First, clarify the schema and definitions (e.g., active merchants, pirated themes, installation date). Then write a SQL query that joins merchants, theme installations, and themes, filters for pirated themes, and computes the percentage of active merchants per year who installed at least one pirated theme. Finally, interpret the year-over-year trend to assess growth in piracy adoption.
Pro tip: Always state your assumptions about ambiguous terms like 'active merchant' and 'pirated theme' upfront, and consider using window functions or CTEs for clarity and performance.
Confirm what 'active merchant' means (e.g., merchants with at least one sale in the year) and how 'pirated theme' is identified (e.g., a flag in the themes table). Also, understand the grain of the theme_installations table.
Create a CTE that lists distinct merchant IDs active in each calendar year, based on the definition of active (e.g., from a sales or activity table).
Join theme_installations with themes to filter installations of pirated themes, then extract distinct merchant IDs and the year of installation (or year of activity if installation date is not available).
For each year, count the number of active merchants and the number of those who installed at least one pirated theme. Calculate the percentage as (count of pirates / count of active merchants) * 100.
Compare the percentages across years to determine if piracy adoption is growing. Consider statistical significance and potential confounders (e.g., changes in merchant base or detection methods).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.