Review advertising budgets using forecasts
Analyze current advertising performance and forecast assumptions to propose budget changes for a media buyer to approve.
How this works
Analyze current advertising performance and forecast assumptions to propose budget changes for a media buyer to approve. Budget allocation combines observed performance with assumptions about what additional spend can produce. A campaign's average cost so far does not prove its next increment will perform equally well, especially when audiences overlap or outcomes arrive late.
Prepare a bounded budget proposal using comparable account data and explicit forecast assumptions. Calculate current measures and scenario totals deterministically. The assistant explains trade-offs and missing evidence; an authorized buyer decides whether to apply changes and monitors the actual result.
Before you start
Planning objective
Define the business outcome, total budget, and allocation horizon.
Comparable data
Align currencies, attribution windows, and outcome maturity.
Change constraints
Specify minimum commitments, maximum changes, and ownership.
Prepare the inputs
- performance
- Required
Campaign-period table
Include spend, mature outcomes, and relevant quality measures.
- current_budgets
- Required
Account settings
Distinguish daily budgets from lifetime caps.
- forecast_assumptions
- Required
Scenario inputs
Label uncertain marginal performance.
- constraints
- Required
Numeric and operational rules
Include channel limits and existing commitments.
- recent_actions
- Required
Change history
Avoid evaluating a campaign before its last change can be observed.
Implementation, step by step
Validate the planning snapshot
- Freeze the performance and budget extracts with their dates.
- Flag incomplete attribution and incompatible conversion definitions.
Checkpoint: A comparable planning dataset.
Calculate current efficiency
- Compute cost and outcome measures at the appropriate campaign or channel grain.
- Show missing outcomes explicitly and reconcile totals with the account export.
Checkpoint: Verified current-state measures.
Build bounded scenarios
- Compare maintaining budgets with specific increases, reductions, or reallocations.
- Use explicit assumptions for additional spend rather than extending historical averages invisibly.
Checkpoint: A scenario table with assumptions.
Inspect constraints and interactions
- Check total spend, audience overlap, learning periods, and operational capacity.
- Reject proposals that violate a cap or depend on unavailable fulfillment capacity.
Checkpoint: A feasible allocation shortlist.
Draft the buyer proposal
- Explain the proposed delta, evidence, uncertainty, and observation period for each change.
- Prepare settings for review without claiming an account update has occurred.
Checkpoint: A precise approval queue.
Reconcile observed outcomes
- Record applied settings and later mature results against the scenario.
- Separate forecast error from execution differences and concurrent changes.
Checkpoint: A review note for the next allocation cycle.
Decisions along the way
A campaign's outcomes are delayed
Use an earlier mature window or defer its reallocation.
All scenarios exceed the total cap
Reduce or remove proposals before presenting an actionable plan.
Capacity cannot absorb more leads
Reflect the constraint instead of optimizing only acquisition cost.
What you will produce
budget-review.csv
CSVSupports specific allocation decisions.
Include: campaign_id · current_budget · proposed_budget · assumption · expected_range · owner · status
budget-scenarios.md
MarkdownExplains why a bounded change is worth testing.
Include: objective · constraints · scenarios · tradeoffs · review_date
Check the result
- Reconcile proposed totals with the budget ceiling.
- Verify daily and lifetime settings are not mixed.
- Check every forecast has explicit assumptions.
- Confirm applied settings match the approved proposal.
When something goes wrong
Forecast treats average efficiency as guaranteed marginal efficiency
Add a sensitivity range and state the uncertainty.
A budget update has unknown status
Inspect account state before retrying.
Observed results use a different attribution window
Rebuild the comparison with aligned definitions.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Review a weekly budget split across two campaigns.
Inputs
Synthetic example. Synthetic A spends 500 for 25 mature leads; B spends 500 for 10. Total next-week cap is 1,000.
Expected result
Synthetic expected result. Propose a bounded reallocation scenario, such as 600/400, with a stated marginal-efficiency assumption. Do not promise 30 leads from A simply by scaling its average.
Run a small pilot
Start small
A small allocation change with a defined observation window.
Record the baseline
Record current spend, mature outcomes, and buyer planning effort.
Agree success criteria
Agree acceptable forecast error and operational constraints before applying changes.
Stop or revise
Stop if tracking breaks, limits are exceeded, or lead quality falls outside the agreed review criteria.
Keep the scope clear
- Recommendations do not authorize live spend changes.
- Do not claim causal improvement from a single uncontrolled reporting period.
Check before using
Validate attribution and data quality. Establish spending limits and a rollback path; do not generalize reported returns.
Business activities
- Run advertising and acquisition tests
- Review business performance
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Campaign performance data · Targets and forecast methods · Budget and account constraints
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow