AfterShip Tracking
by AfterShip
AfterShip normalizes parcel tracking across a long list of couriers into one model: trackings with a status tag, a checkpoint for every scan, the courier that carried the parcel and an estimated delivery date. In the Lake those records sit next to orders, support tickets and repeat purchases, so e-commerce and CX teams can measure delivery promises kept, spot problem lanes and see what a late parcel costs in lost loyalty.
- Category
- Operations & Logistics
- Type
- Shipment tracking & returns
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- API key
02Data
What you can sync.
- Trackings
- Checkpoints
- Couriers
- Estimated delivery dates
Join AfterShip Tracking trackings with orders and costs to see service levels and spend by route, site or customer.
Need more? Name any other AfterShip Tracking data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- An API key
AfterShip Tracking uses an API key. You add the credentials once, when the AI asks for them, and it tells you where to create them in AfterShip if you need help.
04Questions
Questions you can answer.
Once AfterShip Tracking data is in your Lake, ask an AI Mind in plain words or build the answer in DataPrism BI.
- Which couriers miss the estimated delivery date most often, by destination region and order value?
- How many days pass between the first checkpoint and the Delivered tag, by courier and service?
- What share of 'where is my order' tickets come from parcels that hit the Exception or AttemptFail status?
- Do customers whose first order arrived after its estimated delivery date place a second order less often?
- Which warehouses hand over parcels that sit longest at InfoReceived before the first carrier scan?
05How it works
How it works in DataMind.
-
Pick AfterShip Tracking.
Open Lake, pick AfterShip Tracking in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the AfterShip Tracking API docs.
-
Answer a few questions.
The AI reads the AfterShip Tracking documentation, then asks which credentials you will use, which data to sync (for example trackings and checkpoints), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then AfterShip Tracking data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
06Pairs
Works well with AfterShip Tracking.
Sources teams join with AfterShip Tracking in the Warehouse, each one connected the same way.
07Related
