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Assortment cannibalization detector

Compares a new or proposed product against an existing assortment to flag positioning overlap, quantify cannibalization risk by SKU, and recommend differentiation angles, repricing, or repositioning moves. Use when…

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Purpose

Compare a new or proposed product against the existing assortment to surface where it overlaps with current SKUs, estimate how much of its volume is incremental versus stolen from your own shelf, and prescribe specific differentiation, pricing, or positioning moves to protect portfolio margin. Stops a launch from quietly trading high-margin sales for low-margin ones.

When to Use

Invoke this skill when the user asks to:

  • Evaluate whether a new product will cannibalize existing SKUs before launch
  • Check positioning overlap between a line extension and the current lineup
  • Audit an assortment for products that are competing with each other
  • Decide whether to launch, reposition, or kill a proposed SKU
  • Find a differentiation angle that protects an incumbent product
  • Justify a launch decision to leadership with a cannibalization estimate

Inputs Required

Before starting, collect:

  • New product: Name, core benefit, price, and key specs/features
  • Existing assortment: List of current SKUs with price, primary benefit, and (if known) revenue or unit share
  • Target customer for the new product: Who it's meant to win
  • Strategic intent: Incremental growth / trade-up / defend against competitor / fill a gap
  • Margin context: Approximate gross margin of the new product vs. incumbents (or relative ranking if exact numbers are unavailable)
  • Channel(s): Where it sells (DTC, retail, marketplace) — affects shelf adjacency
  • Constraint: Any fixed decisions (price is locked, must launch by date, etc.)

If revenue/share or exact margins are unavailable, work from relative rankings and flag the assumption in the output.

Instructions

Step 1: Build the Positioning Map

Place the new product and every existing SKU on two axes that matter most for this category. Default axes:

  • Price (low → high)
  • Primary benefit / use case (cluster SKUs by the job they're hired to do)

For each SKU, record: price, primary benefit, target customer, and approximate share. Identify which existing SKUs occupy the same region as the new product — these are the cannibalization candidates. A SKU is a candidate if it shares two of three: price band (within ~15%), primary benefit, and target customer.

Step 2: Score Overlap Per Candidate SKU

For each cannibalization candidate, score three dimensions on a 1–5 scale (5 = highest overlap):

  • Price proximity: How close are the price points? Closer = more substitutable
  • Benefit overlap: Do they solve the same core job, or adjacent jobs?
  • Audience overlap: Same buyer, or different buyer who happens to be browsing?

Sum to an Overlap Score (3–15). Map to a risk tier:

  • 12–15: High — likely a true substitute; expect significant cannibalization
  • 8–11: Medium — partial overlap; some switching, some incremental
  • 3–7: Low — adjacent, mostly incremental

Step 3: Estimate the Cannibalization Split

For each candidate, estimate what portion of the new product's volume is incremental (net-new demand) versus cannibalized (switched from the incumbent). Use the risk tier as the anchor:

  • High overlap → 50–80% of overlapping volume is cannibalized
  • Medium → 25–50%
  • Low → 5–25%

Then apply the margin test: cannibalization only hurts if you're trading down on margin. For each candidate, classify the trade:

  • Accretive cannibalization: New product has higher margin or AOV → switching is good, lean in
  • Neutral cannibalization: Comparable margin → volume shift is a wash
  • Destructive cannibalization: New product has lower margin → you're funding your own decline; this is the one to fix

Step 4: Recommend Differentiation Angles

For each Medium and High candidate, prescribe at least one concrete move to widen the gap. Pull from these levers:

  • Reprice: Move the new product into a clear higher or lower tier (specify the target price band)
  • Re-benefit: Sharpen messaging on a distinct primary benefit the incumbent doesn't own
  • Re-segment: Aim the new product at a different buyer or occasion
  • Re-feature: Add or remove a feature to create a real functional ladder
  • Sequence/sunset: If overlap is irreducible and cannibalization is destructive, recommend repositioning or retiring the incumbent rather than launching into a collision

Each recommendation must name the specific incumbent it protects and state the expected effect on overlap.

Step 5: Render the Launch Verdict

Issue one clear verdict and defend it:

  • Launch as-is — overlap is low or cannibalization is accretive
  • Launch with changes — list the required differentiation moves from Step 4
  • Reposition before launch — current positioning collides destructively; fix first
  • Don't launch — incremental volume doesn't justify the margin damage

Be opinionated. State the single biggest risk and the single biggest opportunity in one sentence each.

Output Format

  • Structure: Positioning map (described and/or table) → per-SKU overlap scoring table → cannibalization split + margin classification → differentiation recommendations → launch verdict
  • Length: 800–1,100 words
  • Format: Tables for the overlap scoring and cannibalization split; prose for recommendations and verdict
  • Sections: Positioning Map → Overlap Scoring → Cannibalization & Margin Assessment → Differentiation Recommendations → Launch Verdict
  • Tone: Merchandising director — analytical, portfolio-minded, willing to say no to a launch

Quality Checklist

Before delivering output, verify:

  • [ ] Every existing SKU was evaluated as a potential cannibalization candidate, not just the obvious one
  • [ ] Each candidate has a numeric Overlap Score (3–15) and a mapped risk tier
  • [ ] Each candidate is classified as accretive, neutral, or destructive — not just "some overlap"
  • [ ] Every Medium/High candidate has at least one specific differentiation move that names the SKU it protects
  • [ ] The launch verdict is one of the four defined options — no hedging
  • [ ] Any estimate built on missing margin/share data is flagged as an assumption

Notes

  • The margin test is the whole game: cannibalizing a low-margin SKU with a high-margin one is a win, and many "cannibalization" fears are actually trade-up opportunities.
  • The most dangerous overlap is invisible on a spec sheet — two SKUs with different features that the customer perceives as solving the same job. Weight audience and benefit overlap over feature lists.
  • If three or more incumbents score High against the new product, the problem is usually the assortment, not the launch — recommend a portfolio rationalization pass.
  • Revisit this analysis 60–90 days post-launch with actual unit data to calibrate future cannibalization estimates.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills