Summary
Every brand gets its own EDD Prediction Model, built for it alone and trained on its own data, covering the journey from the warehouse to the shopper’s door. It blends your history with Perform.AI network data across more than 1,100 carriers, models the journey as a distribution rather than a single number, turns that distribution into a date at a confidence level you choose, and adjusts to live network conditions.Who it’s for
E-commerce and logistics teams who need a delivery date they are willing to show on a product page, rather than a carrier estimate that starts at collection or a static transit table padded for the worst day. It is scoped first and never used alone: the Checkout EDD Widget or the Checkout EDD API renders what it produces.Key benefits
- Your own date, not your carrier’s estimate. The model is trained on how your warehouses, carriers and trade lanes perform, and covers every carrier you use with one definition.
- The warehouse is in the number. Non-working days, processing times and cut-off windows, the stretch a carrier estimate never sees, are part of the prediction.
- Confident where your own data is thin. Your signals are blended with network data from more than 1,100 carriers, down to postal-code level, so lanes you ship rarely still get a usable date.
- The ambition of the date is a choice. A buffer and a confidence threshold decide how near or how safe the date is, and you can see the trade in conversion and in kept promises.
- It moves when the network moves. Weather, strikes, hub backlogs and peak adjust the date without waiting for a retrain, and the model is retrained against what was delivered.
How it works
Scoping
Before the model is built, Perform.AI assesses what your delivery date does today: whether shoppers see a date at all, how soon after ordering it reaches them, how often it was right and how much buffer sits between the predicted and the actual delivery. The scope is then agreed lane by lane in an EDD questionnaire with your account team. Coverage is per scope; outside it, the widget shows a text range rather than a date, and the scope widens as data accumulates.What the model uses
Where the data supports it, the model can be segmented by product type, because different products travel differently.
From a distribution to a date
The journey is modelled as a distribution rather than a single estimate. You choose the confidence level at which that distribution becomes a date: a higher level produces a later, safer date; a lower one produces a nearer, riskier one. Buffers and confidence thresholds are set per scope. You decide what you want and Perform.AI configures it.Responding to change
The model adjusts the date to live network conditions, such as weather, strikes, hub backlogs and peak season, without a full retrain. It is also retrained periodically against actual first delivery attempts, so it does not decay into the static rule it replaced.Marketplaces
Marketplace onboarding also needs seller identifiers and the service agreements between sellers and the marketplace. A new seller with its own warehouse runs on fallback logic until there is enough data to retrain; a seller using marketplace fulfilment uses the existing model.Related model: Shipment EDD
Shipment EDD is a separate model for parcels already moving, with its own inputs and its own buffer, tuned in the opposite direction. The two share a network and a method, not a configuration.Set up in the portal
Use via API and MCP
- Guide: Predict checkout delivery dates requests a prediction for a destination, origin, carrier and shipping service, and links the
checkout_idto the shipment. - API reference: Predict checkout EDD.