weekly-business-review

name	weekly-business-review
description	Generate a weekly Amazon BUSINESS review - profit, margin, ad efficiency (TACoS) and inventory - comparing this week to your trailing 4-week normal, explaining any margin/fee anomaly, and ending in concrete actions. Use for "weekly business review", "weekly profit review", "weekly P&L", "how is the business doing", "what changed and why", "is my margin or TACoS ok", "profit this week". For a quick sales-only snapshot, use the Weekly Sales Briefing instead.
metadata	{"author":"DataDoe","check-more-skills-at":"https://app.datadoe.com/hub/ai-agents-and-skills","access":"read","category":"Profit & Finance","interface":"mcp","output":"report"}

Weekly Business Review

The Monday-morning business review: one card across profit, margin, ad efficiency (TACoS) and inventory that compares this week to your normal (a trailing baseline, not just last week), explains any margin/fee anomaly, and ends in the few actions worth doing. This is the deep profit/diagnosis review - for a quick sales-only snapshot use the Weekly Sales Briefing instead. Live from DataDoe.

When to use this

• Every Monday, or the start of any week - the deeper profit/business review.
• The single "how's the business doing, what changed, why, what do I do" read.
• Trigger phrases: "weekly business review", "weekly profit review", "weekly P&L", "how is the business doing", "what changed and why", "is my margin ok", "is my TACoS ok", "profit this week". (For plain "sales this week" / "weekly sales report", the Weekly Sales Briefing is the right skill.)

The framework. Baseline -> anomaly -> movers -> 3 actions

1. Compare to normal, not just last week. Show this week vs the trailing 4-week median (median, not mean - one anomalous week shouldn't move the baseline) as the primary signal, plus last week for context, plus a small 6-8 week trend. A single week is noisy; the baseline keeps the headline honest.
2. Explain the anomaly - and check it's real. If margin or TACoS deviates materially from baseline (e.g. > 5pp), decompose why using the cost columns as % of sales - COGS %, fees %, FBA fees %, ad %. Name the driver. Then apply the settlement-timing test: Amazon books fees on settlement date, not sale date, so profit_by_date weekly margin is lumpy - a fee spike concentrated in 1-2 weeks on otherwise-flat sales is usually a settlement batch, not a real cost increase. Say so, and route to the Reimbursement/Fee Audit (which reconciles by order_date) to confirm before the seller panics. Sales, units and ad spend are sale-dated and reliable weekly; margin is only trustworthy over a trailing 4-week window.
3. Movers. Biggest SKU profit gainers/droppers this week vs last.
4. Do this week. 3 concrete actions, each pointing to the deeper skill.

Configuration

• MCP base: https://mcp.datadoe.com/mcp/v1
• Data sources:
  • Profit by Date (amazon_profit_by_date) - account-level weekly totals AND cost breakdown (total_sales, profit, ad_spend, cogs_total, total_fees, fba_fees, total_selling_fees, total_units_sold).
  • Profit by SKU & Date (amazon_profit_by_sku_and_date) - per-SKU movers.
  • Optional: FBA Inventory Health (amazon_fba_inventory_health) for stockout watch-outs.
• Complete weeks only (drop the partial current week). Currency: read currency, localise; if the connection spans currencies, group by currency and report the main one (never sum across currencies).

Step-by-step workflow (MCP-native)

1. sellers_and_vendors_list -> pick the seller.
2. exports_sources_get -> confirm sources enabled.
3. Weekly account trend + cost mix: exports_create on amazon_profit_by_date, last ~8 weeks, groupBy [date, currency] + dateInterval WEEK, sum total_sales, profit, ad_spend, total_units_sold, cogs_total, total_fees, fba_fees, total_selling_fees. Drop the partial current week.
4. Baseline: latest complete week = "this week"; prior = "last week"; baseline = mean of the 4 complete weeks before this one. For each KPI show this week, the Δ vs last week, and the Δ vs baseline. Recompute margin = profit/sales and TACoS = ad_spend/sales per week (never sum ratios).
5. Anomaly drill: if this week's (or a recent week's) margin/TACoS is > ~5pp off baseline, express each cost as % of sales per week (COGS%, total_fees%, fba_fees%, ad%) and identify which line moved - that is the cause. Report it in plain words.
6. Movers: exports_create on amazon_profit_by_sku_and_date for this week and last week, groupBy [sku, product_name], sum profit+total_sales, pull a wide set (limit ~200 each so mid-size SKUs aren't missed), diff by SKU -> top gainers/droppers.
7. Render the HTML card.

Output format (interactive HTML card)

Render a single self-contained HTML card (KPI tiles, a small trend sparkline per KPI, a movers table, watch-outs, actions). Header "Weekly Insights", footer "Data via DataDoe". If the client cannot render HTML, fall back to the text layout below.

Weekly Insights - {marketplace} - week of {start}   (vs 4-wk avg)

            This wk    vs last wk   vs normal(4wk)
Sales       {cur}..    {+/-}%       {+/-}%
Profit      {cur}..    {+/-}%       {+/-}%
Margin      {m}%       {+/-}pp      {+/-}pp
Ad spend    {cur}..    {+/-}%       {+/-}%
TACoS       {t}%       {+/-}pp      {+/-}pp
Units       {u}        {+/-}%       {+/-}%
Trend (8wk): sales ▁▃▆▅▆▇  profit ▆▇▃▁▄▆

Why (if anomaly): margin {m}% vs normal {b}% - driver: {fees/COGS/ads} moved
  from {x}% to {y}% of sales in wk {date}.

Top movers   +{sku} {cur}..   -{sku} {cur}..
Watch-outs   {rising TACoS / stockouts / buy-box}
Do this week 1) ...  2) ...  3) ...

Worked example (illustrative)

Suppose this week shows ~62% margin and, versus last week alone, profit looks "up ~10%" - misleading, because last week was itself depressed. The trend + drill tell the real story: two recent weeks cratered to ~33% and ~18% margin. Decomposing costs as % of sales, COGS and ad held flat every week - the mover was fees, which jumped from single digits to ~50% of sales for two weeks (FBA fees far above their normal level). Then the settlement-timing test: that spike is concentrated in two weeks on otherwise-flat sales - the signature of a settlement batch (fees for earlier sales landing later), not a real cost jump. So the card concludes: "the margin dip is a fee-settlement batch, not an operational problem; confirm in the Reimbursement/Fee Audit by order_date," and treats weekly margin as indicative only. That is the difference between a scary wrong number and a correct insight.

Quality self-check

• Did I compare to the trailing baseline, not just last week (so a noisy prior week can't mislead)?
• If margin/TACoS was off, did I name the cost driver, not just flag it?
• Complete weeks only; ratios recomputed (not summed)?
• Did I end in 3 concrete actions?

Common mistakes

• WoW-only headline: last week being abnormal makes this week look great/terrible.
• Reading a single week's margin as real - fees settle in batches; use the trailing window for margin and confirm fee spikes in settlements by order_date.
• Flagging "margin dropped" without decomposing which cost moved.
• Summing TACoS/margin/ACoS columns.
• A wall of metrics with no action.

Notes

• Read-only. Routes to the deeper DataDoe skills for each action.
• A DataDoe skill. Built on DataDoe Profit by Date + Profit by SKU.