Commerce BI Copilot

Problem

In business reviews, KPI evidence is often scattered across Shopify, Meta Ads, GA4, refund tables, and meeting notes. Operators need to answer questions like "why did GMV drop" quickly, but full warehouse diagnosis can be too heavy. commerce-bi-copilot is aimed at this lightweight workflow: it turns pasted KPI notes, channel clues, and business questions into an actionable insight brief.

How it works

The skill first captures the business question, time frame, and referenced channels, then aligns metric definitions such as GMV, net revenue, ROAS, MER, and refund rate. It builds a short driver tree around traffic, conversion, pricing, refunds, inventory, and mix, then outputs likely causes, prioritized drill-downs, and next actions. The final markdown brief includes a KPI snapshot table, assumptions, risks, suggested questions, and an executive-ready summary.

Boundaries

This is a heuristic MVP and does not access live warehouses, ad APIs, ERP systems, or raw spreadsheets. The output is only as reliable as the supplied context; partial data, anecdotal signals, or inconsistent metric definitions should keep evidence gaps visible. Budget, pricing, inventory, and operational decisions still require human approval.