Average Order Value Trend, BigCommerce

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At a glance

Percentage change in average order value (basket size) versus the previous period. Falling AOV often signals over-discounting or a shift toward lower-priced products. Together with revenue_trend and order_count_trend, this card decomposes whether revenue is rising/falling because of order volume or basket size, two completely different problems with different fixes.

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Calculation

aov_trend (%) = (current_period_aov - previous_period_aov) ÷ previous_period_aov × 100

current_period_aov  = SUM(orders.total) ÷ COUNT(orders)
                      WHERE order_date IN [today - 30d, today)
                      AND payment_status IN ('paid', 'captured', 'completed')
previous_period_aov = SUM(orders.total) ÷ COUNT(orders)
                      WHERE order_date IN [today - 60d, today - 30d)
                      AND payment_status IN ('paid', 'captured', 'completed')

• If the previous period had zero orders, the trend is reported as n/a.
• Cancelled and refunded orders are excluded from both periods.
• B2B orders may be filtered separately if a per-channel profile is configured.

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Worked example

1. AOV is rising while revenue is falling. This is the diagnostic signature of “fewer-but-bigger orders”, typically caused by a discount cycle that pulled forward demand from deal-driven shoppers in the prior period. With the discount campaign ended, only higher-intent (typically higher-AOV) shoppers remain.
2. The discount-cycle artefact. In the prior period (16 Mar - 15 Apr), discount % of revenue was 12.4%; in the current period it is 0.0%. Discounts pull forward orders from price-sensitive shoppers who buy fewer items per order. Without the discount, those shoppers do not return; the remaining shoppers buy at full price with their normal basket size, which is structurally larger.
3. Why this is not entirely a win. A +7.3% AOV gain is offset by -12.7% order count and -6.3% revenue. The AOV rise is a side effect of losing the deal-driven cohort, not of upselling existing shoppers. True AOV growth (through bundle, threshold-based shipping incentive, upsell) would show AOV rising without order count falling.
4. Cross-reference checks to identify the cause:
   • discount_dependency, confirm whether the previous period was discount-heavy.
   • repeat_rate, repeat customers typically have higher AOV; rising AOV with rising repeat rate is healthy.
   • ga_conversion_rate, falling conversion with rising AOV typically means deal-shoppers are not converting at the higher prices.
   • order_count_trend, pair the two trends to see the full picture.
5. What “good AOV growth” looks like (for contrast):
   • AOV rises 5-10%
   • Order count holds flat or grows
   • Discount % of revenue stable or declining
   • Repeat rate steady or rising
   • Revenue grows in line with AOV
6. Recommended response, in priority order:
   • Day 1: Cross-check whether order count fell and revenue fell, if yes, this is the discount-cycle artefact, not an upsell win.
   • Day 1-3: Review whether the prior discount campaign should be re-run on a more targeted basis (segment-specific instead of site-wide).
   • Day 3-7: Plan upsell and bundle programmes (post-purchase recommendations, threshold-based free-shipping) to convert the AOV growth from artefact to durable lift.
   • Day 14: Confirm AOV holds the gain after the next promotional cycle.
7. What to do if AOV is falling instead. Falling AOV with stable or falling order count signals genuine basket-size erosion. Likely causes: (a) over-discounting making lower-priced items relatively more attractive, (b) category mix shift (lower-margin SKUs gaining share), (c) traffic source shift toward channels with lower-AOV cohorts (TikTok ads vs email, for example).

1. Read AOV trend %. Above 0% is healthy; below -5% triggers alert.
2. Pair with order count trend. Together they decompose revenue change.
3. Cross-reference discount, repeat rate, conversion for upstream causes.
4. Confirm whether AOV gain is durable (pair with repeat-rate, bundle-attach metrics).

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Sibling cards merchants should reference together

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Reconciling against the vendor’s own dashboard

• Analytics → Orders for the order list with totals.
• Analytics → In-Store Insights → Orders for BC’s own AOV display.

• shopify.aov_trend, adobe_commerce.aov_trend: same metric on other commerce platforms.
• ga_revenue_trend ÷ ga_orders_trend: GA-derived AOV; divergence reveals attribution gaps.

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Known limitations / merchant FAQs

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Tracked live in Vortex IQ Nerve Centre