Run a supervised search for a better churn model
Let a coding assistant explore candidate prediction models against an agreed evaluation objective. Collect the experiment results and hand the selected model to engineers for validation and deployment review.
How this works
Let a coding assistant explore candidate prediction models against an agreed evaluation objective. Collect the experiment results and hand the selected model to engineers for validation and deployment review. Use this to compare predictive approaches for a clearly defined churn event. The experiment should establish whether a candidate improves the agreed evaluation objective on unseen data, not merely fit historical labels better.
Define churn and the prediction horizon, construct time-appropriate features, and split data to prevent future information leakage. Compare candidates with the current baseline under a fixed metric and resource budget. Package the selected experiment for engineering validation rather than deploying it automatically.
Before you start
Churn definition
Agree event, observation window and prediction horizon.
Permitted dataset
Obtain customer history and labels with timestamps and access limits.
Evaluation plan
Choose baseline, metric, holdout and operational constraints.
Prepare the inputs
- customer_events
- Required
entity ID, timestamps and permitted features
Exclude post-outcome information from prediction-time features.
- labels
- Required
churn status and event date
Verify label consistency and censoring rules.
- experiment_config
- Required
splits, metric, seed and resource budget
Keep comparisons reproducible.
- baseline_model
- Required
current method and performance evidence
Use the same evaluation population.
Implementation, step by step
Validate the prediction task
- Define when a prediction is made and what later event counts as churn.
- Check class balance, label quality and unavailable outcomes.
Checkpoint: The target is operationally meaningful.
Build leakage-safe data
- Create features using only information available at prediction time.
- Split by time or customer as appropriate and keep the holdout untouched.
Checkpoint: The dataset supports a fair comparison.
Run bounded experiments
- Try justified feature and model variations within the agreed budget.
- Record code, parameters, artifacts and failures for each run.
Checkpoint: Experiments can be reproduced.
Evaluate beyond one score
- Compare the agreed metric, calibration and relevant subgroup or threshold behavior.
- Inspect errors and operational tradeoffs rather than select by training accuracy.
Checkpoint: The candidate's strengths and limits are visible.
Prepare handoff
- Package the selected model, feature contract and evaluation evidence.
- Ask engineering to validate deployment behavior and monitoring needs separately.
Checkpoint: The recommendation is ready for review.
Decisions along the way
Labels include unresolved customers
Apply the agreed censoring rule rather than label them retained.
Candidate improves average score but misses costly cases
Show the tradeoff under the business metric.
Training data contains future information
Remove the leakage and rerun the comparison.
What you will produce
churn-experiment-report.md
MarkdownA supervised model comparison.
Include: definition · splits · baseline · candidates · errors · limits
candidate-model-package
Model artifact and metadataA reproducible candidate for engineering review.
Include: features · parameters · training_window · validation · version
Check the result
- Audit feature timestamps against prediction time.
- Verify holdout separation and customer leakage handling.
- Recompute metrics on the same population.
- Inspect calibration and threshold consequences relevant to the business.
When something goes wrong
Data schema changes
Stop affected runs and repair the feature contract.
Experiment exceeds budget
End the run and retain partial evidence without inventing results.
Holdout was used during tuning
Create a fresh evaluation set before claiming generalization.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Synthetic: compare churn models for a 30-day horizon.
Inputs
Baseline recall at an agreed review capacity is 0.60; candidate is 0.68 on a future holdout. A feature records cancellation requests made after prediction time.
Expected result
Synthetic result: reject the contaminated comparison, remove the future feature and rerun. Do not report the apparent gain as validated improvement.
Run a small pilot
Start small
Use one product and a bounded historical period with a genuine holdout.
Record the baseline
Record baseline errors and review-capacity constraints.
Agree success criteria
Agree metric and operational acceptance criteria before model selection.
Stop or revise
Stop conclusions when labels, leakage controls or comparable splits are invalid.
Keep the scope clear
- Prediction scores do not establish why a customer will leave.
- Experimentation does not authorize deployment or automated customer treatment.
Check before using
Use a held-out dataset and compare the same metric with the current baseline. Any performance gains require separate validation.
Business activities
- Plan products and business changes
- Build and change software
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: Customer behavior data · Existing prediction baseline and evaluation metric
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow