---
schema_version: "1"
id: 100a698393ebd3c736b6f96535dbb0bd4283e156831b27c19b2f8f93d52adc15
title: Revenue Field Guide and Calculation Logic for the B2C Commerce Intelligence JDBC Driver
collection: developer
version: current
locale: en-us
portal_url: https://docs.salesforce.com/developer/current/en-us/commerce-cloud/b2c-commerce/guides/jdbc/jdbc_field_guide_and_calculation_logic.md
canonical_url: https://developer.salesforce.com/docs/commerce/pwa-kit-managed-runtime/guide/jdbc_field_guide_and_calculation_logic.html
source_site: developer.salesforce.com
content_type: documentation
content_hash: a7af89a3c2aa5fc10ec7458e7633d701a0b3bb963aafca09c06ea38b515b780b
products:
  - Composable Storefront
  - Salesforce APIs
---
# Revenue Field Guide and Calculation Logic for the B2C Commerce Intelligence JDBC Driver

Understanding revenue calculations is critical for accurateA Key Performance Indicator (KPI) creation and ensuring consistency across different dashboards and analytics tools. This section provides definitive guidance based on actual CCAC implementation, addressing common customer concerns about revenue discrepancies and metric consistency.

This topic addresses revenue calculation discrepancies between different analytics tools, the need for detailed metric descriptions and business context, and understanding how data models connect to dashboard implementations.

## Revenue Field Selection

This section provides the essential rules for selecting revenue fields in your analytics queries.

### Primary Rule: Use `std_revenue` for All Analytics

**The system automatically handles currency complexity for you:**

**note:** Any column with the prefix `std` refers to a monetary value that is converted to a common realm reporting currency.

- `std_revenue` = **Always** in your site's configured base currency
- Multi-currency transactions are automatically converted to `std_revenue`
- No complex business type decisions needed

### Revenue Field Definitions

| Field Name    | Data Type | Definition                                                                 | When to Use                            |
| ------------- | --------- | -------------------------------------------------------------------------- | -------------------------------------- |
| `std_revenue` | NUMERIC   | **Gross Merchandise Value (GMV)** in the realm's reporting currency (auto-converted) | **All KPIs, reporting, and analytics** |

### Critical: Revenue = Gross Merchandise Value

**All revenue fields represent GROSS merchandise value (which includes certain discounts), not net revenue.** This ensures consistent baselines across all analytics, matching the approach for Commerce reports and dashboards.

**Revenue Calculation:**

```sql
-- Line Item Level (Source of Truth)
gross_merchandise_value = (quantity × unit_price) -
                         ((promotion_discount + promo_discount_order_allocated) +
                          (manual_discount + manual_disc_order_allocated))

-- Aggregated Level (What you query)
std_revenue = SUM(std_li_gross_merchandise_value)
```

GMV includes all orders placed on the storefront, converted to common reporting currency, and adjusted for reporting timezone.

GMV **isn't** adjusted by returns, cancellations, etc.

The `std_li_gross_merchandise_value` column in `ccdw_fact_line_item` represents GMV at the line item level in standard reporting currency.

In aggregate tables (like `ccdw_aggr_sales_summary` or `ccdw_aggr_product_sales_summary`), the `std_revenue` column is an aggregate of the `std_li_gross_merchandise_value` column.

## Standard Revenue Analytics

These examples demonstrate common revenue analysis patterns using the standardized revenue fields.

### All Business Teams: Use `std_revenue`

**Daily Revenue Trend:**

```sql
SELECT
    submit_date,
    SUM(std_revenue) AS daily_revenue,
    SUM(num_orders) AS daily_orders,
    SUM(std_revenue) / SUM(num_orders) AS avg_order_value
FROM ccdw_aggr_sales_summary
WHERE submit_date BETWEEN ? AND ?
GROUP BY submit_date
ORDER BY submit_date;
```

**Site Performance Comparison:**

```sql
SELECT
    s.nsite_id,
    SUM(ss.std_revenue) AS site_revenue,
    SUM(ss.num_orders) AS site_orders,
    SUM(ss.std_revenue) / SUM(ss.num_orders) AS site_aov
FROM ccdw_aggr_sales_summary ss
JOIN ccdw_dim_site s ON s.site_id = ss.site_id
WHERE ss.submit_date BETWEEN ? AND ?
GROUP BY s.nsite_id
ORDER BY site_revenue DESC;
```

**Product Performance Analysis:**

```sql
SELECT
    p.product_display_name,
    SUM(pss.std_revenue) AS product_revenue,
    SUM(pss.num_units) AS units_sold,
    SUM(pss.std_revenue) / SUM(pss.num_units) AS revenue_per_unit
FROM ccdw_aggr_product_sales_summary pss
JOIN ccdw_dim_product p ON p.product_id = pss.product_id
WHERE pss.submit_date BETWEEN ? AND ?
GROUP BY p.product_display_name
HAVING SUM(pss.std_revenue) > 1000  -- Focus on significant revenue products
ORDER BY product_revenue DESC;
```

## Promotion Revenue Analysis

This section shows how to analyze the impact of promotions on revenue using the standardized revenue calculations.

**Example**: Analyze promotion impact using revenue data:

```sql
-- Promotion revenue analysis using aggregated data
SELECT
    p.display_name AS promotion_name,
    SUM(pss.std_revenue) AS promoted_revenue,
    SUM(pss.std_total_discount) AS discount_given,
    SUM(pss.num_orders) AS orders_with_promotion,
    ROUND(SUM(pss.std_total_discount) / SUM(pss.std_revenue) * 100, 2) AS discount_percentage
FROM ccdw_aggr_promotion_sales_summary pss
JOIN ccdw_dim_promotion p ON p.promotion_id = pss.promotion_id
WHERE pss.submit_date BETWEEN ? AND ?
GROUP BY p.display_name
ORDER BY promoted_revenue DESC;
```