Explain attribution through an interactive marketing simulation
A team described an AI-built game that lets prospects manage three ad campaigns with and without attribution data. The comparison illustrates why additional evidence can change spending decisions.
How this works
A team described an AI-built game that lets prospects manage three ad campaigns with and without attribution data. The comparison illustrates why additional evidence can change spending decisions.
Build an educational simulation in which the same campaign data can be viewed with different attribution evidence. Make the assumptions inspectable so a changed decision illustrates the value of information rather than disguising a predetermined sales claim.
Before you start
Defined operating scope
Prospects need to understand how attribution evidence can change marketing decisions.
Required source material
Obtain Three campaign scenarios; Example results with and without attribution. If connectors are unavailable, use authorized files with the same fields.
Decision and exception rules
The richer view changes the underlying data as well as visibility. Correct the comparison so only available information differs.
Prepare the inputs
- campaign_scenarios
- Required
Three fictional campaigns and outcomes
Label all values synthetic and define the time window.
- decision_rules
- Required
Available allocation choices and constraints
Specify the total budget and allowed actions.
- measurement_views
- Required
Limited and richer attribution fields
Use the same underlying scenario in both views.
- simulation_model
- Required
Deterministic calculations and explanation
State how decisions affect displayed outcomes.
Implementation, step by step
Establish the working record
- Confirm the selected records, effective versions and responsible owner.
- Check campaign_scenarios, decision_rules, measurement_views, simulation_model before beginning. Record the source version and the selected scope; an unavailable value stays unknown rather than becoming an inferred fact.
Checkpoint: The input record identifies the selected campaign_scenarios and any missing required fields.
Define the campaign decisions available to the player
- Define the player’s allocation options, budget constraints and the learning question before building the interface.
- Recalculate every displayed result and reconcile budget and unique-outcome totals.
Checkpoint: A saved intermediate result for: Define the campaign decisions available to the player.
Build contrasting views with different levels of measurement
- Create bounded contrasting views that hide or reveal attribution fields without changing underlying facts; test calculations and reset behavior.
- Test a neutral scenario, reset state and a decision at each budget boundary.
Checkpoint: A saved intermediate result for: Build contrasting views with different levels of measurement.
Let the player compare decisions and outcomes
- Let the player compare decisions and explain which additional evidence changed the recommendation, including situations where it does not.
- Recalculate every displayed result and reconcile budget and unique-outcome totals.
Checkpoint: A saved intermediate result for: Let the player compare decisions and outcomes.
Reconcile the proposed result
- Recalculate every displayed result and reconcile budget and unique-outcome totals.
- Test a neutral scenario, reset state and a decision at each budget boundary.
Checkpoint: The listed discrepancies have a named reviewer or an explicit unresolved status.
Package the result and unresolved work
- Check the simulation’s assumptions and explain that its outcomes are illustrative. The recording gives only a brief account, not a complete implementation.
- All outcomes are illustrative and must remain labeled synthetic.
Checkpoint: An interactive demonstration of the product’s analytical value. Unresolved items remain visible in the review record.
Decisions along the way
The richer view changes the underlying data as well as visibility.
Correct the comparison so only available information differs.
A scenario guarantees the promoted product always wins.
Add a neutral or contrary case and explain the assumptions.
The calculation violates budget or attribution totals.
Stop the demo result and fix the model before sharing.
What you will produce
demonstrate-marketing-attribution-with-simulation-result.json
JSONAn interactive demonstration of the product’s analytical value. Label proposed and completed states separately.
Include: scenario_id · budget_allocation · measurement_view · underlying_outcomes · decision_result · assumptions · status · evidence_reference · review_note
demonstrate-marketing-attribution-with-simulation-review.md
MarkdownKeep the evidence and unresolved decisions needed to apply this result, including exceptions involving simulation_model.
Include: source_reference · question · owner · next_action
Check the result
- Recalculate every displayed result and reconcile budget and unique-outcome totals.
- Test a neutral scenario, reset state and a decision at each budget boundary.
- Inspect campaign_scenarios and decision_rules in both the original and output; confirm that record identity and interpretation survived the transformation.
- Check the simulation’s assumptions and explain that its outcomes are illustrative. The recording gives only a brief account, not a complete implementation.
When something goes wrong
The richer view changes the underlying data as well as visibility.
Correct the comparison so only available information differs. Retain the affected evidence and recovery status.
A scenario guarantees the promoted product always wins.
Add a neutral or contrary case and explain the assumptions. Retain the affected evidence and recovery status.
The calculation violates budget or attribution totals.
Stop the demo result and fix the model before sharing. Retain the affected evidence and recovery status.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Explain attribution through an interactive marketing simulation for the following bounded example; prepare the result for review.
Inputs
Synthetic example: Synthetic campaigns A, B and C each spend 100 units. Last-click conversions are 8, 4 and 2; the richer view adds documented assist counts without treating assists as extra unique customers.
Expected result
Synthetic expected result: Display both views of the same 300-unit spend. Explain how overlap affects interpretation; do not add assisted and last-click conversions as unique conversions.
Run a small pilot
Start small
Trial a representative batch containing the ordinary case and the exception demonstrated above.
Record the baseline
Record current preparation time, review effort and correction types for the same records.
Agree success criteria
Agree acceptable review and error limits; measure prospect comprehension in a reviewed session; simulation logic accuracy.
Stop or revise
The calculation violates budget or attribution totals. Pause affected records and apply this recovery: Stop the demo result and fix the model before sharing.
Keep the scope clear
- All outcomes are illustrative and must remain labeled synthetic.
- Generated code stays within the demo; it must not connect to or change live advertising accounts.
Check before using
Check the simulation’s assumptions and explain that its outcomes are illustrative. The recording gives only a brief account, not a complete implementation.
Business activities
- Run sales conversations and proposals
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: Three campaign scenarios · Example results with and without attribution
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow