— Sales & Growth

Campaign Prediction Engine

Judges a planned campaign before spend is committed, testing its audience, offer, channel mix and budget against how comparable campaigns actually performed, then giving a clear go, amend or hold call with the reasoning and confidence behind it.

  • Sales & Growth
  • Human review built in
  • Traceable reasoning
  • Runs anywhere
Preview methodology

2–3 weeks → ~25 minutes

Including the external research pass

No coding required

Input
The proposed campaign: audience, offer, channels, timing and budget
Output
A clear go, amend or hold recommendation with the reasoning stated
Runs in
Skillsize · ChatGPT · Claude · Copilot
Export
SKILL.md · MCP
Time saved
~2–3 weeks per run

— USE CASES

What people use Campaign Prediction Engine for

A view before the budget is committed

Test a proposed campaign against comparable past activity to see whether the expected return is realistic before money and team time go in.

Consistent calls across teams

Every campaign is assessed on the same criteria, so approvals do not depend on who happened to review the plan.

Amendments, not just rejections

Where the plan is close, the weak elements are named — audience, offer, channel weighting or budget level — so it can be improved rather than dropped.

A record you can learn from

Each call is kept alongside its reasoning, so predictions can be compared with what actually happened and the criteria tightened over time.

What it works from

  • The proposed campaign: audience, offer, channels, timing and budget
  • Performance history from comparable past campaigns
  • Your own thresholds for expected return and acceptable risk

What you get back

  • A clear go, amend or hold recommendation with the reasoning stated
  • The evidence and comparable campaigns the call rests on, with confidence noted
  • The specific changes that would improve a borderline plan, and a record of the decision for sign-off

— HOW IT BEHAVES

How Campaign Prediction Engine produces its result

The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.

Live research on the pipeline in scope

Current external sources on the pipeline in scope are researched during the run rather than recalled from training data, and every source travels with the output.

Human sign-off before the write-up

The run pauses for a person to confirm the accounts that matter before the deliverable is composed.

Composed as work product

Findings on the pipeline in scope are written up as a document that reads like professional output, with each claim tied back to a account.

Why this is expensive by hand

Campaign decisions are usually made on conviction and a rough read of past results, with the evidence scattered across old reports and nobody's memory quite matching. In practice it shows up as a view before the budget is committed: test a proposed campaign against comparable past activity to see whether the expected return is realistic before money and team time go in. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 weeks of senior time whenever it comes round.

How this Skill produces it

Here the same job runs as a Skill. Your material goes in; a clear go, amend or hold recommendation with the reasoning stated comes out, alongside the evidence and comparable campaigns the call rests on, with confidence noted. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. Net effect: 2–3 weeks down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.

Who it's for

  • Revenue and commercial leaders
  • Sales operations and enablement teams
  • Customer success and account management leads
  • Consultants advising on go-to-market

Run it in Skillsize — or export it anywhere

Campaign Prediction Engine exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.

ChatGPTClaudeCopilotAI Products (MCP)
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