{"blocks": [{"key": "79c1e7f5", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "91ed5453", "text": "As a product/analytics lead at a food-delivery marketplace you must evaluate several Dasher-facing initiatives (Top-Dasher prioritization, Extra-Pay incentives, and switching pay model from per-order to per-time) before deciding whether to launch them.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "088280ca", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d00e9a87", "text": "How would you assess whether the Top-Dasher program should be launched? 2) For an \"extra pay\" incentive aimed at improving Dasher engagement: a) what primary success metric(s) would you track? b) design an A/B test (including treatment, control, experiment length, sample-size, and guardrail metrics). 3) The company is considering switching Dasher compensation from per-order to per-time. What are the key pros & cons of each model, and how would you experimentally validate which model is better for marketplace health?", "type": "unordered-list-item", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "6db2503e", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b6e6ba35", "text": "Discuss causal identification, experiment vs. quasi-experiment trade-offs, KPI definition (accept rate, fulfillment time, retention), supply-demand balance, cost impact, and possible negative externalities.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}