At a glance
Daily timeseries of total discount applied to orders. The promotional health view: when did promotions run, how heavy were they, what was the post-promo recovery shape?
Calculation
Worked example
A US apparel brand on Shopify Plus. Period: 12 Feb 26 to 12 May 26. Daily total-discount summary:
Five things to notice:
- Two clear promo events stand out. Spring (05-09 Mar peak) and Easter (13-16 Apr). Each shows a clean ramp-up, peak, and return-to-baseline shape.
- The Spring tail (10-14 Mar) reveals coupon stacking. $4,180 / day for 5 days after the peak suggests the SPRING20 code wasn’t fully shut down, or it was extended via influencer codes that overlapped. Pair with Top Discount Codes to confirm.
- Baseline is informative. 1,020 / day is the “always-on” promotional cost: welcome codes for new subscribers, abandoned-cart vouchers, loyalty-tier unlocks. This is structural and rarely worth fighting; the question is whether the promo events are paying back vs the always-on cost.
- Promotion-to-recovery shape matters. Spring’s recovery to baseline took 6 days (15-20 Mar); Easter’s took 3 days. Faster recovery suggests Easter was driven by genuine seasonal demand; Spring may have pulled forward demand that would have come anyway, hurting margin.
- POS discounts contribute the surprise. A staff training week 26-28 Mar included a 20% off employee discount across 3 retail outlets, contributing $640 / day in line items. The card sums all sources; bonus categories (employee, wholesale, charity) need filtering at the source-tag level for cleaner promotional analysis.
Sibling cards merchants should reference together
Discount timeseries needs context. Pair with these:Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Analytics → Reports → “Discounts” → set the date range and resolution. Shopify’s report gives daily / weekly discount totals plus a breakdown by code. The headline number should align with this card to within sync-lag tolerance. Other Shopify Admin views:- Reports → Sales by discount: same data, different framing.
- Discounts page: shows current active codes; not historical.
- Apps like Lifetimely, Polar Analytics: build profit-aware discount views by allocating margin per discount; useful complement.
Cross-connector reconciliation:
Known limitations / merchant FAQs
Why is my baseline discount higher than I expected? Always-on promotions add up. Welcome codes, abandoned-cart vouchers, loyalty unlocks, employee discounts, and influencer codes that never expired all contribute to the baseline. Audit your active codes and check what is silently running. Why does the spike look smaller than my “20% off everything” promo would suggest? Several reasons:- Not every order used the code; usage rate is typically 30 to 60% of orders during a promo.
- Customers who would have ordered without the discount used it anyway, the discount went to natural-demand buyers, not incremental ones.
- Order mix shifted; not all line items were eligible (some categories often excluded).
totalShippingPrice set to zero rather than as a discount; check the Free vs Paid card for that view.
Refund timing, do refunds appear here?
No. The card sums discount-at-order-time. Refunds are tracked separately in Refund Value. Discount-and-refund interactions (e.g. customer used 20% off then returned the goods) need cross-card analysis.
Cancelled orders, are their discounts counted?
Yes. Discounts on cancelled orders bucket on their order day. To exclude, filter out cancelledAt IS NOT NULL orders manually.
Multi-currency, any impact?
The card sums in original currency without FX. Stores transacting in multiple currencies see distorted totals; filter to a single presentment currency.
Shopify Plus vs basic?
Plus stores use Shopify Functions for richer custom-discount logic (tier-based, exclusive bundles). The card captures the resulting discount value; the source attribution differs but the totals align.
Refresh cadence?
5 to 15 minute index lag; daily buckets recompute each cycle.
B2B vs DTC?
B2B price catalogues often apply standing discounts (10% off list for tier-A customers). These appear here daily as a steady contribution. A B2B-heavy store should expect a higher baseline; the dynamic peaks usually come from DTC promo events.
The card spiked unexpectedly, what should I do?
- Cross-reference Top Discount Codes for the peak day.
- Confirm the active campaign list, an unintended-still-live code is the most common cause.
- Check whether a partner / influencer code went viral.
- If the peak is genuine promo, evaluate ROI: what was the revenue lift? Did AOV move? Was the campaign profitable?