyse, and synthesise competitor intelligence across multiple markets and languages
Built for founders, strategists, and marketing leads who track competitors manually and know they're missing signals
You'll walk away with a working workflow, tested prompts, and a clear method for turning scattered information into decisions
Covers Claude, ChatGPT, and Gemini, with specific notes on where each one earns its place
Why This Matters
Most competitor analysis is either too shallow or too slow. You skim a rival's website, check their latest LinkedIn posts, maybe read a funding announcement, and call it done. Or you spend two days building a spreadsheet that's outdated by Thursday. Neither approach gives you anything you can act on.
The problem gets worse when you're operating across Asian markets. Your competitor in Indonesia might be running campaigns on Tokopedia that you'd never see from Singapore. A Chinese rival's product positioning lives on Xiaohongshu reviews and Douyin comments, not on their English-language homepage. A Japanese competitor's strategic direction is buried in their quarterly earnings call transcript, which was published only in Japanese. You're not just tracking companies. You're tracking companies across languages, platforms, and regulatory environments, often simultaneously.
AI doesn't replace the strategic thinking that turns information into insight. But it can collapse the gathering and synthesis phases from days into hours. The multilingual capability alone changes what's possible for a five-person team trying to stay informed across Southeast Asia, China, Japan, and Korea. Here's the process that makes it work.
---
The problem gets worse when you're operating across Asian markets. Your competitor in Indonesia might be running campaigns on Tokopedia that you'd never see from Singapore. A Chinese rival's product positioning lives on Xiaohongshu reviews and Douyin comments, not on their English-language homepage. A Japanese competitor's strategic direction is buried in their quarterly earnings call transcript, which was published only in Japanese. You're not just tracking companies. You're tracking companies across languages, platforms, and regulatory environments, often simultaneously.
AI doesn't replace the strategic thinking that turns information into insight. But it can collapse the gathering and synthesis phases from days into hours. The multilingual capability alone changes what's possible for a five-person team trying to stay informed across Southeast Asia, China, Japan, and Korea. Here's the process that makes it work.
---
How to Do It
1
### Step 1: Define your intelligence questions before you touch any tool
Write down 3-5 specific questions you need answered about each competitor. Not "what are they doing?" but "have they changed their pricing structure in the last 90 days?" or "what features are their users complaining about on app store reviews?"
Vague questions produce vague intelligence. If you can't write the question in one sentence, you don't know what you're looking for yet. Spend ten minutes here. It shapes everything downstream.
### Step 2: Build a competitor profile document as your working base
Create a structured document for each competitor that AI can reference across sessions. Include: company name, headquarters, key markets, product lines, known pricing, recent funding, leadership team, and any public strategic statements.
You probably know 60% of this already. Use AI to fill the gaps by feeding it the competitor's website, recent press coverage, and any investor materials you can find. Claude and Gemini both handle URL analysis well here. The point is to create a single source that you can paste into future prompts, so every analysis starts from a shared baseline.
### Step 3: Gather raw intelligence from multiple source types
This is where most people stop too early. They check a competitor's blog and their LinkedIn. That's one signal type from one platform. You need variety.
Collect across at least three categories: public communications (website, blog, press releases, social media), customer signals (app store reviews, forum posts, social mentions, Xiaohongshu or Trustpilot reviews depending on market), and structural signals (job postings, patent filings, regulatory submissions, earnings transcripts). AI can process all of these, but you need to feed them in.
For Asian markets specifically, Gemini's access to Google Search makes it useful for pulling recent coverage in local languages. Claude handles longer documents well if you're feeding in full earnings transcripts or regulatory filings.
### Step 4: Use AI to extract patterns, not just summarise
Summarisation is the lazy use case. Anyone can get AI to summarise a competitor's blog post. The value is in extraction: pulling out specific data points, changes over time, and signals that indicate strategic direction.
Ask AI to identify what changed between two versions of a pricing page. Ask it to categorise 200 app store reviews by complaint type and rank them by frequency. Ask it to compare the job titles a competitor is hiring for this quarter versus last quarter and flag any new capability areas. These are specific analytical tasks with structured outputs, not open-ended "tell me about this company" requests.
### Step 5: Synthesise across sources into an intelligence brief
Once you've gathered and extracted from multiple sources, bring them together. Paste your findings into a single prompt and ask AI to identify connections: does the hiring pattern match the product changes? Do customer complaints align with areas where the competitor seems to be underinvesting?
Structure your brief around your original intelligence questions from Step 1. Each question gets a finding, the evidence behind it, and a confidence level. This is the output your leadership team or board actually wants, not a pile of raw observations.
### Step 6: Set up a recurring cadence, not a one-off project
Competitor intelligence is only useful if it's current. Build a lightweight weekly or fortnightly process: update the profile document, run your extraction prompts against new data, and update the brief. The whole thing should take 60-90 minutes once your templates are in place, compared to the full-day exercise most teams run quarterly and then forget about.
---
Write down 3-5 specific questions you need answered about each competitor. Not "what are they doing?" but "have they changed their pricing structure in the last 90 days?" or "what features are their users complaining about on app store reviews?"
Vague questions produce vague intelligence. If you can't write the question in one sentence, you don't know what you're looking for yet. Spend ten minutes here. It shapes everything downstream.
### Step 2: Build a competitor profile document as your working base
Create a structured document for each competitor that AI can reference across sessions. Include: company name, headquarters, key markets, product lines, known pricing, recent funding, leadership team, and any public strategic statements.
You probably know 60% of this already. Use AI to fill the gaps by feeding it the competitor's website, recent press coverage, and any investor materials you can find. Claude and Gemini both handle URL analysis well here. The point is to create a single source that you can paste into future prompts, so every analysis starts from a shared baseline.
### Step 3: Gather raw intelligence from multiple source types
This is where most people stop too early. They check a competitor's blog and their LinkedIn. That's one signal type from one platform. You need variety.
Collect across at least three categories: public communications (website, blog, press releases, social media), customer signals (app store reviews, forum posts, social mentions, Xiaohongshu or Trustpilot reviews depending on market), and structural signals (job postings, patent filings, regulatory submissions, earnings transcripts). AI can process all of these, but you need to feed them in.
For Asian markets specifically, Gemini's access to Google Search makes it useful for pulling recent coverage in local languages. Claude handles longer documents well if you're feeding in full earnings transcripts or regulatory filings.
### Step 4: Use AI to extract patterns, not just summarise
Summarisation is the lazy use case. Anyone can get AI to summarise a competitor's blog post. The value is in extraction: pulling out specific data points, changes over time, and signals that indicate strategic direction.
Ask AI to identify what changed between two versions of a pricing page. Ask it to categorise 200 app store reviews by complaint type and rank them by frequency. Ask it to compare the job titles a competitor is hiring for this quarter versus last quarter and flag any new capability areas. These are specific analytical tasks with structured outputs, not open-ended "tell me about this company" requests.
### Step 5: Synthesise across sources into an intelligence brief
Once you've gathered and extracted from multiple sources, bring them together. Paste your findings into a single prompt and ask AI to identify connections: does the hiring pattern match the product changes? Do customer complaints align with areas where the competitor seems to be underinvesting?
Structure your brief around your original intelligence questions from Step 1. Each question gets a finding, the evidence behind it, and a confidence level. This is the output your leadership team or board actually wants, not a pile of raw observations.
### Step 6: Set up a recurring cadence, not a one-off project
Competitor intelligence is only useful if it's current. Build a lightweight weekly or fortnightly process: update the profile document, run your extraction prompts against new data, and update the brief. The whole thing should take 60-90 minutes once your templates are in place, compared to the full-day exercise most teams run quarterly and then forget about.
---
What This Actually Looks Like
The Prompt
I'm going to paste 45 Google Play Store reviews (in Bahasa Indonesia) for [Competitor App Name], all from the last 90 days. Analyse these reviews and produce: 1. A table of the top 5 complaint categories, ranked by frequency, with an example quote (translated to English) for each 2. A table of the top 3 things users praise, same format 3. Any mentions of specific competitor features that don't exist in the current version of our product: [brief description of your product] 4. Any signals about the competitor's recent changes (new features launched, bugs introduced, pricing changes users mention) Output in British English. Translate Indonesian quotes but include the original text in brackets. Reviews: [PASTE REVIEWS]
Prompts to Try
Prompt 1: Competitor Job Posting Analysis
Here are 15 current job postings from [Competitor Name], copied from their careers page and LinkedIn. Analyse these postings and tell me: 1. What capability areas are they building? Group the roles into functional clusters (e.g., "data engineering," "enterprise sales," "Southeast Asia expansion"). 2. Which clusters have the most open roles? What does this suggest about their strategic priorities for the next 6-12 months? 3. Are there any roles that signal a new market entry, new product line, or pivot? Flag these specifically. 4. Compare this to a typical [INDUSTRY] company at their stage. What's unusual or notable about their hiring pattern? Be specific. I want analysis I can present to my leadership team, not a restatement of the job descriptions. Job postings: [PASTE POSTINGS]
What to expect: A structured breakdown that groups roles by strategic theme, with specific inferences about where the competitor is investing. Works well on both Claude and ChatGPT. Claude tends to be more cautious in its inferences, which is actually useful here since you don't want overconfident speculation.
Prompt 2: Multilingual Sentiment Comparison
I have two sets of customer reviews for competing products in the same market: SET A: [Your Product] - 30 reviews from [Platform], in [Language] SET B: [Competitor Product] - 30 reviews from [Platform], in [Language] For each product: 1. Categorise reviews into positive, negative, and mixed 2. Identify the top 3 themes in each category 3. Pull one representative quote per theme (translate to English if not already, include original in brackets) Then compare the two products: - Where does Product A have an advantage based on customer perception? - Where does Product B have an advantage? - Are there unmet needs mentioned for both products that neither is addressing? Output as structured tables. British English. SET A Reviews: [PASTE] SET B Reviews: [PASTE]
What to expect: Side-by-side comparison tables with translated quotes. Gemini handles this well when the reviews are in languages with strong Google Translate support (Bahasa, Thai, Vietnamese, Japanese). Claude is better for Chinese-language reviews where tonal context and implied meaning matter for accurate sentiment reading.
Prompt 3: Weekly Intelligence Brief Generator
You are helping me produce a weekly competitor intelligence brief for my team. I'll provide this week's raw inputs. Our company: [One-sentence description and key markets] Competitors being tracked: [List 2-4 competitor names] This week's inputs: - [Paste any news articles, press releases, or social posts] - [Paste any product updates or changelog entries] - [Paste any job postings or leadership changes] - [Any other signals: pricing changes, partnership announcements, etc.] Produce a brief with this structure: 1. HEADLINE: One sentence on the most significant competitive development this week 2. BY COMPETITOR: 2-3 bullet points per competitor covering what's new and what it means for us 3. SIGNALS TO WATCH: 1-2 emerging patterns that don't require action yet but are worth tracking 4. RECOMMENDED ACTIONS: 1-2 specific things our team should do this week in response Keep it under 500 words. No filler. Every sentence should either inform a decision or flag a risk.
What to expect: A concise brief you could paste into Slack or email to your leadership team. The quality depends heavily on the inputs you provide. Garbage in, platitudes out. Works across all major AI tools. Run it weekly and you'll build a useful competitive timeline over a few months.
Common Mistakes
Asking AI to "research" a competitor from scratch.
AI models don't have access to real-time data (except Gemini with search enabled), and their training data has a cutoff. If you ask "what is Company X's current pricing?", you'll get outdated or fabricated information. Always feed AI the source material yourself.
Treating AI output as verified intelligence.
AI will present inferences with the same confidence as facts. If it says "this hiring pattern suggests a pivot to enterprise sales," that's a hypothesis, not a finding. Label everything with a confidence level and verify the high-stakes claims independently.
Running the same generic prompt for every competitor.
A fintech startup in Jakarta and an enterprise SaaS company in Tokyo require completely different intelligence questions, source types, and analytical frameworks. Customise your prompts for each competitor's market, stage, and business model.
Ignoring non-English sources.
If your competitor operates in markets where the primary business language isn't English, their most revealing information lives in local-language content: customer reviews, local press, job postings on regional boards, regulatory filings. AI's multilingual capability is the biggest advantage it brings to competitive intelligence. Use it.
Producing intelligence that nobody acts on.
A beautiful 20-page competitor report that sits in a shared drive is worth nothing. Structure your output around decisions: what should we do differently this week based on what we learned? If your brief doesn't connect to action, it's just research theatre.
---
---
Tools That Work for This
Claude- Strongest for long-document analysis. Feed it a 50-page earnings transcript or a pile of customer reviews and ask for structured extraction. Handles nuance well in multilingual content. No real-time web access, so you supply the source material.
Gemini- The built-in Google Search integration makes it genuinely useful for pulling recent competitor coverage, especially across languages. Writing quality for analysis is slightly behind Claude, but the access to current information fills a real gap.
ChatGPT (GPT-4o)- Good for structured analysis tasks and following complex multi-step instructions. The browsing feature helps with pulling in recent content. Reliable for producing formatted outputs like comparison tables.
Perplexity- Useful as a research starting point when you need to quickly surface recent news, funding rounds, or public statements about a competitor. Not where you'd do the deep analysis, but good for the initial scan.
PromptAndGo.ai- If you're running these intelligence workflows regularly, storing and iterating on your competitor analysis prompts in one place saves time over digging through chat histories every week.
---
Frequently Asked Questions
Not if you want accuracy and strategic judgement. AI handles the gathering and synthesis phases well, which are the most time-consuming parts. The interpretation, the "what does this mean for our strategy" thinking, still needs a human who understands the market context. For teams that can't afford a full-time analyst, AI gets you 70% of the value at 10% of the cost.
Manually, mostly. Copy app store reviews, download job postings, save press articles, export social media posts. Some tools like Perplexity and Gemini with search can pull recent public content directly. For structured data (pricing pages, feature lists), screenshot and describe, or copy the text. AI can't access login-protected content or proprietary databases.
Surprisingly good for major Asian languages: Japanese, Korean, Mandarin, Bahasa Indonesia, Thai, and Vietnamese all produce useful results. Accuracy drops for less-resourced languages and for content that relies heavily on local slang or cultural context. When stakes are high, have a native speaker spot-check the AI's translations and sentiment readings.
Weekly light scans (30-60 minutes using saved prompts) and a deeper monthly review (half a day) works for most teams. If you're in a fast-moving market like fintech in Southeast Asia or consumer AI globally, bump the light scans to twice weekly. Consistency matters more than intensity.
Yes, and it's the biggest risk in this workflow. AI will invent plausible-sounding statistics, attribute statements to executives who never said them, and present outdated information as current. Treat every factual claim in AI output as unverified until you've checked it against a primary source. Use AI for analysis and pattern recognition, not as a source of facts.
---
---
Next Steps
Pick one competitor and run the full workflow this week. Start with your intelligence questions, build the profile document, gather one set of raw inputs (app store reviews are the easiest starting point), and produce your first brief. The process gets faster every time you run it, especially once your prompts are dialled in.
For the multilingual side of this, see [INTERNAL LINK: How to use AI for multilingual content and translation across Asian markets]. If you're feeding competitor intelligence into strategic planning, [INTERNAL LINK: How to use AI for business strategy and scenario planning] covers the next step in the process.
Want to customise these prompts for your specific use case? PromptAndGo.ai can optimise any prompt for your platform and audience.
For the multilingual side of this, see [INTERNAL LINK: How to use AI for multilingual content and translation across Asian markets]. If you're feeding competitor intelligence into strategic planning, [INTERNAL LINK: How to use AI for business strategy and scenario planning] covers the next step in the process.
Want to customise these prompts for your specific use case? PromptAndGo.ai can optimise any prompt for your platform and audience.