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Investigate a real month-over-month decline inside a deterministic ecommerce dataset. Submit a metric definition, reproducible analysis, validation checks, executive summary, and recommendation.
You support a fictional ecommerce company. The finance lead reports that September 2025 net payment value is lower than August and asks for an explanation before the monthly business review.
Your job is to verify the comparison, identify which measurable changes contribute to it, and separate supported findings from plausible explanations that the data cannot prove. The dataset is synthetic, but the ambiguity is intentional: orders, line items, payments, and refunds describe different events at different grains.
Both options contain the same versioned source tables. Use the SQLite database for direct SQL analysis or the CSV package for Python, spreadsheets, a warehouse, or another tool.
State the population, grain, date rule, successful-payment rule, refund treatment, currency, and limitations.
Provide readable SQL or a notebook that rebuilds the comparison and decomposes the change without duplicating values.
Show at least three checks, including a grain or uniqueness check, a reconciliation, and one edge-case inspection.
Write no more than 300 words: what changed, what the data supports, what remains uncertain, and what should happen next.
Create one chart or table that makes the comparison understandable without requiring the reviewer to read your code.
Practice the payment-and-refund calculation with a separate, smaller teaching fixture. It is not the take-home dataset.
The comparison follows the supplied definition, uses compatible grains, and reconciles to simpler checks.
The work tests multiple hypotheses and does not confuse association, timing, or synthetic patterns with causation.
Another analyst can run the work against dataset version 1.0.0 and understand each transformation.
The summary leads with the decision-relevant result, defines the measure, and keeps limitations visible.
The recommendation follows from the measured contribution and names the next evidence required.