Skip to main content
Card class: Non-Hero • Category: Payment Gateway

​
At a glance

Daily series of refund value issued through Viva Payments. Surfaces refund pattern shape (post-sale spikes, end-of-month batches, “free returns” promo aftermath) that the period summary hides.
What it countsSUM(refunds.Amount) bucketed by refund IssueDate (UTC day). Multi-currency stores get one trend per currency.
API endpoint/api/transactions/{id}/refunds aggregated daily.
CurrencyMulti-currency native, no FX. Per-currency lines.
Refunds countedBoth full and partial refunds.
Disputes / chargebacksExcluded (not refunds).
Failed paymentsNot relevant.
Refund-day vs sale-dayThis card uses issue date. A refund of an April sale issued on 02 May lands on 02 May’s bucket.
ChannelsOnline + POS unified.
Time window90D rolling.
Alert triggerDaily anomalies (>2σ from rolling mean), or monthly running average up >25% vs prior.
Rolesowner, finance

​
Calculation

Calculated automatically from your Viva Payments data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.

​
Worked example

A pan-EU fashion DTC brand on Smart Checkout, 30-day free returns, generous post-sale window. 90-day window 02 Feb 26 to 02 May 26. 
Daily refund value (EUR), 02 Feb 26 to 02 May 26
                                                                                            
Mon | XXX                ~ EUR 800   (back-from-weekend processing)
Tue | XX                 ~ EUR 600
Wed | XX                 ~ EUR 600
Thu | XX                 ~ EUR 600
Fri | XX                 ~ EUR 600   (front-loaded for weekend dispatch)
Sat | X                  ~ EUR 300   (low; processed Monday)
Sun | X                  ~ EUR 300

Notable spikes:
  18 Feb 26 (post Valentine sale)        EUR 4,200    (+520% vs avg)
  03 Mar 26 (sale end + 14-day return)   EUR 6,800    (+850% vs avg)
  28 Apr 26 (post-Easter Spring sale)    EUR 5,200    (+650% vs avg)
What the merchant should notice:
  1. Refunds lag sales by ~ 14, 21 days. A spike in mid-March refunds maps to a sale 14, 21 days earlier (typical “tried it, didn’t fit, returning at the end of the policy window”). Fashion’s standard return-window pattern.
  2. Mondays are the highest refund day. Customer service teams process the weekend’s return-request backlog on Monday. POS in-store returns over the weekend also get keyed on Monday. This is operational, not behavioural.
  3. Sale-end + 14d / 21d / 30d are predictable spikes. Map sale dates → expected refund spike dates. If your refund spike is bigger than expected, the sale’s product-quality or sizing was off.
  4. Multi-currency, separate lines. GBP refund line shows separately from EUR line; don’t conflate.
  5. Sentiment alert at 2σ catches single-day outliers. A normal Monday is EUR 800; an anomaly day at EUR 4,000 will alert. Useful for catching customer-service outages where 100 returns process in one batch.

​
Sibling cards merchants should reference together

CardWhy pair it with Refunds Trend
viva_refund_valueThe summary; this card is its time-series.
viva_refund_rateThe percentage view. Spikes here that don’t move the rate signal a one-off batch, not a behaviour change.
viva_revenue_trendThe numerator equivalent. Subtract daily to read net.
viv_chargeback_rateHigh refund rate often prevents chargebacks; read together.
Stripe stripe_refund_velocity_trend / PayPal refund trendCross-PSP comparison.
Commerce platform Returns trendUpstream cause. Refund originating from Returns flow shows here on issue-date.

​
Reconciling against the vendor’s own dashboard

Where to look in the Viva Payments Dashboard: viva.com/business/account/login. Closest comparable view:
Viva Business → Sales → Refunds (date filter, set granularity to “Daily”)
The Sales → Reports → Net Sales view also shows daily refunds as a deduction overlay. Why our number may legitimately differ from the Viva Dashboard:
ReasonDirectionWhy
Time zone bucketingDay boundaries shiftAthens vs UTC; refunds processed 22:00 Athens land in tomorrow’s UTC bucket.
Issue-date attributionMatchBoth this card and Viva use issue-date for refund timing.
Multi-currencyPer-currency vs rolledToggle in Viva Dashboard.
Cross-connector reconciliation:
ComparisonExpected relationshipWhen divergence is legitimate
viva_refunds_trend ↔ commerce-platform refund trendDaily shapes matchCommerce-platform aggregate covers all rails; Viva is a subset.
viva_refunds_trend ↔ Stripe refund trendIndependentTwo streams summed approximate commerce-platform total.

​
Known limitations / merchant FAQs

A spike day, what likely caused it? Three usual causes: (1) post-sale return-window expiry (sale 14, 30 days ago), (2) customer service batch processing accumulated requests, (3) a single very-large B2B refund (e.g. cancelled wholesale order). Why is Monday always higher? Operational, weekend return requests get keyed Monday morning. How do I match a refund spike to its sale? Look at the sale calendar 14, 30 days before the spike. Apparel returns peak at 14 days; electronics 21, 30; large-format goods 7, 14. My refund trend is rising while sales are flat, what’s happening? Rising refund-rate trend without a refund-volume increase usually means baseline customer behaviour shifted. Check (a) recent product launches with quality issues, (b) sizing-feedback patterns, (c) competitor offering free returns (you’re matching, returns rise). Multi-currency, single chart? Per-currency lines stacked. Don’t read total off one line. Smart POS in-store returns appear here? Yes. POS card-present refunds fire a refund transaction to Viva and contribute to this trend. JP Morgan ownership change anything? No.

​
Tracked live in Vortex IQ Nerve Centre

Refunds Over Time is one of hundreds of KPI pulses Vortex IQ tracks across Viva Payments and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. Start for free or book a demo to see this metric running on your own data.