Campaign performance off target
Identifies where advertising or partner campaign results diverge from targets and forecasts. Provides a short list of recommended changes grounded in performance evidence.
— Sales & Growth
Campaign Optimisation Engine
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.
- Sales & Growth
- Human review built in
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~25 minutes
For one complete, review-ready pass
No coding required
- Input
- Recent advertising platform performance data
- Output
- Performance variance assessment against targets, forecasts and recent trends
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Campaign Optimisation Engine for
Mixed signals across channels
Assesses advertising and partner performance against consistent reference points, including each campaign’s recent trend. Gives teams a clearer basis for deciding where intervention is warranted.
Controlled live campaign adjustments
Applies only individually approved changes within fixed guardrails. Maintains a record of authorised adjustments so campaign decisions remain accountable.
Evidence of optimisation impact
Measures whether approved adjustments deliver the intended improvement. Provides an impact assessment to support decisions about retaining or revising campaign changes.
What it works from
- Recent advertising platform performance data
- Partner programme performance data
- Campaign targets
- Campaign forecasts and expected performance curves from prediction records in plain business terms
What you get back
- Performance variance assessment against targets, forecasts and recent trends
- Focused recommendations for campaign changes
- Approval-controlled campaign adjustments within agreed guardrails
- Record of approved and applied changes
— HOW IT BEHAVES
How Campaign Optimisation Engine produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
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.
Criteria-based analysis of every account
Each account is assessed against explicit criteria you control, so the same standard is applied across the pipeline in scope on every run.
Quantified during the run
Scores and derived figures for each account are calculated as part of the process rather than estimated afterwards.
Why this is expensive by hand
Live campaigns create a constant judgement problem: whether a performance gap calls for intervention or reflects normal variation. Campaign performance off target is the typical trigger — identifies where advertising or partner campaign results diverge from targets and forecasts. Provides a short list of recommended changes grounded in performance evidence. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1–2 weeks of manual work.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; performance variance assessment against targets, forecasts and recent trends comes out, alongside focused recommendations for campaign changes. 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. The practical effect: 1–2 weeks of manual work becomes a ~25 minutes run, held to an identical standard on the tenth engagement as on the first.
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 Optimisation 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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