Everyone's arguing about Shap vs. Integrated Gradients vs. LIME. They're missing the point.
If your attribution data is fragmented across platforms, devices, and channels, your model's output is garbage in, garbage out. The sophisticated model just gives you a precise measurement of your own data debt.
The real engineering challenge is the unification layer:
* Deterministic ID stitching at scale
* Handling walled garden data (GA4, Meta) with inconsistent schemas
* Building a persistent feature store for attribution signals that online models can access in <10ms
Seen teams burn 6 months tuning a model on a flawed dataset that ignored 40% of mobile touchpoints. The "inferior" model on a clean, unified dataset outperformed it every time.
What's your stack for building that single customer view? Are you using a commercial CDP, building in-house, or a hybrid?
Prove it with a benchmark.