This is one of those problems that sounds straightforward until you actually try to explain it.
Start by clarifying the business goals and constraints, then propose a flexible data model that captures user interactions across channels. Discuss trade-offs between different attribution models (e.g., last-click, multi-touch) and outline a scalable system architecture for data collection, processing, and attribution computation.
Pro tip: Emphasize the importance of defining clear success metrics and involving stakeholders early to align on the attribution model, as this is often a product and business decision as much as a technical one.
Ask questions to understand the business context, such as which channels are involved, what conversion events matter, and what latency and accuracy requirements exist.
Propose a schema to track user touchpoints and conversions, including identifiers to stitch user journeys across devices and channels.
Discuss options like last-click, first-click, linear, time-decay, or custom models, and explain how to make the model configurable.
Describe components for data ingestion (e.g., logs, APIs), storage (e.g., data warehouse), processing (e.g., batch/stream), and serving attribution results.
Discuss how to handle large data volumes, ensure data quality, and balance real-time vs. batch processing, as well as privacy considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.