The core problem
Attribution assigns credit. Incrementality estimates causal lift (what ads caused). These are not the same, and confusing them is one of the fastest ways to scale spend into diminishing returns.
What each approach is good at
| Approach | Good for | Common failure modes |
|---|---|---|
| Platform attribution | Fast feedback for optimization | Over-credits retargeting; windows differ; model changes |
| Analytics attribution | Cross-channel visibility | Last-touch bias; missing view-through; tracking gaps |
| MER (top-down) | Alignment and sanity checks | Hides which channel is working; needs clean spend definition |
| Incrementality tests | Causal truth for scale | Contamination, low power, short tests vs purchase cycle |
Attribution windows (why comparisons break)
- Keep windows stable when comparing periods; changing windows changes the metric, not performance.
- Longer windows can inflate credit (especially for retargeting); shorter windows can under-credit longer-cycle products.
- If you must change windows, annotate and avoid comparing across the change.
How to build a measurement stack that scales
- Start with funnel math (CPM/CTR/CVR) plus profit guardrails (break-even targets).
- Add MER to align marketing and finance on top-down efficiency.
- Use marginal ROAS (or incremental profit) as you scale to detect saturation.
- Run incrementality tests when spend is meaningful or when channels fight for credit.
How to run incrementality tests (pragmatic checklist)
- Pick a clean holdout: ensure the control truly has no exposure (avoid contamination).
- Run long enough to cover the purchase cycle and reduce noise.
- Measure both conversions and profit proxies (margin-aware outcomes).
- Repeat when creatives/audiences change; incrementality is not permanent.