ecommerce-analytics-dashboard

name	ecommerce_analytics_dashboard
description	Analyze e-commerce metrics and create actionable dashboards. Calculate KPIs, track trends, and generate insights from sales, traffic, and conversion data.

E-Commerce Analytics Dashboard

Analyze e-commerce metrics and create actionable business insights.

When to Use

Use this skill when the user wants to:

• Analyze sales and revenue data
• Calculate e-commerce KPIs (CAC, LTV, AOV, conversion rates)
• Create dashboard-style metric summaries
• Track trends over time periods
• Generate actionable insights from raw data

Supported Platforms

• Google Analytics, Shopify Analytics, WooCommerce Reports
• Amazon Seller Central, Etsy Stats, eBay Seller Dashboard
• Custom CSV/Excel data import

Workflow

Step 1: Data Collection

Gather available metrics:

Sales Metrics:

• Total Revenue / GMV
• Number of Orders
• Average Order Value (AOV)
• Units per Transaction
• Return Rate

Traffic Metrics:

• Sessions / Visits
• Unique Visitors
• Page Views
• Bounce Rate
• Session Duration

Conversion Metrics:

• Conversion Rate (sessions to orders)
• Add-to-Cart Rate
• Checkout Started Rate
• Checkout Completed Rate

Step 2: KPI Calculation

Calculate key performance indicators:

## Core E-Commerce KPIs

### Revenue Metrics
| Metric | Formula | This Period |
|--------|---------|-------------|
| Average Order Value (AOV) | Revenue / Orders | $[X] |
| Revenue per Visitor (RPV) | Revenue / Visitors | $[X] |

### Conversion Metrics
| Metric | Formula | This Period |
|--------|---------|-------------|
| Conversion Rate | Orders / Sessions × 100 | X% |
| Cart Abandonment | 1 - (Checkouts/Add-to-Cart) | X% |
| Checkout Abandonment | 1 - (Purchases/Checkouts) | X% |

### Customer Metrics
| Metric | Formula | This Period |
|--------|---------|-------------|
| Customer Acquisition Cost (CAC) | Spend / New Customers | $[X] |
| Customer Lifetime Value (LTV) | AOV × Orders/Year × Retention Years | $[X] |
| LTV:CAC Ratio | LTV / CAC | X:1 |

### Marketing Metrics
| Metric | Formula | This Period |
|--------|---------|-------------|
| Return on Ad Spend (ROAS) | Revenue / Ad Spend | Xx |
| Marketing Efficiency Ratio | (Revenue - COGS - Ad Spend) / Ad Spend | X% |

Step 3: Trend Analysis

Compare periods (Week-over-Week, Month-over-Month, Year-over-Year):

## Trend Analysis

### Positive Trends
- [Metric] increased by [X]% (positive)
- [Explanation of driver]

### Negative Trends
- [Metric] decreased by [X]% (watch)
- [Root cause hypothesis]

### Flat Trends
- [Metric] flat (opportunity for improvement)

Step 4: Dashboard Generation

Create visual-style text dashboards:

## 📊 Performance Dashboard

### Revenue Summary
┌─────────────────────────────────────────┐
│ Total Revenue: $[XXX,XXX]  ▲ +XX%     │
│ Orders: [X,XXX]           ▲ +XX%      │
│ AOV: $[XX]               ▼ -X%       │
└─────────────────────────────────────────┘

### Funnel Performance
[████████████░░░░] Sessions: XXX,XXX
[██████░░░░░░░░░] Add to Cart: XX%
[███░░░░░░░░░░░░] Checkout: XX%
[█░░░░░░░░░░░░░░] Purchase: XX%

### Top 3 Insights
1. [Insight with metric]
2. [Insight with metric]
3. [Insight with metric]

### Recommended Actions
- [Priority action based on data]
- [Secondary action]

Best Practices

1. Context Matters: Always compare to previous periods
2. Segment When Possible: By product, channel, customer type
3. Explain the "Why": Don't just show numbers, explain drivers
4. Prioritize Actions: Focus on 3-5 actionable insights max
5. Visualize Trends: Use ASCII charts or trend indicators

Benchmarks for E-Commerce

Metric	Poor	Average	Good	Excellent
Conversion Rate	<1%	1-2%	2-4%	>4%
AOV	<$50	$50-100	$100-150	>$150
Cart Abandonment	>85%	70-85%	60-70%	<60%
LTV:CAC	<1:1	1:1-3:1	3:1-5:1	>5:1
ROAS	<1x	1-3x	3-5x	>5x

Example Prompts

• "Analyze last month's Shopify sales data"
• "Calculate our customer acquisition cost and LTV"
• "Create a dashboard comparing this quarter vs last"
• "Why did our conversion rate drop this week?"
• "What are our top performing products this month?"

Advanced Use Cases

• Cohort Analysis: "Analyze customer retention by acquisition month"
• Attribution Modeling: "Which marketing channels drive the most conversions?"
• Revenue Forecasting: "Predict next month's revenue based on trends"
• Unit Economics: "Calculate our contribution margin per product"