Create an ecommerce weekly performance report from sales, traffic, customer and inventory exports. This Skill explains week-on-week changes, highlights product and channel movements and drafts follow-up actions, helping an operations team move from a table of numbers to a clear weekly trading discussion.
Weekly Ecommerce Performance Analyst works with comparable periods and the definitions you provide. It organizes the headline changes, investigates stronger and weaker products or channels, and brings stock concerns into the same report. Currency, dates and refund treatment need to be clear before comparisons are made.
The deliverable combines the trading summary with questions to investigate and proposed owners for follow-up. Data exports and recurring report scheduling remain part of your existing workflow.
Sales, customer behavior and inventory are reviewed together. A product with rising orders may need replenishment, while a falling channel needs a different investigation. The report connects those observations instead of treating every metric as a separate headline.
Use it to prepare an operations meeting, compare campaign weeks or draft product follow-ups. Consistent exports let the team trace statements back to numbers and distinguish confirmed movements from explanations that still need investigation.
Comparable weekly exports with dates, currency, metric definitions, inventory, and any competitor evidence.
A model chat is enough to draft from these instructions. Real system access or file changes need separately configured tools.
It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.
Use these instructions in a chat, or ask Codex / Claude Code to save them as a personal Skill for repeated tasks.
Set up a personal reusable instruction template for Weekly Ecommerce Performance Analyst in the app I am using.
1. Read the “Collected instruction template” section at https://ecomagenttools.com/skills/weekly-performance-analyst. Use only that complete instruction text, not the page’s reviews, FAQ or installation explanation. If you cannot retrieve the complete text, ask me to paste it; do not guess it or download a same-named third-party package.
2. Show which assumptions, placeholders and access requirements I need to supply. Preserve the collected task instructions. This is a locally adapted template, not an original-author package.
3. If you are Codex or Claude Code with local file access, create a personal Skill named weekly-performance-analyst-local under ~/.agents/skills/weekly-performance-analyst-local/SKILL.md for Codex or ~/.claude/skills/weekly-performance-analyst-local/SKILL.md for Claude Code. Add valid name and description frontmatter if absent; keep the collected instructions in the body and identify the local adaptation. Do not overwrite an existing Skill. If you are in a chat-only app, use these instructions in the current conversation instead and do not claim a local installation.
4. Return the saved path or current-chat setup result and a starter request for this task. List any required tools or data that remain unconfigured. Do not claim account/API access or a completed business workflow just because the template was saved.Network-collected; no confirmed author source. The collected text was reviewed, but it has not been linked to a verified original publication. This does not establish that no official package exists; the examples here use only the collected text.
You are an ecommerce performance analyst. When given weekly store data, generate:
1. EXECUTIVE SUMMARY (3 bullet points):
- Top-line: Revenue vs last week (%, $)
- Traffic: Sessions, conversion rate, AOV trends
- Alert: Any metric outside normal range
2. WIN/LOSS ANALYSIS:
- Top 3 winning products (by revenue growth)
- Top 3 declining products
- Top 3 traffic sources (by conversion quality)
3. CUSTOMER INSIGHTS:
- New vs returning customer split
- Average order value trend
- Customer acquisition cost estimate
4. INVENTORY ALERT:
- Products at risk of stockout (< 2 weeks)
- Overstock items (> 90 days)
5. RECOMMENDATIONS (prioritized):
- 1 immediate action (do today)
- 2 short-term actions (this week)
- 1 strategic initiative (this month)
6. COMPETITIVE PULSE:
- Any notable competitor activity or pricing changes
- Industry benchmark comparison
Format as a clean, boardroom-ready report. Include % changes, not just absolute numbers. Flag anything red (>20% negative change) with 🚨.Starter prompts for the main use cases—copy and use them directly.
Calculate week-over-week revenue, sessions, conversion, and order-value changes using consistent definitions. Use for: Explain weekly performance. Prepare: Comparable weekly exports with dates, currency, metric definitions, inventory, and any competitor evidence.
Use the collected instructions above. Task: Calculate week-over-week revenue, sessions, conversion, and order-value changes using consistent definitions.. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.[INPUT][CONSTRAINTS]Identify winning and declining products and channels, distinguishing volume from conversion quality. Use for: Review product and channel changes. Prepare: Comparable weekly exports with dates, currency, metric definitions, inventory, and any competitor evidence.
Use the collected instructions above. Task: Identify winning and declining products and channels, distinguishing volume from conversion quality.. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.[INPUT][CONSTRAINTS]Flag stock risks from supplied stock and demand, then propose immediate and weekly actions. Use for: Prepare inventory actions. Prepare: Comparable weekly exports with dates, currency, metric definitions, inventory, and any competitor evidence.
Use the collected instructions above. Task: Flag stock risks from supplied stock and demand, then propose immediate and weekly actions.. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.[INPUT][CONSTRAINTS]The merchant supplies two weeks of business data, sales and stock for six SKUs, and current channel performance. The weekly report must explain growth, declining products and inventory priorities.
Case input files: business-inputs.zip
Use the weekly-performance-analyst Skill.
Prepare Monday's trading review from the sales, acquisition and stock exports. Explain revenue, conversion and average-order-value changes, identify winning and declining SKUs, separate acquisition performance from returning customers, and flag stockout and overstock risks. End with today's highest-priority action, two actions for the week and one monthly initiative.
The attached business materials are:
- inputs/brief.json
- inputs/products.csv
- inputs/traffic.json
Deliver a complete result.md report that the operations team can read, plus any finished files needed for this task. Keep supporting calculations and evidence with the deliverables. Write the report in English; use the requested target language for customer-facing copy. The business datasets are fictional examples unless the brief explicitly names a live public website.The primary file produced by this prompt is shown below in its original language.
Week: October 26–November 1, 2026, compared with October 19–25. All amounts are USD. All business data, competitor observations and comparison benchmarks supplied for this review are synthetic.
| Metric | Prior week | Current week | Change |
|---|---|---|---|
| Gross merchandise revenue | $2,400 | $2,880 | +$480 / +20.0% |
| Refund value | $120 | $144 | +$24 / +20.0% |
| Revenue after refunds | $2,280 | $2,736 | +$456 / +20.0% |
| Orders | 100 | 120 | +20 / +20.0% |
| Sessions | 4,000 | 4,800 | +800 / +20.0% |
| Order conversion | 2.50% | 2.50% | 0.00 percentage points / 0.0% |
| Gross AOV | $24.00 | $24.00 | $0.00 / 0.0% |
| Refund value / gross revenue | 5.00% | 5.00% | 0.00 percentage points / 0.0% |
| Ad spend | $300 | $360 | +$60 / +20.0% |
Revenue equals sessions × order conversion × gross AOV. The extra 800 sessions at 2.50% conversion and $24 AOV explain the full $480 increase. This is an accounting bridge, not evidence that advertising caused the growth. Conversion remains inside the supplied 2–4% range, and the 5% refund rate remains inside the 0–8% range. Refund dollars grew in proportion to revenue, so there is no deterioration in the aggregate refund rate.
All six SKUs sell at $24, so the changing SKU mix did not change AOV. Product units reconcile to total orders in both weeks, but no order-line data is available to diagnose basket composition.
Winners are ranked by absolute revenue increase; the percentage-growth ranking is identical.
| Winning SKU | Units: prior → current | Revenue: prior → current | Revenue change |
|---|---|---|---|
| HM-BLUE-001 | 30 → 48 | $720 → $1,152 | +$432 / +60.0% |
| HM-SAND-002 | 20 → 28 | $480 → $672 | +$192 / +40.0% |
| HM-GREEN-003 | 10 → 14 | $240 → $336 | +$96 / +40.0% |
| Declining SKU | Units: prior → current | Revenue: prior → current | Revenue change |
|---|---|---|---|
| 🚨 HM-RED-004 | 18 → 12 | $432 → $288 | −$144 / −33.3% |
| 🚨 HM-GRAY-005 | 13 → 10 | $312 → $240 | −$72 / −23.1% |
| HM-BLACK-006 | 9 → 8 | $216 → $192 | −$24 / −11.1% |
Winners added $720, offset by $240 lost across declining products. Blue represents 40% of current revenue, making its availability the main near-term trading dependency. Green is growing but still carries excessive inventory; Gray combines falling sales with overstock. Red and Black have low cover even at their reduced sales rates. The exports do not establish why individual SKUs declined or whether availability constrained sales during the week.
Quality here means last-click orders divided by sessions; it does not measure margin, incremental demand or new-customer acquisition.
| Rank | Source | Sessions | Orders | Conversion | Session share | Order share |
|---|---|---|---|---|---|---|
| 1 | 960 | 29 | 3.02% | 20.0% | 24.2% | |
| 2 | Organic search | 1,440 | 43 | 2.99% | 30.0% | 35.8% |
| 3 | Paid search | 2,400 | 48 | 2.00% | 50.0% | 40.0% |
Email and organic search are effectively close on this single week's observed conversion. Paid search converts 0.50 percentage points below the store average and sits at the lower bound of the supplied conversion range. It supplies half of traffic but only 40% of orders. Review search terms, intent and landing-page availability before increasing spend. Prior-week channel data is absent, so channel improvement or deterioration cannot be measured.
| Purchasing customers | Prior | Current | Growth | Share: prior → current |
|---|---|---|---|---|
| New | 70 | 80 | +10 / +14.3% | 70.0% → 66.7% |
| Returning | 30 | 40 | +10 / +33.3% | 30.0% → 33.3% |
Each group supplied half of the 20 additional orders. Returning-customer share rose 3.33 percentage points, while new-customer share fell by the same amount. These are purchasing-customer counts, with one customer per order as defined in the brief; they do not establish a cohort retention rate.
Estimated blended acquisition-cost proxy: $4.50 per new customer, up from $4.29 (+5.0%), calculated as total ad spend divided by all new purchasing customers. Spend grew 20.0%, faster than new customers at 14.3%. This proxy includes customers from unpaid sources and excludes other acquisition costs. True paid CAC needs channel spend and paid-attributed new-customer counts. The source table does not identify customer status: email orders cannot be assumed to be returning customers, nor paid-search orders to be new customers. Revenue and AOV by customer type are also unavailable.
Cover uses available units divided by current weekly sales units × 7. It assumes the latest sales rate continues, with no inbound or reserved stock.
| SKU | Available units | Current units/week | Days of cover | Decision |
|---|---|---|---|---|
| HM-BLUE-001 | 30 | 48 | 4.4 | Highest-priority stockout risk |
| HM-BLACK-006 | 10 | 8 | 8.8 | Stockout risk |
| HM-RED-004 | 20 | 12 | 11.7 | Stockout risk |
| HM-SAND-002 | 100 | 28 | 25.0 | Within thresholds |
| HM-GREEN-003 | 200 | 14 | 100.0 | Overstock |
| HM-GRAY-005 | 150 | 10 | 105.0 | Overstock; revenue also declining |
Stockout means less than 14 days of cover; overstock means more than 90 days. Green and Gray hold 350 units with $8,400 of ticket-price value; this is neither inventory cost nor expected cash recovery. At the current run rate, approximately 20 Green units and 22 Gray units are above a 90-day holding level, rounding excess units upward.
Blue needs 66 additional available units to reach a simple two-week floor of 96 units. Black needs 6 and Red needs 4. These are provisional coverage gaps, not purchase-order quantities: supplier lead time, minimum order quantities and safety-stock requirements are missing. Blue's forecast is especially exposed to its 60% weekly sales growth.
The supplied BayCup observations show a price move from $26 to $22, a 15.4% decrease. HarborMug's $24 price is now $2, or 9.1%, above that observed competitor price. Product comparability, shipping and promotion terms are not supplied; this does not justify matching the price, especially while Blue is stock constrained.
Against the supplied exercise-only DTC benchmark, HarborMug's 2.50% conversion matches 2.50%; its $24 AOV is $1, or 4.0%, below $25. These comparisons provide a planning reference, not researched industry evidence. No other competitor activity is evidenced.
Merchant business data is synthetic. Website observations, when present, come from the public URLs listed in the report.
It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.
Network-collected; no confirmed author source. The collected text was reviewed, but it has not been linked to a verified original publication. This does not establish that no official package exists; the examples here use only the collected text.
Use authorized inputs and keep secrets out of prompts. Review customer-facing claims, calculations, code changes, and any proposed external action before applying them.
Draft a weekly trading report from supplied sales, traffic, customer, and inventory exports. The outline combines changes, product winners and losers, inventory alerts, and named next actions.
Copy the collected instructions below into a model chat or project instructions. Replace assumed access, default policies, sample numbers, and placeholders with your actual context. Attach a small redacted sample, use a starter prompt, and review the draft before acting.
Explain weekly performance; Review product and channel changes; Prepare inventory actions
It does not fetch store data or schedule reports. Stock thresholds and the 20% alert rule are template defaults, not proven operating targets.
Network-collected; no confirmed author source. The collected text was reviewed, but it has not been linked to a verified original publication. This does not establish that no official package exists; the examples here use only the collected text.
The complete available Skill content is cataloged and reviewed; public web material does not currently name the original author. Attribution does not affect its directory visibility or content-based recommendation eligibility.
Network-collected; no author source is listed. This label describes attribution only, not capability, visibility, or recommendation eligibility.
No reviews yet.