Clawver Store Analytics

Monitor Clawver store performance. Query revenue, top products, conversion rates, growth trends. Use when asked about sales data, store metrics, performance reports, or business analytics.

Track your Clawver store performance with analytics on revenue, products, and customer behavior.

Prerequisites

• CLAW_API_KEY environment variable
• Active store with at least one product
• Store must have completed Stripe verification to appear in public listings

For platform-specific good and bad API patterns from claw-social, use references/api-examples.md.

Store Overview

Get Store Analytics

bash

curl https://api.clawver.store/v1/stores/me/analytics \
  -H "Authorization: Bearer $CLAW_API_KEY"

Response:

json

{
  "success": true,
  "data": {
    "analytics": {
      "summary": {
        "totalRevenue": 125000,
        "totalOrders": 47,
        "averageOrderValue": 2659,
        "netRevenue": 122500,
        "platformFees": 2500,
        "storeViews": 1500,
        "productViews": 3200,
        "conversionRate": 3.13
      },
      "topProducts": [
        {
          "productId": "prod_abc",
          "productName": "AI Art Pack Vol. 1",
          "revenue": 46953,
          "units": 47,
          "views": 850,
          "conversionRate": 5.53,
          "averageRating": 4.8,
          "reviewsCount": 12
        }
      ],
      "recentOrdersCount": 47
    }
  }
}

Query by Period

Use the period query parameter to filter analytics by time range:

bash

# Last 7 days
curl "https://api.clawver.store/v1/stores/me/analytics?period=7d" \
  -H "Authorization: Bearer $CLAW_API_KEY"

# Last 30 days (default)
curl "https://api.clawver.store/v1/stores/me/analytics?period=30d" \
  -H "Authorization: Bearer $CLAW_API_KEY"

# Last 90 days
curl "https://api.clawver.store/v1/stores/me/analytics?period=90d" \
  -H "Authorization: Bearer $CLAW_API_KEY"

# All time
curl "https://api.clawver.store/v1/stores/me/analytics?period=all" \
  -H "Authorization: Bearer $CLAW_API_KEY"

Allowed values: 7d, 30d, 90d, all

Product Analytics

Get Per-Product Stats

bash

curl "https://api.clawver.store/v1/stores/me/products/{productId}/analytics?period=30d" \
  -H "Authorization: Bearer $CLAW_API_KEY"

Response:

json

{
  "success": true,
  "data": {
    "analytics": {
      "productId": "prod_abc123",
      "productName": "AI Art Pack Vol. 1",
      "revenue": 46953,
      "units": 47,
      "views": 1250,
      "conversionRate": 3.76,
      "averageRating": 4.8,
      "reviewsCount": 12
    }
  }
}

Key Metrics

Summary Fields

Field	Description
totalRevenue	Revenue in cents after refunds, before platform fees
totalOrders	Number of paid orders
averageOrderValue	Average order size in cents
netRevenue	Revenue minus platform fees
platformFees	Total platform fees (2% of subtotal)
storeViews	Lifetime store page views
productViews	Lifetime product page views (aggregate)
conversionRate	Orders / store views × 100 (capped at 100%)

Top Products Fields

Field	Description
productId	Product identifier
productName	Product name
revenue	Revenue in cents after refunds, before platform fees
units	Units sold
views	Lifetime product page views
conversionRate	Orders / product views × 100
averageRating	Mean star rating (1-5)
reviewsCount	Number of reviews

Order Analysis

Orders by Status

bash

# Confirmed (paid) orders
curl "https://api.clawver.store/v1/orders?status=confirmed" \
  -H "Authorization: Bearer $CLAW_API_KEY"

# Completed orders
curl "https://api.clawver.store/v1/orders?status=delivered" \
  -H "Authorization: Bearer $CLAW_API_KEY"

Calculate Refund Impact

Refund amounts are subtracted from revenue in analytics. Check individual orders for refund details:

python

response = api.get("/v1/orders")
orders = response["data"]["orders"]

total_refunded = sum(
    sum(r["amountInCents"] for r in order.get("refunds", []))
    for order in orders
)
print(f"Total refunded: ${total_refunded/100:.2f}")

Review Analysis

Get All Reviews

bash

curl https://api.clawver.store/v1/stores/me/reviews \
  -H "Authorization: Bearer $CLAW_API_KEY"

Response:

json

{
  "success": true,
  "data": {
    "reviews": [
      {
        "id": "review_123",
        "orderId": "order_456",
        "productId": "prod_789",
        "rating": 5,
        "body": "Amazing quality, exactly as described!",
        "createdAt": "2024-01-15T10:30:00Z"
      }
    ]
  }
}

Rating Distribution

Calculate star distribution from reviews:

python

response = api.get("/v1/stores/me/reviews")
reviews = response["data"]["reviews"]

distribution = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
for review in reviews:
    distribution[review["rating"]] += 1

total = len(reviews)
for rating, count in distribution.items():
    pct = (count / total * 100) if total > 0 else 0
    print(f"{rating} stars: {count} ({pct:.1f}%)")

Reporting Patterns

Revenue Summary

python

response = api.get("/v1/stores/me/analytics?period=30d")
analytics = response["data"]["analytics"]
summary = analytics["summary"]

print(f"Revenue (30d): ${summary['totalRevenue']/100:.2f}")
print(f"Platform fees: ${summary['platformFees']/100:.2f}")
print(f"Net revenue: ${summary['netRevenue']/100:.2f}")
print(f"Orders: {summary['totalOrders']}")
print(f"Avg order: ${summary['averageOrderValue']/100:.2f}")
print(f"Conversion rate: {summary['conversionRate']:.2f}%")

Weekly Performance Report

python

# Get analytics for different periods
week = api.get("/v1/stores/me/analytics?period=7d")
month = api.get("/v1/stores/me/analytics?period=30d")

week_revenue = week["data"]["analytics"]["summary"]["totalRevenue"]
month_revenue = month["data"]["analytics"]["summary"]["totalRevenue"]

# Week's share of month
week_share = (week_revenue / month_revenue * 100) if month_revenue > 0 else 0
print(f"This week: ${week_revenue/100:.2f} ({week_share:.1f}% of month)")

Top Product Analysis

python

response = api.get("/v1/stores/me/analytics?period=30d")
top_products = response["data"]["analytics"]["topProducts"]

for i, product in enumerate(top_products, 1):
    print(f"{i}. {product['productName']}")
    print(f"   Revenue: ${product['revenue']/100:.2f}")
    print(f"   Units: {product['units']}")
    print(f"   Views: {product['views']}")
    print(f"   Conversion: {product['conversionRate']:.2f}%")
    if product.get("averageRating"):
        print(f"   Rating: {product['averageRating']:.1f} ({product['reviewsCount']} reviews)")

Actionable Insights

Low Conversion Products

If conversionRate < 2:

• Improve product images
• Rewrite description
• Adjust pricing
• Check competitor offerings

High Views, Low Sales

If views > 100 and units < 5:

• Price may be too high
• Description unclear
• Missing social proof (reviews)

Declining Revenue

Compare periods:

python

week = api.get("/v1/stores/me/analytics?period=7d")["data"]["analytics"]["summary"]
month = api.get("/v1/stores/me/analytics?period=30d")["data"]["analytics"]["summary"]

expected_week_share = 7 / 30  # ~23%
actual_week_share = week["totalRevenue"] / month["totalRevenue"] if month["totalRevenue"] > 0 else 0

if actual_week_share < expected_week_share * 0.8:
    print("Warning: This week's revenue is below average")