Turn campaigns from broad blasts into precise, incremental impact machines. Check how well you can rank for lift, read Qini curves, and avoid sleeping dogs.
A negative uplift score for a customer suggests the best campaign decision is to ______.
Suppress the treatment for that customer
Randomly assign them to treatment
Increase discount depth immediately
Exclude them from all future analytics
Which curve is commonly used to evaluate uplift models by plotting incremental gains versus the targeted fraction?
Cumulative lift chart for probability models
Qini curve (or uplift curve)
Precision–recall curve
ROC curve
The two-model approach for uplift trains ______.
A clustering model to group lookalike audiences
Only a propensity model and no outcome models
Separate models for treated and control groups and uses the difference in predictions
A single model with treatment as a feature and ignores interactions
Including treatment–feature interactions in a single model supports uplift because it ______.
Eliminates the need for a control group
Forces linear relationships for interpretability
Removes multicollinearity among features
Captures heterogeneous treatment effects across segments
Which evaluation design best isolates incremental impact in a live campaign?
Randomized control group held out from treatment
Pre–post comparison of treated users only
Year-over-year comparison without controls
Cross-validation on historical treated users
Uplift models often sort customers into ‘persuadables’, ‘sure things’, ‘lost causes’, and ‘sleeping dogs’. ‘Sleeping dogs’ are ______.
Never respond under any condition
Harmed by the treatment and should be avoided
Always respond regardless of treatment
Ideal for maximum discounting
Why is propensity score weighting sometimes combined with uplift modeling on observational data?
To remove the need for model calibration
To increase class imbalance for stronger signals
To reduce selection bias by balancing covariates between treated and control groups
To transform continuous outcomes into binary labels
A Qini coefficient close to zero typically indicates that the model ______.
Has perfect discrimination at low recall
Is overfitting the control group only
Is miscalibrated but still maximizes uplift
Provides little incremental targeting value over random assignment
Budget-optimized targeting should prioritize segments with ______.
Highest expected uplift per unit cost
Largest population size regardless of effect
Lowest model uncertainty
Highest baseline conversion rate
When treatment spillover occurs between users (interference), uplift estimates can be biased because ______.
Bootstrap resampling is impossible
One unit’s outcome depends on other units’ treatment status
The AUC metric becomes undefined
The model cannot handle continuous outcomes
Starter
Nice start—focus on how treatment and control differ and why Qini curves matter.
Solid
Solid command of uplift concepts—dial in cost per uplift point for budget decisions.
Expert!
Expert: you can deploy, monitor, and optimize for incremental outcomes at scale.
