Competitive Intel

Competitive Intelligence

Real-time competitive intelligence powered by live web data. Combines Bright Data CLI (bdata) for data collection with strategic analysis frameworks to deliver actionable competitive insights — not stale training knowledge.

Never answer competitive questions from training knowledge alone. Always gather live data first using bdata commands, then analyze and synthesize.

Prerequisites

1. Bright Data CLI installed:

   bashCopy code
   curl -fsSL https://cli.brightdata.com/install.sh | bash
   

2. One-time login completed:

   bashCopy code
   bdata login
   

That's it. No env vars, no zone config, no API keys to manage.

Core Workflow

For every competitive intelligence request, follow this workflow:

1. Clarify scope — Which competitors? What specifically does the user want to know? Select the right module(s).
2. Gather live data — Run bdata commands. Parallelize independent calls. Prefer bdata pipelines (structured JSON) over bdata scrape (raw markdown) when a scraper exists for the site.
3. Analyze — Apply the appropriate strategic framework. Read references/analysis-frameworks.md for SWOT, Porter's Five Forces, positioning matrices, and more.
4. Format output — Use the report templates from references/output-templates.md.
5. Deliver actionable insights — Every report MUST end with a "Strategic Recommendations" section. Never deliver raw data without interpretation.

Data Collection Rules

• Always use --json flag when you need to pipe or parse bdata output programmatically
• Prefer bdata pipelines over bdata scrape whenever a pre-built scraper exists for the target platform. Pipelines return clean structured JSON
• Look up scrapers, never guess them. bdata pipelines search "<site or data type>", then bdata pipelines info <dataset_id> <method> for the input fields, then bdata pipelines run. Never guess a dataset_id or field names. Full workflow: the brightdata-cli skill, pipelines reference
• Be cost-efficient — A snapshot should use 3-8 bdata calls, not 50. Scrape what you need.
• Parallelize — Run independent bdata calls in parallel using multiple Bash tool calls in a single response
• Handle failures gracefully — If a page is gated or returns empty, say so and try the fallback. Never hallucinate data to fill gaps.
• Cite every data point — Include source URLs for everything. Users must be able to verify.

For the full mapping of intelligence needs to bdata commands, read references/data-source-guide.md.

For interpreting raw data as strategic signals, read references/industry-signals.md.

Analysis Modules

1. Competitor Snapshot

When to use: User asks to analyze, profile, or understand a specific competitor.

Data gathering:

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# Step 1: Discover competitor's website and recent news
bdata search "[competitor name]" --json

# Step 2: Scrape key pages (run in parallel)
bdata scrape [competitor-url]              # Homepage — positioning, messaging
bdata scrape [competitor-url]/pricing      # Pricing tiers and model
bdata scrape [competitor-url]/about        # Team, mission, history (try /about, /about-us, /company)

# Step 3: Structured data enrichment (if URLs available)
bdata pipelines search "crunchbase company"      # Funding, investors, employee count
bdata pipelines search "linkedin company"        # Employee count, growth, locations
bdata pipelines info <dataset_id> collect_by_url
bdata pipelines run <dataset_id> collect_by_url "[company-url]"

Analysis: Synthesize into a structured profile. Identify positioning, target audience, key claims, strengths, and vulnerabilities. Compare to user's product if context is available.

Output: Use the Competitor Snapshot template from references/output-templates.md.

2. Pricing Intelligence

When to use: User wants to compare pricing, understand pricing models, or find pricing positioning opportunities.

Data gathering:

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# Scrape pricing pages for each competitor (run in parallel)
bdata scrape [competitor-a-url]/pricing
bdata scrape [competitor-b-url]/pricing
bdata scrape [competitor-c-url]/pricing

# For e-commerce products: find the retailer's product scraper first
bdata pipelines search "[retailer] product"
bdata pipelines run <dataset_id> collect_by_url "[product-url]"

# Supplementary: third-party pricing breakdowns
bdata search "[competitor] pricing review" --json

Analysis: Extract plan names, prices, feature lists, and limits from each page. Normalize into a comparison matrix. Identify pricing model types (per-seat, usage-based, freemium, enterprise-only). Flag positioning signals and recommend opportunities.

Output: Use the Pricing Intelligence template from references/output-templates.md.

3. Review Intelligence

When to use: User wants to understand customer sentiment, find competitor pain points, or identify exploitable gaps.

Data gathering:

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# Find review pages via search
bdata search "[competitor] site:g2.com" --json
bdata search "[competitor] site:capterra.com" --json

# Scrape review pages
bdata scrape [g2-url]
bdata scrape [capterra-url]

# Structured review data (use when direct URLs are available)
bdata pipelines search "[platform] reviews"      # e.g. maps, app store, marketplace
bdata pipelines info <dataset_id> collect_by_url # check required fields
bdata pipelines run <dataset_id> collect_by_url "[review-page-url]"

Analysis: Categorize sentiment (positive/neutral/negative). Extract top praised features, top complaints, and feature requests. Identify comparison mentions ("switched from X", "better than Y"). Complaints are the user's positioning opportunity.

Output: Use the Review Intelligence template from references/output-templates.md.

4. Hiring Signal Analysis

When to use: User wants to infer a competitor's strategic direction from their hiring patterns.

Data gathering:

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# Find LinkedIn company page
bdata search "[competitor] linkedin company" --json

# Get structured job listings
bdata pipelines search "linkedin job listings"
bdata pipelines info <dataset_id>                # pick a method, check its fields
bdata pipelines run <dataset_id> <method> "[linkedin-company-url]"

# Fallback: scrape careers page directly
bdata search "[competitor] careers" --json
bdata scrape [careers-url]

Analysis: Categorize roles by department. Analyze hiring velocity (scaling vs. stable vs. contracting). Identify technology signals from job descriptions. Look for geographic expansion signals. Interpret seniority mix (hiring leaders = new initiative; hiring ICs = scaling existing).

Output: Use the Hiring Signal Analysis template from references/output-templates.md.

5. Content & SEO Battle

When to use: User wants to understand competitors' content strategy or search positioning for specific keywords.

Data gathering:

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# Check SERP rankings for target keywords (run in parallel)
bdata search "[keyword 1]" --json
bdata search "[keyword 2]" --json
bdata search "[keyword 3]" --json

# Estimate competitor's indexed content
bdata search "site:[competitor.com]" --json

# Scrape blog/content pages
bdata scrape [competitor-url]/blog
bdata scrape [top-ranking-article-url]

Analysis: Map which competitors rank for which keywords. Estimate content volume and publishing frequency. Identify topic clusters each competitor invests in. Find content gaps — topics nobody covers well that the user could own.

Output: Use the Content & SEO Battle template from references/output-templates.md.

6. Market Landscape Map

When to use: User wants to understand all players in a market, find white space, or map the competitive landscape.

Data gathering:

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# Discover players via multiple search queries (run in parallel)
bdata search "[industry] companies" --json
bdata search "best [product category] tools" --json
bdata search "[product category] alternatives" --json

# Scrape category/comparison pages
bdata scrape [g2-category-url]

# Quick snapshot of each discovered competitor
bdata scrape [competitor-1-url]
bdata scrape [competitor-2-url]
# ... for each key player (limit to top 8-10)

# Enrich key players with funding/size data
bdata pipelines search "crunchbase company"
bdata pipelines run <dataset_id> collect_by_url "[crunchbase-url]"

Analysis: Categorize players by tier (enterprise, mid-market, SMB, open-source). Build a positioning map (e.g., price vs. feature breadth). Identify white space — underserved segments or positioning no one owns. Note market trends, recent entrants, and consolidation signals.

Output: Use the Market Landscape Map template from references/output-templates.md.

Multi-Module Analysis

When the user asks for a comprehensive competitive analysis (e.g., "full battlecard", "deep dive", "board meeting prep"), combine multiple modules:

1. Start with Competitor Snapshot for each competitor
2. Add Pricing Intelligence for comparison
3. Add Review Intelligence for customer sentiment
4. Optionally add Hiring Signals and Content & SEO for strategic depth
5. Wrap everything in the Executive Summary template from references/output-templates.md

For full battlecards, use the Competitive Battlecard template.

Choosing the Right Module

Output Quality Standards

1. Every data point must have a source URL — no unattributed claims
2. Separate facts from analysis — clearly distinguish scraped data from Claude's interpretation
3. End with "So What?" — every report must have actionable strategic recommendations
4. Be honest about gaps — if data is unavailable, say so. Never fill gaps with training knowledge presented as live data.
5. Date-stamp the analysis — include "Data collected on [date]" so users know freshness