AnalyticsBudget, forecasting and MMMIntermediateAnalyses your data

Marketing forecast and scenario planning with an assumption table

Builds a simple, auditable forecast with base, low and high cases driven by a visible assumption table, not hidden model magic.

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 forecasting analyst. Build a next 6 months forecast of monthly new-customer signups for a B2C subscription app using ONLY the history and assumptions below. History (period | value | notes such as promotions or outages): Month | Signups | Notes Apr | 4100 | May | 4300 | Jun | 3900 | summer dip Jul | 3700 | summer dip Aug | 3800 | Sep | 4600 | new campaign Planned changes and assumptions I want included: budget increase of 15% from November; new landing page in December. Seasonality knowledge: dip in summer, peak in January. Do this: 1. List all assumptions in a table (assumption, value, source, confidence) and mark which ones come from the data and which come from me. 2. Choose a simple, transparent method suited to the data length (for example a seasonal naive baseline or a linear trend with an explicit seasonal adjustment), explain why, and show the calculation steps so I can reproduce them in a spreadsheet. 3. Produce base, low and high scenarios, defining exactly what differs between them in terms of assumptions; present ranges rather than false precision. 4. Back-test the method on the last 3 periods of my history if there is enough data, and report the error you observe; if there is not enough data, say so. 5. List the largest risks and the signals I should monitor to know when the forecast is wrong. 6. List missing data. Rules: do not invent history, seasonality factors or growth rates; if something is not provided, ask. Label scenario outputs as scenarios, not predictions; patterns in history show correlation, not causes. Verify all arithmetic and recompute the back-test.

Customize

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

How far ahead.

What to forecast.

Context.

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

What will change.

What you know.

How many recent periods to hold out.

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.

An assumption table with sources and confidence, a transparent method with reproducible steps, base/low/high scenarios defined by assumption differences, a back-test result if data allows, risks and monitoring signals. (Illustrative.)

Expected format: Assumption table, method, scenarios, back-test, risks.

How to use it

  1. Rebuild the calculation in a spreadsheet to verify.
  2. Update assumptions as data arrives.
  3. Revisit scenarios monthly.

Limitations

  • Short or noisy histories give wide uncertainty.
  • Forecasts are conditional on assumptions; they are not guarantees.

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-038. We will correct or remove it.

Related prompts

  • AnalyticsIntermediateAnalyses 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.

    Any capable chat model (ChatGPT, Claude, Gemini, others) · Google Sheets / Excel

  • AnalyticsIntermediateAnalyses your data

    Budget pacing and impression-share analysis

    Checks whether a budget is on pace for the month and whether lost impression share is budget-limited or rank-limited, using the figures you provide.

    Google Ads · Any capable chat model (ChatGPT, Claude, Gemini, others)

  • AnalyticsIntermediateWrites a report

    Executive marketing report narrative with variance analysis

    Writes a concise executive narrative from your KPI table: what moved, by how much, plausible reasons labelled as hypotheses, and decisions needed.

    Any capable chat model (ChatGPT, Claude, Gemini, others) · Google Sheets / Excel