Dataset creation recommendations
These ideas are not implemented. They are discussion material for the data-modeling team, written for readers with approximately Advanced Placement Microeconomics and Macroeconomics background.
Add hidden affordability groups
Plain idea
Divide the simulated population into unobserved income or affordability groups, such as budget-constrained, middle-income, and affluent shoppers. Give each group a different response to product price.
Economics connection
In microeconomics, willingness to buy depends on preferences, while ability to buy depends on budget constraints. A 125-dollar skincare product consumes a much larger share of income for one household than another. A single national average hides this difference.
Example
Suppose a marketplace has 1,000 target shoppers:
- 500 budget-constrained shoppers have a 0.5 percent starting purchase probability;
- 350 middle-income shoppers have a 2 percent starting probability;
- 150 affluent shoppers have a 6 percent starting probability.
The simulator could let a price increase reduce demand more strongly for the first group. Advertising may then appear differently effective depending on who receives it.
Why this improves evaluation
An attribution method that assigns one average response to everyone may look accurate on the current homogeneous data but fail when audiences differ. Hidden groups test whether the method confuses audience composition with advertising effect.
Evidence needed before implementation
The team needs defensible segment shares, price-sensitivity ranges, and a decision about whether group membership may influence which advertisements are shown.
Main risk
Invented income groups can create impressive-looking but arbitrary results. Preserve them as explicit assumptions and run sensitivity analysis.
Sample target-audience membership
Plain idea
Instead of multiplying every count by one fixed target_audience_density, sample whether each simulated person belongs to the campaign's relevant audience.
Economics connection
A market is made of consumers with different tastes. Someone outside the relevant segment may have almost no demand even when exposed repeatedly.
Example
If campaign planning evidence estimates that 3 percent of reachable users match the target, the simulator can draw membership for each synthetic person. Members may have stronger baseline interest, while non-members may still buy occasionally.
Why this improves evaluation
It separates “the advertisement found interested people” from “the advertisement caused interest.” That distinction is central to selection bias in observational attribution.
Evidence needed before implementation
Amazon campaign reach forecasts, audience-overlap reports, or another approved source for segment prevalence and targeting accuracy.
Main risk
Treating target membership as perfectly observed would make the simulation easier than reality. The generator should distinguish true hidden membership from noisy platform labels.
Let economic mood evolve over time
Plain idea
Replace one month-long economic_willingness_multiplier with a time-changing hidden state.
Economics connection
Macroeconomic expectations, inflation concerns, pay cycles, and consumer confidence change over time. Consumption decisions respond to expected future income as well as current income.
Example
A regional mood state could begin neutral, decline after negative news, and recover gradually. Shoppers in every path would face the same daily state, creating correlated changes in conversion that advertising did not cause.
Why this improves evaluation
It tests whether an attribution method mistakes a common economic shock for the effect of whichever campaign happened to run on the same days.
Evidence needed before implementation
A source-dated consumer-confidence or retail-demand series, its reporting frequency, lag assumptions, and a calibration rule connecting index changes to conversion response.
Main risk
Economic indices are often revised, delayed, and much broader than the product category. The mapping should remain weak until validated.
Distinguish clicks, views, and timestamps
Plain idea
Create a versioned event-level schema that records whether a touchpoint was a view or click and when it occurred.
Economics connection
Information has different strength and depreciates over time. Clicking a product advertisement usually reveals stronger current intent than passively viewing a display advertisement, but the view may create awareness earlier.
Example
Two users can share the same ordered channel path:
- User A views display on Monday, clicks search on Tuesday, and buys Tuesday.
- User B views display in January, clicks search in March, and buys in April.
The current aggregated path treats them alike. A timestamp-aware simulator can make recent contacts more relevant while still allowing long-term awareness.
Why this improves evaluation
It supports time-decay, survival, recurrent-event, and sequence methods and reveals whether model performance depends on missing timing information.
Evidence needed before implementation
An approved event schema, time-zone rules, attribution windows, identity rules, and privacy constraints.
Main risk
Event-level data is larger and easier to misuse as person-level truth. It should be a new schema version, not a silent change to the finalized aggregated tables.
Add organic demand and competitor exposure
Plain idea
Simulate purchases that occur without the measured advertisements and advertising contacts from competitors or other channels.
Economics connection
Consumers choose among substitutes. Demand can also arise from brand loyalty, word of mouth, price changes, or offline marketing. A measured Amazon campaign is not the whole market.
Example
A shopper may already intend to buy after a friend's recommendation, then click a Sponsored Products advertisement only because it is the easiest route to checkout. A naive last-touch method gives the click all credit even though much of the demand was organic.
Why this improves evaluation
It creates realistic confounding and tests whether a method over-credits measured touchpoints merely because unmeasured causes are absent from the table.
Evidence needed before implementation
Ranges for organic conversion, competitor share of voice, substitution, and carryover. These may come from holdouts, brand-search trends, or approved market research.
Main risk
Unmeasured-channel assumptions are difficult to identify. Use scenario ranges rather than one supposedly correct number.
Calibrate against real reports and randomized holdouts
Plain idea
Fit simulation ranges to actual aggregate report distributions and use randomized experiments to anchor causal effects.
Economics connection
Observational correlation is not the same as causation. Random assignment creates comparable treatment and control groups, reducing the selection problem that people seeing advertisements may already differ from people who do not.
Example
If a randomized geographic holdout shows that advertising increases conversions by 4 percent, simulator touchpoint and interaction effects should collectively reproduce a plausible 4 percent increment—not an arbitrary 40 percent increment.
Why this improves evaluation
Calibration makes synthetic data resemble important features of the real environment, while holdouts constrain the total causal effect. Attribution methods can then be evaluated against scenarios that are less self-invented.
Evidence needed before implementation
Approved Amazon Marketing Cloud aggregates, Amazon Ads performance reports, experiment design, treatment assignment, confidence intervals, and privacy review.
Main risk
Matching summary statistics does not prove that hidden behavior is correct. Keep a held-out set of statistics and test multiple plausible mechanisms.
Suggested order
- Calibrate the existing baseline distributions so current assumptions have realistic ranges.
- Add target membership and affordability groups because they directly expose selection and price sensitivity.
- Add organic demand and competitor exposure to prevent measured advertising from explaining everything.
- Add evolving economic mood to test time-based confounding.
- Introduce clicks, views, and timestamps only through a new versioned schema.
- Anchor total causal lift with randomized holdouts whenever experiment data becomes available.
This order keeps each change explainable. Adding all hidden variables at once would make failures difficult to diagnose.
Questions for the data-modeling meeting
- Which hidden variable would most likely change the ranking of attribution methods?
- Which proposed parameter can be estimated from data the team can actually access?
- Which assumptions should be varied across a range rather than fixed?
- What real statistic will be held out to test calibration?
- Which schema change requires a new dataset version?
- What result would cause the team to reject a proposed mechanism?