AnalyticsBudget, forecasting and MMMIntermediateAnalyses your data

Budget reallocation scenarios with saturation caveats

Builds transparent what-if scenarios for moving budget between channels, with simple assumptions about diminishing returns clearly labelled.

For: Performance marketers, Marketing analysts, Growth marketers · Works with: Any capable chat model (ChatGPT, Claude, Gemini, others), Google Sheets / Excel

Public beta. This prompt was drafted with AI assistance and checked by automated rules, but it has not been reviewed or tested by a person yet. Treat it as a starting point and check the output. It is hidden from search engines while in beta. Use the “Was this prompt useful?” box to tell us what works.

Your prompt

Act as a media-planning analyst. Build budget reallocation scenarios using ONLY the data below. Current period data (channel | spend | conversions | notes on saturation or constraints): Channel | Spend | Conversions | Notes Paid search | 20000 | 520 | impression share maxed on brand Paid social | 15000 | 260 | frequency rising Affiliates | 5000 | 140 | capped by partner volume Total budget: 40000 USD. Objective: maximise conversions at a CPA no worse than today. Constraints: brand search must keep full coverage; affiliates capped at 6000. Tasks: 1. State assumptions explicitly, especially about how returns change when spend changes. Since I did not give response curves, use a simple, clearly labelled assumption (for example returns diminish as spend increases at a rate that I can change) and show the formula; list it as an assumption to be validated, not a fact. 2. Compute current efficiency per channel (cost per conversion) and verify. 3. Present 3 scenarios (conservative shift, moderate shift, aggressive shift) with the allocation, the assumed marginal efficiency, and the resulting estimated conversions under the stated assumption; give a range, not a point estimate. 4. Highlight what would invalidate each scenario (new channels, creative fatigue, auction changes, seasonality, conversion lag). 5. Recommend how to test the shift safely (small step, time-boxed, with a stop rule) and what to monitor. 6. List missing data (marginal returns, saturation tests, channel interactions). Rules: do not invent response curves, ROI or benchmark data; label every projection as scenario output, not forecast. If data is missing or not provided, say so. Verify the arithmetic so allocations sum to the total budget. Do not claim causation from channel-level correlations.

Customize

The fields start with example values so you can see how the prompt works. Everything stays in your browser.

Paste your table. The default is small fictional sample data so the preview works; replace it with your own.

Budget to allocate.

Currency.

What to optimise.

Hard limits.

Example of what to expect

Illustrative only. It describes the kind of result this prompt aims for; real output varies by tool, model and run.

Assumptions with an editable diminishing-returns rule, current efficiency per channel, three scenarios with ranges (not point forecasts), what would invalidate each, a safe test plan and missing data. (Illustrative.)

Expected format: Assumptions, efficiency table, three scenarios, invalidators, test plan.

How to use it

  1. Change the diminishing-returns assumption and re-run.
  2. Test shifts in small steps.
  3. Check marginal returns with experiments.

Limitations

  • Scenarios rest on assumptions, not measured response curves; they are not forecasts.
  • Channel interactions are ignored.

Platform notes

Any capable chat model (ChatGPT, Claude, Gemini, others)

Official docs read · checked 2026-10-11

Works in any capable chat model. Paste a small anonymised sample and recompute the key numbers yourself: chat models can misread columns or miscalculate, and they cannot run your statistical tests reliably.

General notes on Any capable chat model (ChatGPT, Claude, Gemini, others)
  • These prompts are plain text and work in any chat assistant. For analytics prompts, paste a small, anonymised export; a chat model can misread columns or miscalculate, so recompute key numbers yourself.

Google Sheets / Excel

Not verified: third-party guidance only · checked 2026-10-11

Test any formulas on a small range first; syntax depends on locale and version.

General notes on Google Sheets / Excel
  • Formulas are written for common functions in both products. Locale settings (decimal and argument separators) can change what you must type.

Source, license and attribution

Origin
Original by MarketerTools
Publisher
MarketerTools
License
Original work by MarketerTools, free to copy and use. Informed by the linked documentation; no third-party text is reproduced.
Platform assumptions checked
2026-10-11

Documentation and references behind this prompt

These informed the structure and the platform notes. A reference is not a license, and no third-party prompt text is copied here.

Spotted an attribution error, or are you a source owner with a request? Use the chat button at the bottom right and quote prompt ID ana-037. We will correct or remove it.

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