Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years)

Context

You have 3 years of panel data at weekly SKU (and optionally region/store) granularity for a national retailer. The client runs weekly SKU-level marketing campaigns (e.g., spend, impressions, channels, creative) and wants to estimate causal lift from these campaigns.

Task

Design an analytical approach to quantify campaign lift and translate findings into actionable guidance for future campaigns. Clearly specify:

1. Target variable (Y) and KPI(s)
2. Predictors (X) and key feature engineering (including seasonality)
3. Chosen causal/modeling approach(es)
4. Validation and diagnostic strategy
5. Key caveats/assumptions
6. How the insights will inform future campaign planning and targeting

Consider

• Causal inference options: difference-in-differences (including staggered adoption), uplift modeling (treatment effect heterogeneity), regression with controls / doubly robust learners
• KPI definition (incremental units, revenue, margin, ROI/pROAS)
• Seasonality, trends, holidays, and adstock/carryover
• Data pitfalls: selection bias, overlap, stockouts, cannibalization, interference