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.
— Sales & Growth
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.
2–3 weeks → ~25 minutes
Including the external research pass
No coding required
— USE CASES
Test a proposed campaign against comparable past activity to see whether the expected return is realistic before money and team time go in.
Every campaign is assessed on the same criteria, so approvals do not depend on who happened to review the plan.
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.
Each call is kept alongside its reasoning, so predictions can be compared with what actually happened and the criteria tightened over time.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
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.
The run pauses for a person to confirm the accounts that matter before the deliverable is composed.
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.
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.
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.
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.
Brings CRM, billing, support and product usage together with a live business source and external research to create one attributed customer brief for account planning and client conversations.
Evaluates live advertising and partner campaign performance against targets, forecasts and recent trends, producing focused recommendations with approval-controlled changes and a record of their impact.
Keeps a standing weekly read on the health of every client account, scoring each one on the signals that actually predict trouble, tracking how it moves over time and putting the accounts that need attention in front of the person who owns them.
Combines NPS movement, ticket velocity, usage drop-off and billing changes into a churn score per account, then drafts the save play with the ARR at risk quantified.
Tests every open deal for stalled activity, unholdable close dates, single-threading and missing next steps, then computes the forecast dollars at risk and an action list per rep.
Codes every closed-lost deal to a root cause, themes the language buyers actually used, and sums lost ARR per cluster so the revenue leakage behind each pattern is a real number.