BCG Growth-Share Matrix
SkillAI & modelsThis skill lets your AI run BCG matrix analysis, sorting products or business units into star, question mark, cash cow, and dog groups. It classifies each unit by market growth rate and relative market share, giving you a clear picture of your portfolio. Once added, your AI can take a list of business units and place each one in the right group.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding the skill, give your AI the products or business units you want reviewed and ask it to sort them into the four BCG matrix groups.
Then ask your AI: use the BCG Growth-Share Matrix skill
What your AI can do with it
- Classify business units by market growth rate and relative market share
- Sort products or business units into star, question mark, cash cow, and dog groups
- Analyze an entire portfolio of products or business units
- Compare units across your portfolio on growth and market share
What this skill tells your AI
The instructions your AI receives, as published by zafer-liu/data-analysis-agent in skills/bcg-matrix/SKILL.md and read by ahel’s review.
Overview
Maps each business unit on a 2×2 grid of market growth rate vs. relative market share, revealing which units generate cash, which absorb it, and which to invest in, harvest, or exit. Four quadrants: Stars (invest), Cash Cows (harvest), Question Marks (binary decide), Dogs (exit or hold minimally). Rests on two empirical anchors: experience curve (high share = lowest cost) and industry life cycle (high growth demands reinvestment; maturity throws off cash).
Composes with: porters-five-forces to define industry boundary first · swot-analysis for internal-capability depth · ansoff-matrix to set growth direction for units worth investing in.
When to Use
- Firm operates ≥ 3 distinct business units competing for a shared capital pool
- Annual strategy or budget reviews need a forcing function for prioritization
- PE/VC portfolio requires a quick health-read across holdings; M&A teams assessing retain vs. divest
- AI capital reallocation: deciding which units to harvest to fund AI capex / AI-native bets, and whether an AI unit is a true Star or an expensive Question Mark amid AI-native competition
When NOT to use: single-product startup · highly interdependent units where divesting a Dog may destroy a Cash Cow · market in technology transition with unreliable growth data · firm-level competitive analysis within one market
Coaching Novices (Adaptive Front Door)
- Engine mode: user has specific BU data → run The Process directly.
- Coach mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- BCG shows which businesses fund others, which burn cash, and which need a decision — using two numbers: market growth rate and your share relative to your biggest competitor.
- Check fit: does the user have multiple distinct units? If single-product, redirect to Ansoff or Five Forces.
- Ask: "Which business units are you trying to prioritize?"
[WAIT — do not advance until user responds]
- Walk through unit definition, data collection, quadrant plotting, trend analysis, and strategy assignment one step at a time.
[WAIT — do not advance until user responds]
- Close: "The key thing BCG just revealed is [which unit is your implicit funder and which is consuming it without a clear path to self-sufficiency]."
[WAIT — do not advance until user responds]
The Process
Produce a Portfolio Map — quadrant assignments, trend arrows, and resource-allocation recommendations per SBU.
Step 1 — Define SBUs. Must: serve an identifiable customer group, have identifiable competitors, be manageable with resource independence. Stop rule: if you cannot name the primary competitor, the boundary is wrong.
Step 2 — Market growth rate. 2–3 years external data; calculate CAGR. Dividing line: 10% (raise to 20–30% for AI/clean-tech). Never use own revenue growth as a proxy.
Step 3 — Relative market share. Own share ÷ largest competitor's share. >1.0 = leader; <1.0 = follower.
Step 4 — Plot. X-axis: relative share (log, right = high); Y-axis: growth (linear, up = high); bubble size = revenue. Assign quadrant.
Step 5 — Trend arrows. 2-year trajectory per SBU. Trend often matters more than current position.
Step 6 — Strategy. Star: invest aggressively. Cash Cow: extract surplus; minimize capex. Question Mark: binary — upgrade to Star OR exit by a named date. Dog: harvest/exit; hold only if synergy is named and quantified.
Output Template
BCG Portfolio Map: <company> | Threshold: <X>% | Date: <date>
SBU | Growth | Rel.Share | Quadrant | Revenue | Profitable?
Trend: <SBU> moving <from> → <to> — reason: <…>
Cash generators: <list> | Cash absorbers: <list> | Balance: <surplus/deficit>
Strategy: <SBU A>: invest/harvest/exit by <date>
Key decision: <what the analysis forces>
→ Method in Action: Procter & Gamble's Brand Portfolio Restructuring (2012–2016) · GE's "Fix, Sell, or Close" Pruning (1981–1995) → 2026 lens: Microsoft's Portfolio as AI Reallocates Capital (2024–2026) — which units are Stars, which Cash Cows fund the AI capex build, which are Question Marks or Dogs
Portfolio Packs
| Industry | Share proxy | Growth proxy | Dog trap | Star misread |
|---|---|---|---|---|
| Consumer packaged goods | Nielsen/IRI retail share | Category CAGR | Legacy brand in declining format | Tiny-base subcategory inflating growth rate |
| Enterprise SaaS | ARR share vs. ICP rivals | Gartner/IDC forecast | Feature-complete product commoditizing | VC competitor's discount-driven "growth" |
| AI products (2024+) | Monthly active API users vs. nearest rival | Segment TAM growth | Model-wrapper with no defensible moat | Benchmark-topping product with no enterprise path |
| Retail/e-commerce | GMV share | Segment GMV CAGR | Category with free platform substitute | High-growth vertical with dominant incumbent |
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] Using absolute share instead of relative share | A 25%-share firm facing a 40% rival is a follower. Absolute share hides competitive position. |
| [D] Defining market too broadly to manufacture high relative share | Calling a niche player in "enterprise software" a leader obscures the actual threat. |
| [D] Labeling every Dog as "strategic" to avoid exit | Synergy must be quantifiable — name the mechanism and the dollar amount. |
| [D] Treating the matrix as a one-time exercise | Growth rates and positions shift. Refresh annually at minimum. |
| [D] Assuming every Question Mark deserves investment | Correct default is a defined decision deadline. Most Question Marks should be exited. |
| [D] Using BCG to justify a decision already made | If unit definitions are chosen after quadrant destinations are known, the analysis is reverse-engineered. |
| [D] Applying experience-curve assumption to software or platforms | High share does not mechanically produce low costs in knowledge-intensive businesses. |
| [D] Treating all Cash Cows as permanent | Cows can become Dogs. Maintain a deterioration watch with leading indicators. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Market boundary defined after desired quadrant assignment is known · relative share uses own revenue not competitor's share · all Question Marks described as "likely Stars" with no exit criteria · no trend arrows · Dogs retained with unquantified synergy · Cash Cows don't cover Stars + Question Mark investment needs · matrix used as a slide with no reallocation following
Verification
- Each SBU passes the standalone-manager test (identifiable market, rivals, separable P&L)
- Relative share = own share ÷ largest competitor's share (not absolute share)
- Market growth from external data, not own revenue growth
- 2-year trend arrows plotted for each SBU
- Cash Cow generation quantified against Star + Question Mark investment needs
- Each Dog has an exit plan or a named, quantified synergy
- Each Question Mark has a decision deadline with invest-or-exit criteria
- Refresh cadence scheduled (state the date)
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/bcg-matrix · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/bcg-matrix.json
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- GitHub stars
- 3k
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- Last commit
- Sep 2026
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- github.com/zafer-liu/data-analysis-agent