AfterShip is a shipment tracking platform that helps e-commerce businesses track packages across 1,100+ carriers worldwide. It provides real-time tracking updates, delivery estimates, and analytics to improve post-purchase customer experience and operational efficiency.

Configuring AfterShip as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the AfterShip option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need an AfterShip API key to authenticate. The following configurations are available:

API Key: Your AfterShip API key used to authenticate API requests.
Start Date: The earliest date from which records will be synced based on the last modification date.

How to get your AfterShip API Key

Go to AfterShip and log in to your account.

Navigate to API Settings

Click on your profile icon in the top-right corner
Select Settings
In the left sidebar, click on API Keys under the “Integrations” section

Generate a new API Key

Click Generate API Key
Give your API key a descriptive name (e.g., “Nekt Integration”)
Click Generate
Copy the generated API key immediately

Important: The API key is only shown once. Copy it immediately and store it securely. If you lose it, you’ll need to generate a new one.

Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.

Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new or updated data based on the updated_at field.
- Full table: every time the extraction happens, we’ll get the current state of all data.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from AfterShip and their corresponding fields:

Trackings

Stream containing shipment tracking information with full tracking history.Key Fields:

id - Unique identifier for the tracking
legacy_id - Legacy tracking ID
tracking_number - The carrier tracking number
slug - Courier code (e.g., “ups”, “fedex”, “dhl”)
active - Whether the tracking is currently active
created_at - When the tracking was created
updated_at - When the tracking was last updated (replication key)

Order Information:

order_id - Associated order ID
order_number - Order number
order_id_path - Order IDs path
order_date - Date of the order
order_tags[] - Tags associated with the order
order_promised_delivery_date - Promised delivery date

Origin Location:

origin_country_region - Origin country/region code
origin_state - Origin state
origin_city - Origin city
origin_postal_code - Origin postal code
origin_raw_location - Raw origin location string

Destination Location:

destination_country_region - Destination country/region code
destination_state - Destination state
destination_city - Destination city
destination_postal_code - Destination postal code
destination_raw_location - Raw destination location string
courier_destination_country_region - Courier-reported destination

Delivery Status:

tag - Current tracking status tag (e.g., “Delivered”, “InTransit”, “OutForDelivery”)
subtag - Detailed status subtag
subtag_message - Human-readable status message
on_time_status - On-time delivery status
on_time_difference - Difference from promised delivery time (in days)
signed_by - Name of person who signed for delivery
return_to_sender - Whether package was returned to sender

Shipment Details:

shipment_type - Type of shipment
shipment_package_count - Number of packages
shipment_pickup_date - Pickup date
shipment_delivery_date - Actual delivery date
shipping_method - Shipping method used
shipment_tags[] - Tags for the shipment
shipment_weight.value - Weight value
shipment_weight.unit - Weight unit (kg, lb, etc.)
transit_time - Transit time in days

Estimated Delivery:

courier_estimated_delivery_date.estimated_delivery_date - Courier’s estimated delivery
courier_estimated_delivery_date.estimated_delivery_date_min - Minimum estimate
courier_estimated_delivery_date.estimated_delivery_date_max - Maximum estimate
aftership_estimated_delivery_date - AfterShip’s predicted delivery date
custom_estimated_delivery_date - Custom estimated delivery date
first_estimated_delivery.datetime - First estimated delivery datetime
first_estimated_delivery.source - Source of first estimate
latest_estimated_delivery.datetime - Latest estimated delivery datetime
latest_estimated_delivery.revise_reason - Reason for revision

Checkpoints (Tracking History):

checkpoints[] - Array of tracking events:
- checkpoints[].checkpoint_time - Timestamp of the event
- checkpoints[].tag - Status tag at checkpoint
- checkpoints[].subtag - Detailed subtag
- checkpoints[].subtag_message - Status message
- checkpoints[].message - Checkpoint description
- checkpoints[].location - Full location string
- checkpoints[].city - City
- checkpoints[].state - State
- checkpoints[].country_region - Country/region code
- checkpoints[].country_region_name - Country/region name
- checkpoints[].postal_code - Postal code
- checkpoints[].coordinate.latitude - Latitude
- checkpoints[].coordinate.longitude - Longitude
- checkpoints[].slug - Courier slug
- checkpoints[].source - Data source
- checkpoints[].created_at - When checkpoint was created
- checkpoints[].events[] - Events at this checkpoint

Customer Information:

customers[] - Array of customers:
- customers[].name - Customer name
- customers[].email - Customer email
- customers[].phone_number - Phone number
- customers[].language - Preferred language
- customers[].role - Role (buyer or receiver)

Notifications:

subscribed_emails[] - Subscribed email addresses
subscribed_smses[] - Subscribed SMS numbers

Delivery Attempts:

first_attempted_at - First delivery attempt timestamp
failed_delivery_attempts - Number of failed attempts
delivery_type - Type of delivery
delivery_location_type - Type of delivery location
signature_requirement - Signature requirement info
pickup_location - Pickup location (for pickup deliveries)
pickup_note - Pickup note

Tracking Links:

courier_tracking_link - Direct link to carrier tracking
courier_redirect_link - Courier redirect link
aftership_tracking_url - AfterShip tracking page URL
aftership_tracking_order_url - AfterShip order tracking URL

Additional Fields:

title - Tracking title (usually order number or product name)
note - Notes about the tracking
source - Source of the tracking data
language - Language of the tracking record
tracked_count - Number of times tracked
tracking_account_number - Carrier account number
tracking_key - Tracking key
tracking_ship_date - Ship date (YYYY-MM-DD)
courier_connection_id - Courier connection ID
location_id - Location ID
carbon_emissions - Estimated carbon emissions
custom_fields.item_names - Custom field for item names
first_mile - First mile information
last_mile - Last mile information

Data Model

The following diagram illustrates the structure of the AfterShip data. The Trackings stream is the main entity containing all shipment tracking information including nested checkpoints and customer data.

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating AfterShip data, along with ready-to-use SQL queries that you can run on Explorer.

1. Delivery Performance Overview

Track delivery performance across carriers and identify trends in on-time delivery. Business Value:

Monitor carrier performance and reliability
Identify delivery delays and their causes
Optimize carrier selection based on performance data

SQL query

WITH delivery_metrics AS (
    SELECT
        slug AS carrier,
        tag AS status,
        COUNT(*) AS total_shipments,
        AVG(transit_time) AS avg_transit_days,
        SUM(CASE WHEN on_time_status = 'on_time' THEN 1 ELSE 0 END) AS on_time_count,
        SUM(CASE WHEN tag = 'Delivered' THEN 1 ELSE 0 END) AS delivered_count
    FROM
        nekt_raw.aftership_trackings
    WHERE
        created_at >= CURRENT_DATE - INTERVAL '30' DAY
    GROUP BY
        slug,
        tag
)
SELECT
    carrier,
    status,
    total_shipments,
    ROUND(avg_transit_days, 1) AS avg_transit_days,
    on_time_count,
    ROUND(on_time_count * 100.0 / NULLIF(delivered_count, 0), 1) AS on_time_rate_pct
FROM
    delivery_metrics
ORDER BY
    total_shipments DESC

WITH delivery_metrics AS (
    SELECT
        slug AS carrier,
        tag AS status,
        COUNT(*) AS total_shipments,
        AVG(transit_time) AS avg_transit_days,
        SUM(CASE WHEN on_time_status = 'on_time' THEN 1 ELSE 0 END) AS on_time_count,
        SUM(CASE WHEN tag = 'Delivered' THEN 1 ELSE 0 END) AS delivered_count
    FROM
        `nekt_raw.aftership_trackings`
    WHERE
        DATE(created_at) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY
        slug,
        tag
)
SELECT
    carrier,
    status,
    total_shipments,
    ROUND(avg_transit_days, 1) AS avg_transit_days,
    on_time_count,
    ROUND(SAFE_DIVIDE(on_time_count * 100.0, delivered_count), 1) AS on_time_rate_pct
FROM
    delivery_metrics
ORDER BY
    total_shipments DESC

Sample Result

carrier	status	total_shipments	avg_transit_days	on_time_count	on_time_rate_pct
ups	Delivered	1,245	3.2	1,156	92.9
fedex	Delivered	892	2.8	845	94.7
dhl	InTransit	456	4.1	-	-
usps	Delivered	378	4.5	312	82.5

2. Shipment Status Distribution

Analyze current shipment statuses to understand pipeline and identify potential issues. Business Value:

Monitor shipments in transit
Identify stuck or problematic shipments
Track exception rates by carrier

SQL query

SELECT
    tag AS status,
    subtag AS detailed_status,
    COUNT(*) AS shipment_count,
    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 1) AS percentage
FROM
    nekt_raw.aftership_trackings
WHERE
    active = true
GROUP BY
    tag,
    subtag
ORDER BY
    shipment_count DESC

SELECT
    tag AS status,
    subtag AS detailed_status,
    COUNT(*) AS shipment_count,
    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 1) AS percentage
FROM
    `nekt_raw.aftership_trackings`
WHERE
    active = true
GROUP BY
    tag,
    subtag
ORDER BY
    shipment_count DESC

Sample Result

status	detailed_status	shipment_count	percentage
InTransit	InTransit_PickedUp	523	35.2
InTransit	InTransit_Departed	312	21.0
OutForDelivery	OutForDelivery_ToBeDelivered	178	12.0
Delivered	Delivered_Signed	156	10.5
Exception	Exception_Delayed	89	6.0

3. Destination Analysis

Analyze shipment destinations to understand geographic distribution and regional performance. Business Value:

Identify top shipping destinations
Analyze regional delivery performance
Plan logistics and carrier partnerships by region

SQL query

SELECT
    destination_country_region AS country,
    destination_state AS state,
    COUNT(*) AS total_shipments,
    AVG(transit_time) AS avg_transit_days,
    SUM(CASE WHEN tag = 'Delivered' THEN 1 ELSE 0 END) AS delivered,
    SUM(CASE WHEN tag = 'Exception' THEN 1 ELSE 0 END) AS exceptions
FROM
    nekt_raw.aftership_trackings
WHERE
    destination_country_region IS NOT NULL
    AND created_at >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY
    destination_country_region,
    destination_state
ORDER BY
    total_shipments DESC
LIMIT 20

SELECT
    destination_country_region AS country,
    destination_state AS state,
    COUNT(*) AS total_shipments,
    AVG(transit_time) AS avg_transit_days,
    SUM(CASE WHEN tag = 'Delivered' THEN 1 ELSE 0 END) AS delivered,
    SUM(CASE WHEN tag = 'Exception' THEN 1 ELSE 0 END) AS exceptions
FROM
    `nekt_raw.aftership_trackings`
WHERE
    destination_country_region IS NOT NULL
    AND DATE(created_at) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY
    destination_country_region,
    destination_state
ORDER BY
    total_shipments DESC
LIMIT 20

Sample Result

country	state	total_shipments	avg_transit_days	delivered	exceptions
US	California	456	2.8	423	12
US	Texas	312	3.1	287	8
US	New York	289	2.5	271	5
US	Florida	234	3.4	212	11
CA	Ontario	178	4.2	156	7

Implementation Notes

Incremental Sync Support

The Trackings stream supports incremental sync using the updated_at field as the replication key
Only trackings modified since the last sync will be fetched, improving extraction performance

Nested Data Structures

The checkpoints array contains the full tracking history with timestamps, locations, and status updates
The customers array may contain multiple customers (buyer and receiver)
Use JSON functions or UNNEST to analyze nested checkpoint and customer data

Tracking Status Tags

AfterShip uses standardized tags to represent shipment status:

Pending - Tracking created, no updates yet
InfoReceived - Carrier received shipment information
InTransit - Shipment is in transit
OutForDelivery - Out for delivery
AttemptFail - Delivery attempt failed
Delivered - Successfully delivered
AvailableForPickup - Available at pickup location
Exception - Exception occurred (delay, customs, etc.)
Expired - Tracking expired with no delivery confirmation

Skills for agents

Download AfterShip skills file

AfterShip connector documentation as plain markdown, for use in AI agent contexts.

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Resources

AfterShip as a data source

Configuring AfterShip as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Delivery Performance Overview

2. Shipment Status Distribution

3. Destination Analysis

Implementation Notes

Incremental Sync Support

Nested Data Structures

Tracking Status Tags

Skills for agents

Download AfterShip skills file

​Configuring AfterShip as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​1. Delivery Performance Overview

​2. Shipment Status Distribution

​3. Destination Analysis

​Implementation Notes

​Incremental Sync Support

​Nested Data Structures

​Tracking Status Tags

​Skills for agents

Download AfterShip skills file

Configuring AfterShip as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Delivery Performance Overview

2. Shipment Status Distribution

3. Destination Analysis

Implementation Notes

Incremental Sync Support

Nested Data Structures

Tracking Status Tags

Skills for agents