Operations and supply chain
Delivery Date Promise
A calibrated delivery date from order, inventory, and carrier history, shown on the product page and at checkout, with proactive alerts when a delay is coming.
The problem
A vague or optimistic delivery estimate costs sales at checkout, and a missed one costs trust and floods support with questions about where the order is.
Who owns it
The head of e-commerce or customer experience, with fulfillment and logistics operations.
What it learns from
- Orders and their fulfillment history
- Inventory by location
- Carrier scans and delivery outcomes
- Warehouse cut-off times and capacity
- Destination and service level
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Predicts a calibrated delivery date
Models predict when an order will arrive, from the inventory location, warehouse load, carrier performance, and destination, as a range whose stated confidence matches how often it is met.
- 02
Decides what to promise through a policy
The promise shown is the date the model meets at the confidence level you choose. Where the model cannot support a promise, none is shown, and a delay above the threshold you set triggers an alert.
- 03
Acts on the storefront and in customer messages
The date appears on the product page and at checkout, and customers are told before a delay rather than after. Support sees the same prediction.
- 04
Measures with an A/B test
An A/B test compares conversion with the predicted date against the current estimate. Promise accuracy and contacts asking where an order is are tracked alongside.
What it moves
The business levers
Conversion
More checkouts completed when the date is clear and credible.
Support contacts
Fewer questions about where an order is.
Trust
Promises kept, and delays explained before they happen.
Shipping choice
Customers choose the speed they need from dates they can rely on.
Proof
How the return is proven
The return is the incremental conversion of the predicted date over the current estimate in the A/B test, plus support contacts avoided, stated against the return model agreed up front. Promise accuracy is reported beside it.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Order and customer data are visible by role through your single sign-on.
Approval gates
Changes to the confidence level behind the promise need the e-commerce owner's approval.
Audit trail
Every promise shown is logged with the model version and the actual delivery.
Consumer protection
A promise is shown only where its calibration supports it, consistent with consumer-protection rules on delivery claims.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.