> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # AOV with/without Discount, Shopify > Discounted-order AOV vs full-price-order AOV, exposes whether promos cannibalise margin. How to read it, why it matters, and how to act on it. **Metrics type:** [Supporting Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ecommerce Platform](/nerve-centre/connectors#connectors-by-type) > Discounted-order AOV vs full-price-order AOV, exposes whether promos cannibalise margin. ## At a glance > Two AOV figures side-by-side: orders that used a discount code or automatic discount vs orders that didn't. Tells you whether promotions are growing baskets or just cutting margin. | | | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | Two parallel calculations: `AVG(totalPrice WHERE discountCode IS NOT NULL OR totalDiscounts > 0)` and `AVG(totalPrice WHERE discountCode IS NULL AND totalDiscounts = 0)`. The card surfaces both numbers and the spread between them. | | **VAT / tax treatment** | Both sides inherit `taxesIncluded` mode of the store; comparable apples-to-apples. | | **Shipping** | Included (sits in `totalPrice`). | | **Discounts** | The discount-side reflects post-discount totals; the spread between full vs discounted AOV understates the gross-line-item gap. | | **Refunds** | NOT deducted on either side. | | **Cancelled / voided orders** | Included if Shopify indexed them. | | **Currency** | Multi-currency arithmetic without FX. Filter to one currency for clean reads. | | **Channels / sources** | All channels. POS often has manual discounts that look like discount-orders here. B2B with contracted pricing technically counts as "discounted". | | **Time window** | `30D` (default 30D rolling) | | **Alert trigger** | None; descriptive comparison. | | **Roles** | owner, marketing | ## Calculation Calculated automatically from your Shopify 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 US activewear DTC brand on Shopify Plus, runs WELCOME10 + monthly seasonal codes. 30D window 12 Apr 26 to 12 May 26. | Cohort | Orders | AOV | Note | | ----------------- | ---------- | -------- | -------------------------------------- | | Discounted orders | 4,280 | \$112 | WELCOME10, FRIENDS25, TRAINING20 codes | | Full-price orders | 6,150 | \$87 | No code, no automatic discount | | **All orders** | **10,430** | **\$97** | Blended | ```text theme={null} Discount uplift = $112 - $87 = $25 per discounted order, +28.7% ``` Six things to notice: 1. **Discounted orders are larger.** $112 vs $87 = customers using codes spend 28.7% more. Common pattern; codes act as bundling triggers (people add items to qualify, or use the code on bigger pre-planned purchases). 2. **The naive read is wrong.** "Discounted AOV is higher" doesn't mean "discounts grow basket". The gap reflects code self-selection: customers who plan a £100 purchase look up codes; impulse buyers don't. Codes don't *cause* the larger basket; they *attract* the larger-basket cohort. 3. **The margin question.** A WELCOME10 (10% off) on a $112 order = $11 discount. If product margin is 50%, customer gross margin per order goes from $43.50 (full $87) to $44.50 (discounted $112 - $11 - $56 COGS). Marginally better. But the comparison should be: would the customer have bought at $87 without the code? For most return customers, yes; the brand effectively gave away $11. 4. **First-time buyer cohorts are different.** New-customer code redemptions are usually true incremental; the customer wouldn't have bought without it. Existing-customer code redemptions are mostly cannibalistic. 5. **Stacked codes inflate the discount-AOV.** If your store allows code stacking, the inflation in discounted-AOV is over-counted; customers stacking 3 codes appear in the discount cohort but their \$-discount is much higher. 6. **The spread shrinks during sales.** During a 25%-off site-wide event, the full-price cohort shrinks (everyone uses the code) and the spread becomes meaningless. Use this card outside major-promo windows for clean reads. ## Sibling cards merchants should reference together The discount-vs-full-price comparison is one slice. The companions: | Card | Why pair it with AOV with/without Discount | | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | [Average Order Value](/nerve-centre/kpi-cards/shopify/average-order-value) | The blended cousin. Mathematical relationship: AOV = (discount\_AOV × discount\_orders + full\_AOV × full\_orders) / total\_orders. | | [Total Discount Used](/nerve-centre/kpi-cards/shopify/total-discount-used) | The £-cost of the discount-cohort. Pair to compute net margin per cohort. | | [Top Discount Codes](/nerve-centre/kpi-cards/shopify/top-discount-codes) | Drills into which codes drove the discount cohort. | | [Discount % of Revenue](/nerve-centre/kpi-cards/shopify/discount-of-revenue) | The ratio version; helps frame the discount-cohort spend in context of total revenue. | | [Repeat Customer Rate](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) | Cohort overlap: discount-buyers often skew first-time; repeat-buyers skew full-price. | | [New Customers](/nerve-centre/kpi-cards/shopify/new-customers) | Cohort overlap; new customers heavily use first-order codes. | | [Discount Over Time](/nerve-centre/kpi-cards/shopify/discounts-given-over-time) | Trend cousin; spike in discount cohort tied to specific promo events. | ## Reconciling against the vendor's own dashboard **Where to look in Shopify Admin:** Shopify doesn't expose a direct discount-vs-full-price AOV comparison; reconstruct from: * **Analytics → Reports → "Discounts"**: shows discount-code usage and value. Use to identify the discount-cohort population. * **Orders → Filter by "has discount"**: order list scoped to discount-cohort. Sum totals and divide by count for AOV. * **Reports → Sales attributed to first-time vs returning customers**: tangentially useful, since first-time customers use codes more. **Why our number may legitimately differ from a manual reconstruction:** | Reason | Direction | Why | | ------------------------------ | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Definition of "discounted"** | Either | We flag any order with `totalDiscounts > 0` OR a discount code. Shopify reports may use just code-based or just automatic-discount-based depending on the report. | | **B2B contracted pricing** | Theirs may differ | We treat B2B price-list discounts as "discounts"; Shopify Plus reports may treat them as separate price-tier orders, not discounts. | | **Shipping discounts** | We include | Shipping-only discount codes contribute to the discount cohort here; some Shopify reports separate shipping-discounts. | | **Time zone** | Boundary | UTC vs store time zone. | | **Sync lag** | Ours lower for "today" | Most-recent 5 to 15 minutes of orders may not be in. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | ---------------------------------------------------------------------------------------------------- | --------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | | [`klaviyo.kl_revenue_from_email`](/nerve-centre/klaviyo/kl_revenue_from_email) (when connected) | Indirect | Klaviyo flow emails often carry discount codes; the discount cohort here usually contains a high share of email-attributed orders. | | [`google_ads.gads_promo_attributed_revenue`](/nerve-centre/google_ads/gads_promo_attributed_revenue) | Indirect | Paid-search-driven promo orders contribute to the discount cohort. | ***
Documentation cross-reference (for agencies running multiple platforms) * [`bigcommerce.shp_aov_discount`](/nerve-centre/bigcommerce/shp_aov_discount) * [`adobe_commerce.shp_aov_discount`](/nerve-centre/adobe_commerce/shp_aov_discount)
## Known limitations / merchant FAQs **Why is my discount AOV higher than full-price AOV?** Self-selection. Customers planning a larger purchase actively look up codes; impulse and small-basket buyers often don't bother. The code didn't cause the larger basket; the larger-basket-intent customers found the code. **Should I take this as proof discounts are profitable?** No. The right comparison is: would the discount-cohort customers have bought at full price without the code? For first-timers, often no (incremental). For repeats, often yes (cannibalistic). The blended profitability depends on the cohort mix. **My discount AOV is *lower* than full-price AOV. What's that mean?** Either: 1. **Mass-market codes** (BLACK20, sale-wide events) attract impulse buyers who would have spent less anyway. Heavy first-time-buyer skew on small baskets. 2. **Free-shipping-threshold codes** that customers use to buy small things just below the threshold (use code instead of adding filler). The code subverts the threshold rather than complementing it. 3. **Affiliate / influencer codes** with broad audiences and low-intent traffic. The cohort behaves like cold-acquisition. **How can I tell if a specific code is profitable?** Pair this card with [Top Discount Codes](/nerve-centre/kpi-cards/shopify/top-discount-codes) and segment by code. For each code: 1. Compute average \$-discount per redemption (code rate × code AOV). 2. Compute incremental orders (redemptions that wouldn't have happened without the code; estimate via attribution data). 3. Net profitability = incremental\_orders × avg\_margin\_per\_order − total\_discount\_cost. Most welcome codes are profitable; most existing-customer reactivation codes are not. **Why does discount AOV jump during sale events?** Because the full-price cohort shrinks dramatically (everyone uses the site-wide code), the discount cohort takes over. The card becomes less informative during major sales. Filter the window to exclude sale weeks for clean reads. **My subscription store, do recurring billings count?** Yes. If the customer applied a code on the original subscription order, every recurring billing inherits the discount and contributes to the discount-AOV cohort. Subscription brands often see persistent discount-AOV cohorts because of this propagation. **Can I segment this by first-time vs returning?** Not directly on this card. But pair with [Repeat Customer Rate](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) and [New Customers](/nerve-centre/kpi-cards/shopify/new-customers). First-time discounted orders should be roughly equal to first-time orders (most first-timers use the welcome code); returning discounted orders are more nuanced. **Action playbook for using this card:** 1. **If discount-AOV >> full-price-AOV** by 20%+: codes attract premium-basket buyers. Lean into bigger bundle codes ("Save 20% over \$200"). 2. **If discount-AOV ≈ full-price-AOV**: codes are doing nothing for basket size. Either reposition (welcome-only) or sunset. 3. **If discount-AOV \<\< full-price-AOV** by 15%+: codes are cannibalising small baskets. Tighten code usage rules (one-time-per-customer, minimum-basket). 4. **Audit code-stacking allowance**: stacking inflates the discount-AOV without delivering corresponding margin. 5. **Segment by code source**: welcome codes vs reactivation codes vs influencer codes have very different economics. The blended view hides which. *** ### Tracked live in Vortex IQ Nerve Centre *AOV with/without Discount* is one of hundreds of KPI pulses Vortex IQ tracks across Shopify 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](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.