Recommend and validate a budget allocation strategy

Using the insights you would derive from the SQL task—top advertiser by total spend and the most profitable program by net profit and by margin—propose a concrete next-quarter budget allocation that maximizes net profit while controlling risk.

Constraints and Requirements:

• Constraints: no program's budget share may increase by more than 15 percentage points compared with its current share; total budget is fixed; each advertiser's spend must include at least one secondary program (≥10% of their budget) to mitigate concentration risk.
• Deliverables: (a) recommended budget shares at the program level; (b) advertiser-level allocation rules (e.g., by size tier or historical ROI); (c) expected incremental net profit with a back-of-the-envelope calculation and the assumptions used.
• Validation: design an experiment to validate the strategy, selecting among geo holdout, randomized advertiser split, or time-based A/B. Specify the unit of randomization, sample size/power considerations, the primary KPI (incremental net profit), and guardrails (e.g., CAC, conversion rate, churn, revenue volatility). Define success thresholds and a decision framework.
• Analysis plan: indicate the segmentation you would run (e.g., advertiser size, vertical, new vs. returning), how you would handle seasonality and regression to the mean, and what additional data you would request (e.g., conversion quality, LTV, attribution window). Be explicit about confounders and how you will detect/mitigate them.

Overview: This question evaluates a data scientist's competency in budget allocation, experimental design, KPI definition, segmentation, and causal inference by requiring translation of SQL-derived spend and profitability insights into constrained program- and advertiser-level budget rules while managing concentration risk.