Delivery predictions give you a structured, data-backed estimate of when a card will arrive — before the cardholder has to ask. Instead of leaving cardholders to guess from a dispatch confirmation email, you can surface a specific date range directly in your app or card management portal. Varmo produces these predictions automatically for every tracked card, refreshing accuracy as time passes from the dispatch date.
How predictions work
Varmo builds each delivery prediction from four inputs:
- Dispatch date — the timestamp at which the card entered the postal network
- Destination postal code — the delivery target at street-route resolution
- Destination country — used to select the correct regional model and postal service norms
- Regional latency model — a continuously updated dataset built from ground-truth delivery outcomes across postal regions worldwide
Varmo cross-references the dispatch date and destination against its latency model to produce a delivery window and assign a confidence level. The model is updated as new ground-truth delivery data is collected, so predictions improve over time for all regions.
The delivery window
The prediction.delivery_window object contains two ISO 8601 date strings representing the earliest and latest expected delivery dates for the card.
"prediction": {
"delivery_window": {
"min": "2026-05-02",
"max": "2026-05-04"
},
"confidence_level": "High"
}
min — the earliest date by which Varmo expects the card to arrive, based on the fastest observed transit times for the destination regionmax — the latest date within the predicted window; deliveries beyond this date may trigger an exception status
Both dates are calendar dates (not datetimes) and reflect the destination’s local calendar.
Confidence levels
The confidence_level field tells you how much historical signal Varmo has for the destination region. Use it to decide how precisely to phrase delivery messaging to cardholders.
| Confidence level | What it means | Window characteristics |
|---|
High | Strong historical signal exists for this postal region. | Narrow window, typically 2–3 days |
Medium | Moderate historical data is available; some variability in past delivery times. | Wider window, typically 4–6 days |
Low | Sparse ground-truth data for this destination. The window is a best estimate based on country-level averages. | Broad window, treat as an approximation |
Using predictions in your app
The simplest path is to display the ui_suggestion.recommended_message field directly — it is already localized and phrased appropriately for the confidence level. See UI suggestions for guidance on rendering this field.
If you need custom copy or want to match your brand voice precisely, construct your message from the structured prediction fields instead:
- Use
delivery_window.min and delivery_window.max to build a date range string
- Use
confidence_level to decide how hedged your language should be — for Low, add qualifiers such as “approximately” or “estimated”
- Use
ui_suggestion.locale for language routing even when writing your own copy
Whichever approach you choose, display the delivery window rather than a single date. A range communicates appropriate uncertainty and reduces cardholder disappointment when delivery falls on the later end.
Predictions become more accurate as time elapses from the dispatch date. Refresh the status endpoint periodically — for example, once per day — so your UI always reflects the latest prediction.
