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Orders

ConversionE-commerce

Indicates the number purchases or transactions successfully completed an individual customer or all customers during a selected time period.

Business Use Case

1️⃣ Marketing Attribution & ROI: Identify which campaigns actually drive orders & revenue versus those that just drive "window shopping" (traffic) 2️⃣ Inventory & Supply Chain Management: Order volume over time allows businesses to predict future demand and avoid "Stockouts" or overstocking 3️⃣ Customer Loyalty & Retention Analysis: 4️⃣ Product Performance: identify high-interest items that convert or failed to convert due to price, reviews, or poor descriptions etc., 5️⃣ Product Finding Methods: Analyze which site features like internal search, category menus, or recommendation widgets are most effective at leading users to a final purchase.

Business Criticality

High

Journey / Navigation Area

Digital analytics, Business intelligence

Scope

Global

Formula

Orders = COUNT(DISTINCT order_id)

Calculation Notes

1️⃣ Filter duplicate transactions by unique order_id. 2️⃣ Exclude test or internal transactions via predefined filters. 3️⃣ Consider refunds or cancellations as adjustments in Orders and downstream Revenue KPIs to report actual business impacting metrics.

Technical Details

Measure TypeCounter
Aggregation WindowSession, User, Time based - Hourly/Daily/Monthly/Yearly

Events

PlatformEvent
Google Analytics 4purchase
Adobepurchase

Data Mappings

W3 Data Layer
window.digitalData = {
  page: {
    pageInfo: {
      pageName: "Order Confirmation",
      destinationURL: window.location.href
    },
    category: {
      primaryCategory: "Ecommerce",
      pageType: "Purchase Confirmation"
    }
  },
  transaction: {
    transactionID: "T_12345_1",
    total: {
      basePrice: 30.03,
      taxAmount: 4.90,
      shipping: 5.99,
      currency: "USD"
    },
    voucherCode: "SUMMER_SALE",
    item: [{
      productInfo: {
        productName: "Stan and Friends Tee",
        sku: "SKU_12345",
        manufacturer: "Google"
      },
      category: {
        primaryCategory: "Apparel",
        subCategory1: "Adult",
        subCategory2: "Shirts"
      },
      quantity: 3,
      price: {
        basePrice: 10.01,
        currency: "USD"
      },
      attributes: {
        variant: "green",
        coupon: "SUMMER_FUN",
        discount: 2.22
      }
    }]
  },
  event: [{
    eventInfo: {
      eventName: "Purchase",
      eventType: "transaction",
      eventAction: "purchase-complete"
    }
  }]
};
GA4 Data Layer
{
        transaction_id: "T_12345_1",
        value: 30.03,
        tax: 4.90,
        shipping: 5.99,
        currency: "USD",
        coupon: "SUMMER_SALE",
        items: [
        // If someone purchases more than one item,
        // you can add those items to the items array
         {
          item_id: "SKU_12345",
          item_name: "Stan and Friends Tee",
          affiliation: "Google Merchandise Store",
          coupon: "SUMMER_FUN",
          discount: 2.22,
          index: 0,
          item_brand: "Google",
          item_category: "Apparel",
          item_category2: "Adult",
          item_category3: "Shirts",
          item_category4: "Crew",
          item_category5: "Short sleeve",
          item_list_id: "related_products",
          item_list_name: "Related Products",
          item_variant: "green",
          location_id: "ChIJIQBpAG2ahYAR_6128GcTUEo",
          price: 10.01,
          quantity: 3
        }]
    }
XDM Mapping
/*In a practical setup, it is a practice to XDM-formatted object into the Adobe Client Data Layer to have the benefit of both.

window.adobeDataLayer.push({
  "event": "order-complete",
  "xdm": {
    // The XDM block from above goes here
  }
});
*/

{
  "xdm": {
    "eventType": "commerce.purchases",
    "commerce": {
      "order": {
        "purchaseID": "T_12345_1",
        "currencyCode": "USD",
        "priceTotal": 30.03,
        "taxAmount": 4.90,
        "shippingAmount": 5.99,
        "purchaseReferralCode": "SUMMER_SALE"
      },
      "purchases": {
        "value": 1
      }
    },
    "productListItems": [
      {
        "SKU": "SKU_12345",
        "name": "Stan and Friends Tee",
        "quantity": 3,
        "priceTotal": 30.03,
        "product": "SKU_12345"
      }
    ]
  }
}

SQL Queries

Google Analytics 4 (BigQuery)
SELECT COUNT(DISTINCT order_id) AS total_orders, DATE(TIMESTAMP_MICROS(event_timestamp)) AS order_date FROM `your_project.your_dataset.ecommerce_events`, UNNEST(event_params) AS params WHERE event_name = 'purchase' AND params.key = 'order_id' GROUP BY order_date ORDER BY order_date;
Adobe Experience Platform (AEP Query Service)
SELECT COUNT(DISTINCT commerce.purchases.orderID) AS total_orders, DATE(eventTimestamp) AS order_date FROM your_dataset.your_table WHERE eventType = 'commerce.purchases' GROUP BY order_date ORDER BY order_date;

Source Data

Digital analytics, Business intelligence

Dependencies

Prerequisite: List the dependencies required for this KPI

Events:

purchase

Report Attributes

Order ID, Transaction Date, Revenue, Items Purchased, Campaign, Product category

Recommended Reporting Views

Views
Product Performance, Sales/E-commerce Dashboard, Key Metrics

Segment Eligibility

True

Governance

Data SensitivityInternal

Governance

Created bydevyendarm
Created on11/30/2025
Last modified bydevyendarm
Last modified on5/7/2026
StatusPUBLISHED

GitHub

View related Pull Requestcatalog/kpis/orders.yml

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