{"blocks": [{"key": "c4c825f2", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "cbbe37b5", "text": "Technical case: national retail client runs weekly SKU-level marketing campaigns and wants to measure effectiveness from 3 years of data.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "85a17480", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "8112c2a9", "text": "Design an analytical approach to evaluate campaign lift. Specify target variable (Y), predictors (X), chosen model(s), validation strategy, and key caveats. Explain how the insights will inform future campaigns.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6a4c914e", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "44cdfd9a", "text": "Discuss causal inference options (difference-in-differences, uplift modeling, regression with controls), feature engineering, seasonality, and KPI definition.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}