Attribution Modeling and Multi Touch Attribution Questions

Covers the theory and practice of assigning credit for conversions across marketing touchpoints. Candidates should know single touch models such as first touch and last touch, deterministic multi touch models like linear and time decay, and algorithmic or data driven models that use statistical or machine learning techniques. Discuss the pros and cons of each approach including bias introduced by simple models, the data and engineering requirements for algorithmic models, and trade offs between interpretability and accuracy. Topics include model selection aligned to business questions, dealing with long purchase cycles, cross device and cross channel journeys, limitations of deterministic attribution, approaches to model validation, and how attribution differs from causal incrementality testing.

MediumTechnical

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Stakeholders complain that your data-driven attribution model reduces reported conversions for paid search compared to last-touch. How do you investigate whether the model is wrong or last-touch is over-crediting? Provide a step-by-step diagnostic plan including data checks, alternative models, and experiments to run.

EasyTechnical

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Describe at a high level how a Markov chain model can be used for multi-touch attribution. Explain what transition probabilities and absorbing states represent in this marketing context and how removal effects are computed conceptually.

MediumTechnical

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When building an ML attribution model that predicts conversion probability, how do you detect and prevent target leakage? Provide concrete examples of features that cause leakage in attribution and describe mitigation techniques such as time-based feature engineering and causal holdouts.

MediumTechnical

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Explain how you would use SHAP or other feature importance methods to explain an algorithmic attribution model's outputs. Which parts of the explanation map directly to channel credit and which may reflect model artifacts rather than causal effects?

MediumTechnical

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You must choose between a highly interpretable linear model for attribution and a complex ensemble model with higher predictive accuracy but less interpretability. Outline criteria you would use to choose the model including stakeholder needs, regulatory constraints, expected maintenance cost, and required accuracy.

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