First, clarify the table schemas and the definition of 'destination' (e.g., a column indicating 'shop' vs 'external'). Then, filter both tables to the most recent 30 days, aggregate revenue and conversions by date and destination, and compute the daily share of revenue from the Shop destination using a window function or self-join.
Pro tip: Mention that you would validate the share calculation by checking that daily shares sum to 1 across destinations and handle edge cases like days with zero total revenue to avoid division errors.
Ask about the columns in the revenue and conversion tables, how 'destination' is represented (e.g., a string column), and whether revenue is already aggregated daily. Confirm the date range definition for 'most recent 30 days' (e.g., relative to current date or max date in data).
Use a WHERE clause to restrict both tables to the most recent 30 days based on the date column. If the data has multiple rows per day per destination, you may need to aggregate first.
Sum the revenue for each date and destination (Shop vs. external). Ensure you have one row per date-destination combination.
Calculate the total revenue per day across all destinations, then compute the share of revenue from the Shop destination as shop_revenue / total_revenue for each day.
Use NULLIF or CASE to avoid division by zero on days with no revenue. Validate that shares are between 0 and 1 and sum to 1 across destinations for each day.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the two tables and their join keys, then build a daily time series for the past 30 days that aggregates revenue and conversions by destination. Compute revenue per conversion and a share-style diagnostic (e.g., Shop's share of total conversions or revenue) to track volume shifts, and present the results in a stakeholder-friendly format with clear trends and comparisons.
Pro tip: Always validate the time series for completeness (e.g., missing dates, outliers) and consider adding a rolling average to smooth daily noise, which helps stakeholders see the underlying trend rather than day-to-day fluctuations.
Identify the two tables, their join keys, and the fields needed for revenue, conversions, and destination. Clarify definitions of 'Shop' vs 'website' and ensure the time period is correctly filtered to the past 30 days.
Aggregate data by date and destination, summing revenue and conversions. Ensure all dates in the 30-day window are present, filling missing dates with zeros if necessary.
Compute revenue per conversion for each destination and day. Also calculate share-style diagnostics, such as Shop's share of total conversions or revenue, to monitor volume shifts.
Check for anomalies, outliers, or missing data that could skew results. Consider smoothing techniques like 7-day rolling averages to highlight trends.
Create clear visualizations (e.g., line charts for trends, stacked area for share) and summarize key insights, focusing on whether volume is shifting toward Shop.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.