Set up the properties of your network to shape the base of the data model. Declare details like carrier pick up times, fulfillment days, and carrier volume limits, which influence the additional, more dynamic layers of the model.

Historical shipments are the most heavily weighted factor in estimating a date. Transit data is automatically fed into your EDD models when pairing Delivery Promise with Carrier Selection, but there are available methods to feed transit data to Shipium when only using Delivery Promise on its own.

Rely on the Shipium in-house transportation ops team to set platform-wide adjustments to estimations based on macro conditions, such as weather delays or a carrier issuing a ZIP-level delay due to issues like labor shortages.

EDDs are ultimately a probabilistic value, coming as close to 100% accuracy as possible, knowing that total accuracy is impossible. The eventual EDD is a statistical composite that predicts the date that is most probable to share with the customer.

Calculating delivery dates

Pull forward cost optimizations to ensure estimated dates fit certain cost parameters. Shipium makes it easy to ensure a shipping designed to be a profit center remains a key profit driver, while also pulling forward accurate dates that align in order to improve customer experience.

Forecast a future shipment date when calculating an EDD. This is a perfect fit for companies who manage timed launches, such as clothing brands or video game retailers.

Configure time-in-transit (TNT) models from three available options, including bringing your own, that can be applied to some or all of your inventory.

Calculating delivery dates

For customers who use Carrier Selection, historical shipping data is automatically captured and fed into their Delivery Promise model.

Use Delivery Promise on its own by sending historical shipping data to the platform via several options, from a specific data stream to a fiat file upload, and more.

Combine APIs to get EDDs for a multi-SKU cart and determine split shipment options at the cart level.

If you don't have an origin to share, fall back to automatic geographic determination by the platform. The data model powering Shipium's EDDs can assume a likely origin if you don't provide one in the API call.

Improve accuracy by layering in additional known downstream constraints. For example, if you are a retailer who uses Shipium for both warehouse and store shipping, but you know a particular SKU is only available in the warehouses, you can exclude store origins from the probable fulfillment origins for a more accurate prediction.

Show EDDs for a single product on its description page. If the product has variable SKUs, such as different sizes of apparel, utilize the API nature of Delivery Promise to update EDDs on the fly based on possible origin changes based on inventory availability.

View live examples on SaksOFF5th.com

Measure how delivery promises improve conversion by using metadata tagging during checkout along a multi-stage workflow. Combined with the flexibility of showing one date or split dates for a multi-SKU order, the ability to tie promises to abandonment and conversion insight is simple and powerful.

Read the Saks OFF 5TH case study