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Online Order Conversion Rate

Also known as: Order Conversion Rate, Order Conversion Rate (Sessions), E-commerce Conversion Rate, Session Conversion Rate, Purchase Conversion Rate

ConversionkpiE-commerce: Cart & Checkout Funnel Analyticsconversion ratesessionsRetail

Online Order Conversion Rate is the percentage of all website or app sessions that include at least one completed order (a purchase event with a unique transaction ID). It is the standard session-based e-commerce conversion rate used in GA4, Adobe Analytics and Shopify reporting to judge how effectively traffic turns into orders.

Business Use Case

1️⃣ The E-commerce Manager reviews the rate weekly and, when it falls more than 10% below the trailing 4-week average, opens a funnel review of cart and checkout steps to find the drop-off. 2️⃣ The Marketing team compares the rate by traffic source and campaign each month and shifts budget away from channels whose sessions convert well below the site average. 3️⃣ The Product team uses the rate by device type as the primary success metric for checkout and mobile UX A/B tests, rolling out variants only when the lift is statistically significant. 4️⃣ Merchandising checks the rate during promotions and sale events and adjusts landing pages or offers when promotional traffic fails to convert.

Business Criticality

High

Journey / Navigation Area

Checkout

Owning Function

E-commerce

Scope

Session

Formula

(Sessions with at least one Completed Order / Total Sessions) × 100

Calculation Notes

1️⃣ Denominator is the full base population: every session in the period (distinct user_pseudo_id + ga_session_id), not only sessions that viewed a product or reached checkout. 2️⃣ Numerator counts sessions containing at least one purchase event with a non-null transaction_id; a session with several orders counts once, so the rate can never exceed 100%. 3️⃣ Deduplicate purchases by transaction_id so page reloads of the order confirmation page do not inflate conversions. 4️⃣ Sessions end after 30 minutes of inactivity (GA4 default); in AEP the same 30-minute timeout is applied with SESS_TIMEOUT. 5️⃣ Exclude internal, test and known bot traffic, and exclude fully refunded or cancelled test orders flagged in the order payload. 6️⃣ Offline, phone and in-store orders are out of scope; blend them only in a separate omnichannel conversion view. 7️⃣ Report the rate alongside session volume, because small daily session counts make the percentage volatile.

Technical Details

Measure TypePercentage
Aggregation WindowDaily, Weekly, Monthly

Events

PlatformEvent
Google Analytics 4purchase
Adobepurchase

Data Mappings

W3 Data Layer
{
  "page": {
    "pageInfo": {
      "pageName": "order confirmation",
      "pageType": "checkout-confirmation"
    }
  },
  "transaction": {
    "transactionID": "T-100245",
    "total": {
      "basePrice": 90,
      "currency": "USD",
      "shipping": 5,
      "taxRate": 0.0556,
      "priceWithTax": 100
    },
    "item": [
      {
        "productInfo": {
          "productID": "SKU-123",
          "productName": "Running Shoe"
        },
        "price": {
          "basePrice": 45
        },
        "quantity": 2
      }
    ]
  },
  "event": [
    {
      "eventInfo": {
        "eventName": "purchase",
        "eventAction": "order-completed"
      }
    }
  ]
}
GA4 Data Layer
{
  "event": "purchase",
  "ecommerce": {
    "transaction_id": "T-100245",
    "value": 100,
    "tax": 5,
    "shipping": 5,
    "currency": "USD",
    "coupon": "SUMMER10",
    "items": [
      {
        "item_id": "SKU-123",
        "item_name": "Running Shoe",
        "item_category": "Footwear",
        "price": 45,
        "quantity": 2
      }
    ]
  }
}
Adobe Client Data Layer
{
  "event": "purchase",
  "eventInfo": {
    "eventName": "purchase"
  },
  "transaction": {
    "purchaseID": "T-100245",
    "total": 100,
    "tax": 5,
    "shipping": 5,
    "currency": "USD",
    "coupon": "SUMMER10"
  },
  "products": [
    {
      "productID": "SKU-123",
      "productName": "Running Shoe",
      "price": 45,
      "quantity": 2
    }
  ]
}
XDM Mapping
{
  "eventType": "commerce.purchases",
  "commerce": {
    "purchases": {
      "value": 1
    },
    "order": {
      "purchaseID": "T-100245",
      "priceTotal": 100,
      "currencyCode": "USD"
    }
  },
  "productListItems": [
    {
      "SKU": "SKU-123",
      "name": "Running Shoe",
      "quantity": 2,
      "priceTotal": 90
    }
  ]
}

SQL Queries

Google Analytics 4 (BigQuery)
-- ==========================================
-- 1. GOOGLE BIGQUERY SQL
-- ==========================================
-- Online Order Conversion Rate = sessions with >= 1 purchase / all sessions x 100
WITH events AS (
  SELECT
    PARSE_DATE('%Y%m%d', event_date) AS event_date,
    user_pseudo_id,
    event_name,
    (SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') AS ga_session_id,
    ecommerce.transaction_id AS transaction_id
  FROM `your-project.analytics_123456789.events_*`
  WHERE _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
                          AND FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
),
sessions AS (
  SELECT
    MIN(event_date) AS session_date,
    user_pseudo_id,
    ga_session_id,
    MAX(IF(event_name = 'purchase' AND transaction_id IS NOT NULL AND transaction_id != '(not set)', 1, 0)) AS has_order
  FROM events
  WHERE ga_session_id IS NOT NULL
  GROUP BY user_pseudo_id, ga_session_id
)
SELECT
  session_date,
  COUNT(*) AS total_sessions,
  SUM(has_order) AS converting_sessions,
  ROUND(SAFE_DIVIDE(SUM(has_order), COUNT(*)) * 100, 2) AS online_order_conversion_rate_pct
FROM sessions
GROUP BY session_date
ORDER BY session_date;
Adobe Experience Platform (AEP Query Service)
-- ==========================================
-- 2. ADOBE AEP QUERY SERVICE SQL
-- ==========================================
-- Online Order Conversion Rate = sessions with >= 1 purchase / all sessions x 100 (30-minute session timeout)
WITH sessionized AS (
  SELECT
    endUserIDs._experience.ecid.id AS ecid,
    timestamp,
    eventType,
    commerce.order.purchaseID AS purchase_id,
    SESS_TIMEOUT(timestamp, 60 * 30) OVER (
      PARTITION BY endUserIDs._experience.ecid.id
      ORDER BY timestamp
      ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
    ) AS session
  FROM web_experience_events
  WHERE timestamp >= DATE_SUB(CURRENT_DATE, 30)
    AND timestamp < CURRENT_DATE
),
sessions AS (
  SELECT
    ecid,
    session.num AS session_num,
    CAST(MIN(timestamp) AS DATE) AS session_date,
    MAX(CASE WHEN eventType = 'commerce.purchases' AND purchase_id IS NOT NULL THEN 1 ELSE 0 END) AS has_order
  FROM sessionized
  GROUP BY ecid, session.num
)
SELECT
  session_date,
  COUNT(*) AS total_sessions,
  SUM(has_order) AS converting_sessions,
  ROUND(SUM(has_order) * 100.0 / NULLIF(COUNT(*), 0), 2) AS online_order_conversion_rate_pct
FROM sessions
GROUP BY session_date
ORDER BY session_date;

Source Data

Digital Analytics (GA4 / Adobe Analytics)

Dependencies

Prerequisite: List the dependencies required for this KPI

Events:

purchasesession_start

Metrics:

sessionstransactions

Dimensions:

transaction-idsession-id

Report Attributes

Traffic Source / Medium Campaign Device Category Country Landing Page New vs Returning User

Recommended Reporting Views

Views
E-commerce Performance DashboardConversion Rate Optimization DashboardMarketing Channel Performance Dashboard

Segment Eligibility

true

Governance

Data SensitivityInternal

Governance

Created bydev-user
Created on5/28/2026
Last modified bydevyendarm
Last modified on9/18/2026
StatusPUBLISHED

GitHub

View related Pull Requestcatalog/kpis/online-order-conversion-rate.yml

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