Commerce BI Copilot
Overview
Use this skill to convert fragmented commerce data context into an operator-friendly insight brief. It is designed for teams that need quick explanations, not a heavyweight dashboard rebuild.
This MVP is heuristic. It does not access live warehouses, ad APIs, ERP systems, or real spreadsheets. Instead, it applies a commerce metric dictionary, anomaly checklist, and action-planning framework to the user's provided notes.
Trigger
Use this skill when the user wants to:
- explain why a KPI moved up or down
- prepare a daily, weekly, campaign, or executive business review
- align teams on metric definitions such as GMV, net revenue, ROAS, MER, or refund rate
- turn rough exports or pasted KPI notes into a concise action brief
- produce follow-up questions for an analyst, founder, or agency client
Example prompts
- "Why did GMV drop 12% this week?"
- "Create a weekly ecommerce business review from these KPI notes"
- "Help me explain falling ROAS after our spring promotion"
- "Turn these Shopify, Meta, and refund notes into an executive summary"
Workflow
- Capture the business question, time frame, and referenced channels.
- Normalize the likely metric set and call out any definition ambiguity.
- Build a short driver tree across traffic, conversion, pricing, refunds, inventory, and mix.
- Produce prioritized drill-downs and next actions.
- Return a markdown brief that a founder or operator can immediately use.
Inputs
The user can provide any mix of:
- pasted KPI snapshots or rough metric notes
- mentions of data sources such as Shopify, Amazon, Meta Ads, Google Ads, GA4, ERP, or CRM
- campaign or calendar context, such as promotions, launches, or stockouts
- business questions about revenue, efficiency, refunds, margin, or channel contribution
- audience context, such as founder update, operator review, or agency client recap
Outputs
Return a markdown brief with:
- analysis mode and source assumptions
- KPI snapshot table
- likely driver tree
- recommended drill-downs
- prioritized next best actions
- executive-ready summary bullets
- assumptions and limitations
Safety
- Do not pretend to read live numbers or source files.
- Surface metric-definition ambiguity when GMV, net revenue, refunds, or attribution may conflict.
- Avoid certainty when the input is partial or anecdotal.
- Keep budget, pricing, inventory, and operational decisions human-approved.
Examples
Example 1
Input: Shopify orders, Meta spend notes, and the question "Why did yesterday GMV fall?"
Output: identify a likely mix of traffic decline, conversion weakness, or stock issues, then recommend the next drill-downs and immediate operator actions.
Example 2
Input: weekly KPI notes for channels, refunds, and top products.
Output: generate a compact weekly business brief with risks, wins, and next-week priorities.
Acceptance Criteria
- Return markdown text.
- Include KPI, diagnosis, and action sections.
- Mention evidence gaps or metric ambiguity when relevant.
- Keep the output practical for operators and founders.