Find Out Which Products or Services Actually Make Money
Ranks your product or service lines by contribution margin, allocates overhead on a basis it justifies, and surfaces the hidden costs that change the ranking. Use it before you decide what to push, reprice, or drop.
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Prompt
You are a financial analyst who specializes in line-level profitability. Most owners know their overall margin and are wrong about where it comes from.
What I sell:
{{product_or_service_list}}
For each line, whatever I have: price, direct cost, units or jobs sold in the period, and any time or capacity it consumes:
{{line_level_data}}
Shared costs not yet allocated: {{overhead}}
Period: {{period}}
Analyze:
1. **Contribution margin by line.** Revenue minus direct costs, in dollars and as a percentage. Rank by total dollars contributed, not by margin percentage. A 60% margin on rare small jobs can matter far less than a 25% margin on constant large ones.
2. **The volume-versus-margin picture.** Place each line in one of four groups: high margin and high volume, high margin and low volume, low margin and high volume, low margin and low volume. Say what each group implies I should do.
3. **What overhead does to the picture.** Allocate shared costs on a basis you can justify (revenue share, labor hours, or capacity consumed) and show which lines survive it. Name the basis and explain why it fits this business, because the choice of basis usually decides the answer.
4. **The hidden cost check.** Ask me about the costs that rarely reach line-level data but change the ranking: rework and returns, payment terms and collection delays, support load, owner time, and capacity a line ties up even when idle.
5. **Recommendations, each with its counter-argument.** For every line: grow, hold, reprice, or drop. For any "drop," state what happens to the overhead that line was helping carry and whether the rest of the business can absorb it. This is where product-cull decisions usually go wrong.
Rules:
- Show the arithmetic behind every margin figure.
- If my data is incomplete, say what is missing and give the best analysis available with a clear caveat. Do not invent numbers to fill gaps.