Design a multi-touch attribution model
spreading credit across a long journey.
pick a model (time-decay, position-based, or data-driven Shapley), stitch touchpoints by user identity into ordered paths, then assign fractional credit.
What's really being asked
The interviewer wants both a defensible attribution model and the data engineering to support it, recognizing that long, multi-channel journeys make single-touch models misleading.
The full answer
Start with model choice. Heuristic options include linear (equal credit), time-decay (more credit near conversion), and position-based or U-shaped (heavier on first and last). Data-driven options such as Shapley-value or Markov-chain removal-effect attribution learn credit from observed paths and are harder to game but need volume and rigor. State the trade-off: heuristics are transparent and cheap; data-driven models are more accurate but opaque and data-hungry. For implementation, resolve identity so anonymous and known interactions map to one user, ingest timestamped touchpoints from ad platforms, web, and product events, sessionize them into ordered paths, and define a conversion event plus a lookback window. Then apply the chosen credit rule and roll results up by channel and campaign for ROI.
The mistakes people make
Falling back to last-touch, ignoring identity resolution so paths fragment, omitting the lookback window so ancient touches get credit, or proposing Shapley without acknowledging its data and compute demands.
What usually comes next
How do you validate an attribution model when ground truth is unknown? How do incrementality experiments or geo-tests complement attribution? How do you handle cross-device and privacy-driven identity loss?
A concrete example
A user clicks a paid search ad, returns via email a week later, reads a blog post, then converts after a retargeting ad. A U-shaped model gives 40 percent each to the first paid-search and last retargeting touch and splits 20 percent among the middle two. The pipeline stitches all four events to one user id, orders them within a 30-day window, and credits each channel so the marketing team sees that paid search, not just retargeting, deserves budget.
Interview question
What capability is most essential before any multi-touch attribution model can produce meaningful results?
- a.A fixed 7-day conversion window for all campaigns
- b.Resolving identity to stitch a user's touchpoints across channels into one ordered pathCorrect
- c.Real-time streaming of every ad impression
- d.A neural network trained on conversion data
Why? this is the answer
Without identity resolution, touchpoints fragment across devices and channels and no model can reconstruct the true path. The window length and streaming are secondary, and a neural net is not required.
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- #conversion
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