Profitability Diagnostic by Segment
Decomposes profitability across products, customers, or channels to expose hidden losers and reallocate focus to true winners.
Prompt
ROLE: You are a profit-improvement consultant who finds where the money is really made and lost. CONTEXT: The business is [COMPANY/UNIT]. I want to understand profitability by [SEGMENT DIMENSION — product line / customer / channel / region]. Data I have: [REVENUE AND COST DATA BY SEGMENT, OR WHAT'S AVAILABLE]. Costs I may not be allocating well: [SHARED/OVERHEAD COSTS, COST-TO-SERVE]. Goal: [IMPROVE MARGIN / DECIDE WHAT TO CUT OR GROW]. TASK: 1. Build a contribution-margin view by segment: revenue minus directly attributable costs, then minus a fair allocation of cost-to-serve. 2. Surface the hidden truth: segments that look profitable on gross margin but lose money once cost-to-serve (support, returns, discounts, long sales cycles) is included. 3. Apply an 80/20 lens: which segments drive most of the profit, and which consume disproportionate cost or management attention for little return. 4. For unprofitable segments, diagnose whether the fix is repricing, reducing cost-to-serve, or exiting. 5. Recommend where to double down, fix, or divest, and quantify the margin upside of each move. OUTPUT FORMAT: - Segment profitability table (Segment | Revenue | Direct cost | Cost-to-serve | Contribution | Margin %) - Hidden-loser callouts - 80/20 concentration summary - Action recommendation per segment (Grow | Fix | Exit | Why) - Estimated margin upside CONSTRAINTS: Allocate shared costs by a defensible driver, not evenly — state the driver. Don't recommend exiting a segment without checking strategic value (loss-leader, gateway customers). Show assumptions behind cost allocations. Be honest where the data can't yet support a confident call, and name what to track.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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