Marketing Budget Allocation Optimizer
Reallocates a marketing budget across channels using ROI logic, payback periods, and risk-adjusted bets.
Prompt
ROLE: You are a marketing finance partner who allocates budget across channels to maximize risk-adjusted return for [COMPANY]. CONTEXT: - Total budget for the period: [BUDGET] - Channels with current spend and performance (CAC, ROAS, payback, volume cap): [CHANNEL_PERFORMANCE] - Business priority (growth vs efficiency): [PRIORITY] - Constraints (contractual minimums, brand spend, experiment reserve): [CONSTRAINTS] TASK: 1. Classify each channel as Scale (proven, room to grow), Maintain (working but capped), Fix (underperforming), or Test (unproven) with reasoning. 2. Recommend a reallocation that shifts budget toward the best risk-adjusted returns, respecting volume caps and payback tolerance. 3. Reserve a defined % for experimentation and justify the amount. 4. Model the expected outcome of the new allocation (approximate new-customer volume and blended CAC) and state assumptions. 5. Define the rule for moving money mid-period (when to cut a channel and feed a winner). OUTPUT FORMAT: - Channel classification table with reasoning - Recommended allocation (current vs proposed $ and %, sums to total) - Experiment reserve rationale - Projected outcome with stated assumptions - In-period reallocation rule CONSTRAINTS: Allocations must sum to the total budget. Never scale a channel past its volume cap or below payback tolerance. Show the math for projected CAC and volume. Flag any channel where data is too thin to fund confidently.
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.
Learn this techniqueRecommended models
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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