Backtest creative-stopping rules before changing live spend
Compare historical threshold policies to understand the trade-off between wasted test spend and prematurely stopping eventual winners.
How this works
Compare historical threshold policies to understand the trade-off between wasted test spend and prematurely stopping eventual winners. A stopping rule can save spend while also cutting off creatives that would later become valuable. Historical replay must use only information available at each simulated decision time; using final outcomes to make earlier decisions produces misleadingly good results.
Reconstruct time-indexed creative histories and replay candidate threshold policies without look-ahead. Calculate saved exposure and lost observed value separately, then show explicit assumptions for any reinvestment scenario. Validate promising rules on a held-out period before proposing a bounded live test.
Before you start
Historical sequence
Obtain time-stamped spend and outcomes with enough detail to replay decisions.
Candidate policies
Specify thresholds, minimum observations, and decision cadence.
Value model
Define how outcomes and delayed qualification are valued without inventing revenue.
Prepare the inputs
- creative_history
- Required
Time-indexed rows
Retain cumulative values and outcome arrival times.
- policy_set
- Required
Versioned rules
Use only variables known at decision time.
- outcome_value
- Required
Approved values or labels
Keep unknown values unknown.
- holdout_period
- Required
Separate dates
Do not tune and evaluate on the same period only.
- reinvestment_assumptions
- Required
Optional scenarios
Keep hypothetical reuse of saved budget explicit.
Implementation, step by step
Validate replay data
- Check timestamps, cumulative resets, attribution delays, and creative identities.
- Exclude histories that cannot support the chosen decision cadence.
Checkpoint: A replay-ready dataset.
Define candidate rules
- Translate each policy into deterministic conditions and stage behavior.
- Record minimum data requirements and what happens at the budget cap.
Checkpoint: An executable policy specification.
Replay chronological decisions
- At each simulated check, expose only observations available by that time.
- Record the first stop point and retain the full observed history for later outcome comparison.
Checkpoint: A decision trace per creative and policy.
Calculate trade-offs
- Measure spend after the simulated stop and later observed outcomes that would be missed.
- Do not assume all saved spend can be redeployed at the same efficiency.
Checkpoint: A transparent trade-off table.
Test robustness
- Compare results across periods, segments, and a held-out sample.
- Inspect rules that succeed only because of a few unusual creatives.
Checkpoint: A robustness review.
Recommend a bounded live test
- Select a policy only after the owner reviews premature stops and assumptions.
- Define exposure limits and monitoring before any live application.
Checkpoint: A reviewed policy-test proposal.
Decisions along the way
Outcome arrives after its originating click
Use the information availability rule consistently and document the lag.
Value per outcome is unknown
Report outcomes and spend separately rather than inventing profit.
Rule performs poorly on holdout
Do not promote it based on the training period.
What you will produce
stopping-rule-backtest.csv
CSVSupports inspection of trade-offs.
Include: policy · creative_id · stop_time · saved_spend · later_outcomes · limitations
policy-comparison.md
MarkdownFrames the next experiment honestly.
Include: data_scope · assumptions · holdout_results · recommendation · live_test_limits
Check the result
- Audit a few replay decisions for look-ahead leakage.
- Reconcile spend and outcome totals with the raw history.
- Separate observed results from reinvestment assumptions.
- Inspect false-stop examples and holdout performance.
When something goes wrong
Only final aggregate metrics exist
Do not claim a chronological backtest; request time-series data or narrow the analysis.
Policy uses a later qualification label early
Correct the timing and rerun.
A few outliers drive the conclusion
Report sensitivity and avoid a universal rule.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Compare a stop-after-50-spend policy with current practice.
Inputs
Synthetic example. Synthetic C1 has no known conversion at spend 50 but receives a delayed qualified outcome later. C2 never produces an outcome.
Expected result
Synthetic expected result. The replay records both the potential saving and C1's missed later value. It does not use C1's final result to pretend the early rule would have known better.
Run a small pilot
Start small
Historical replay followed by a small capped live comparison only if approved.
Record the baseline
Record current test spend, eventual outcomes, and manual stopping decisions.
Agree success criteria
Agree acceptable premature-stop trade-offs before judging the rule.
Stop or revise
Stop live use when tracking or chronology cannot support the policy.
Keep the scope clear
- Historical replay does not establish causal live performance.
- Do not change campaign rules or spend without the authorized test decision.
Check before using
Historical counterfactuals rely on assumptions and may contain leakage. Require enough data, account for attribution delays, and do not treat modeled savings as achieved results.
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: Creative-level performance history · Current stopping rules · Winner definition and reinvestment assumptions
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow