Competitive Research Pack

The Data Wrecking Ball of Competitive Analysis

We built the Competitive Research Pack because we were tired of watching strategy analysts spend 60% of their week cleaning data instead of finding insights. You know the drill: a competitor announces a "AI-powered" feature on a blog post, and your PM immediately drafts a ticket. Two weeks later, you realize it's just a wrapper around an open-source model they didn't build. Meanwhile, your internal feature matrix is a Google Doc from Q2, and sales is pitching a pricing tier that was deprecated three months ago.

Competitive intelligence isn't a one-time report; it's a continuous data problem. You're juggling feature matrices in Excel, pricing sheets in PDFs, and positioning maps that haven't been updated since the last board meeting. The result? Decisions based on gut feel instead of structured data. As noted in foundational CI research, building a rock-solid intelligence framework is the first step, but most teams skip straight to tools without a workflow, leaving them vulnerable to ad-hoc noise ^[2]. When your research is fragmented across Slack threads, browser tabs, and stale spreadsheets, you're not doing competitive analysis; you're just collecting digital clutter.

If you're also looking to conduct comprehensive competitive intelligence that includes SWOT analysis and broader market mapping, this pack integrates seamlessly into that workflow, but it solves the specific pain of structured data ingestion and validation that generic tools miss.

What Stale Intelligence Costs Your Bottom Line

When your research is fragmented, downstream teams make expensive mistakes. Sales reps pitch features that don't exist because the battle cards are outdated. Product managers prioritize a roadmap based on a competitor's beta announcement that never shipped. Klue's framework highlights that without centralized intelligence and timely win/loss data, your internal messaging drifts into "sugar-free" territory—or worse, becomes fiction ^[3]. Every hour you spend reconciling a YAML file against a competitor's changelog is an hour you're not spotting the market shift that kills your moat.

The financial impact is quantifiable. Pricing errors alone can erode 15% of your gross margin. We've seen teams lose deals because their battle cards didn't account for a competitor's bundling strategy, which was only visible in a PDF invoice they couldn't parse. The Strategy Institute emphasizes that integrating analysis into your strategic plan requires rigorous data gathering and framework application, not just a quarterly PDF dump ^[4]. When you can't prove differentiation with structured data, you're forced to compete on price, which is a race to the bottom.

For teams that need to validate market segments and gap analysis, the SBA-aligned standards in our references ensure your competitor data aligns with TAM/SAM/SOM assessments, preventing the common error of analyzing the wrong competitive set.

How a SaaS Team Fixed Their Positioning with a Price-Benefit Map

Imagine a mid-market SaaS team preparing for a Series B. They need to prove differentiation to investors. Their current "competitive analysis" is a folder of screenshots and a Google Doc. They try to build a positioning map from scratch. It takes two weeks. The axes are arbitrary. The result is a blob of dots that proves nothing. A classic HBR case study ^[1] demonstrates that a rigorous price-benefit positioning map allows you to see your product through the customer's eyes, revealing gaps that internal bias hides. That team eventually adopted a structured workflow. They defined axes based on customer willingness-to-pay, mapped direct and indirect competitors, and realized they were over-engineering a feature set that the market didn't value. They cut scope, adjusted pricing, and the investor deck went from "nice to have" to "category leader."

Another example comes from the logistics sector, where a team of 200 endpoints struggled with inconsistent error schemas across their API surface. While that's a different domain, the lesson holds: without a canonical schema and validation, your data is noise. Just as that team needed structured logging across services to fix their incidents, a strategy team needs a canonical schema to fix their intelligence.

The team in our story implemented a 5-step workflow: identify, gather, matrix, map, act. They used a feature-pricing matrix to normalize competitor data, then mapped it on a 2x2 grid. The mapping revealed a white space in the "high price, low complexity" quadrant that they could own. They didn't just find a gap; they validated it against customer survey data and updated their pricing model within a sprint. That's the difference between a report that sits on a shelf and intelligence that moves the needle.

From Ad-Hoc Spreadsheets to Validated Strategic Assets

Once the Competitive Research Pack is installed, your workflow shifts from chaos to code. You run scripts/scaffold-research.sh and get a directory structure with industry defaults. You populate templates/feature-pricing-matrix.yaml, and scripts/validate-matrix.sh enforces strict schema rules—no more missing pricing tiers or unstructured feature flags. The validator parses your matrix against config/matrix-schema.json, exiting non-zero on missing required keys, invalid pricing formats, or unstructured feature data. You sleep better knowing your data is structurally sound.

scripts/analyze-trends.py ingests your data and outputs pricing deltas and feature coverage scores automatically. It computes trend vectors based on gap analysis, so you can see where competitors are investing and where they're retreating. You generate a positioning-2x2.json that aligns with the axes defined in your references/positioning-canonical.md. Sales gets battle cards that actually work, with structured sections for competitor weaknesses, our differentiators, and deal-breakers. Product gets trend vectors. You stop arguing about what the competitor is doing and start acting on validated data.

This pack doesn't replace your judgment; it amplifies it. We built this so you don't have to wrestle with YAML schemas or write Python scripts to compute pricing deltas. You focus on the strategy; we handle the plumbing. If you need to conduct comprehensive competitive intelligence for deeper SWOT analysis, the battle cards generated here feed directly into that broader analysis.

What's in the Competitive Research Pack

• skill.md — Orchestrator skill definition; maps the 5-step competitive research workflow, explicitly references all templates, references, scripts, validators, and examples by relative path to guide agent execution.
• templates/feature-pricing-matrix.yaml — Production-grade YAML template with strict schema keys for systematic feature comparison, pricing tier mapping, and value proposition tracking across direct/indirect competitors.
• templates/positioning-2x2.json — JSON configuration for 2x2 market positioning mapping; defines axes, quadrant labels, strategic implications, and differentiation triggers per industry vertical.
• templates/battle-card.md — Structured markdown template for win/loss analysis, objection handling, and messaging playbooks; includes sections for competitor weaknesses, our differentiators, and deal-breakers.
• references/zhang-5step-framework.md — Canonical knowledge extraction of Zhang Zaiwang's Effective Competitive Analysis methodology; embeds the 5-step workflow (identify, gather, matrix, map, act) with tactical checklists and decision gates.
• references/positioning-canonical.md — Authoritative reference on positioning statement templates, 2x2 mapping methodology, category strategy options, and differentiation playbooks; includes real-world axis selection criteria and quadrant strategy mappings.
• references/market-research-standards.md — SBA-aligned market research standards and actionable results framework; covers direct vs indirect competitor identification, market segment assessment, and gap validation for value proposition defense.
• scripts/validate-matrix.sh — Programmatic validator that parses the feature-pricing-matrix.yaml against config/matrix-schema.json; exits non-zero (exit 1) on missing required keys, invalid pricing formats, or unstructured feature data.
• scripts/scaffold-research.sh — Executable workflow script that initializes a competitive research project; clones templates, sets up directory structure, and generates a baseline positioning-2x2.json with industry defaults.
• scripts/analyze-trends.py — Python script that ingests structured competitor data, computes pricing deltas, feature coverage scores, and trend vectors; outputs actionable strategic recommendations based on gap analysis.
• examples/worked-example.yaml — Fully populated production example demonstrating correct YAML structure, realistic competitor data, pricing tier normalization, and feature weighting for a SaaS vertical.
• config/matrix-schema.json — JSON Schema definition enforcing strict structure on the feature-pricing-matrix.yaml; validates competitor IDs, pricing currency/format, feature boolean/numeric arrays, and strategic tags.

Install the Workflow. Ship the Insight.

Stop wrestling with ad-hoc spreadsheets and stale PDFs. Start shipping structured competitive intelligence that drives pricing strategy, product roadmap, and sales enablement. Upgrade to Pro to install the Competitive Research Pack and lock in your workflow. We built this so you can focus on the strategy, not the schema.

For teams that need to validate market segments and gap analysis, this pack provides the competitor data layer that feeds into broader market research workflows. If you're also looking to conduct comprehensive competitive intelligence including SWOT analysis, this pack ensures your data is clean and validated before you start your analysis.