Revenue Trend, BigCommerce

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At a glance

Percentage change in revenue versus the previous period. Revenue is the ultimate health indicator. Flat or declining revenue needs immediate attention, even 0% growth means the store is losing ground to inflation, while a 5% drop sustained over a quarter compounds to a 14% revenue gap by year-end. The card answers one question every BC owner asks every morning: are we growing, holding, or sliding?

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Calculation

revenue_trend (%) = (current_period_revenue - previous_period_revenue) ÷ previous_period_revenue × 100

current_period_revenue  = SUM(orders.total) WHERE order_date IN [today - 30d, today)
                          AND payment_status IN ('paid', 'captured', 'completed')
previous_period_revenue = SUM(orders.total) WHERE order_date IN [today - 60d, today - 30d)
                          AND payment_status IN ('paid', 'captured', 'completed')

• If previous_period_revenue is 0 (new store), the trend is reported as n/a rather than infinity.
• Cancelled and refunded orders are excluded from both period totals.
• Orders pending payment at the period boundary are excluded; they re-enter the calculation in the next ingestion cycle.

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Worked example

1. The headline is negative and breaches the alert threshold. A -6.3% decline over 30 days, if sustained, compounds to roughly -54% over a year. The card flags this as Action Needed and the dashboard surfaces it in the morning review.
2. The composition story matters as much as the headline. Order count dropped sharply (-12.7%) while AOV rose (+7.3%). This is a classic “fewer-but-bigger orders” pattern, often caused by: (a) a discount campaign in the previous period that pulled forward demand without retention, (b) a traffic acquisition issue (paid ad cuts, organic traffic loss, app store delisting, GMB issue), (c) seasonality not yet matched by the period-over-period comparison.
3. What is driving the order count drop, in priority order:
   • Cross-reference psi_perf_score_summary and crux_lcp_p75, site speed regressions reduce conversion and traffic retention.
   • Cross-reference gads_total_spend and gads_roas, paid traffic cuts directly reduce order volume.
   • Cross-reference ga_traffic_trend, traffic source shifts surface in this card before they hit revenue.
   • Cross-reference gsc_clicks_trend, organic search erosion lags by 1-3 weeks.
4. Why AOV is rising while orders fall. Likely the discount campaign in the prior period pulled in lower-value, deal-driven shoppers. With the campaign ended, only higher-intent (typically higher-AOV) shoppers remain. AOV gain does not offset order loss at this magnitude (-12.7% orders × +7.3% AOV ≈ -6.3% revenue, exactly matching the headline).
5. Recommended response, in priority order:
   • Day 1: Confirm whether a paid traffic cut occurred (check ad platform spend logs Mar-May).
   • Day 1-2: Confirm whether a site speed regression occurred (check CrUX trend cards).
   • Day 2-3: If neither traffic nor speed explains it, investigate competitive pricing shifts or category-level demand softening.
   • Day 3-7: Plan a coordinated traffic-recovery campaign. Pull weekly revenue trend daily until the slope reverses.
   • Day 14: Confirm trend slope has flattened or reversed. If still negative at -5%+, escalate to founder-level review.
6. What to do if the trend reverses on its own. A single -6.3% reading in May may be a comparison artifact: April included Easter promotions; May does not. Compare against the same period last year (year-over-year revenue) before declaring an actionable regression. Use Vortex Mind to investigate the cause if YoY also shows decline.

1. Read the headline %. Below -5% is an alert; below -10% is critical.
2. Decompose into orders × AOV. Identify which leg is moving.
3. Cross-reference traffic, conversion, and speed cards for upstream causes.
4. Confirm against prior 90D trend and YoY to rule out seasonality.

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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 and totals over a date range.
• Analytics → In-Store Insights → Revenue for BC’s own period-over-period comparison.
• Reports → Sales Tax Report when reconciling tax-inclusive vs tax-exclusive totals.

• shopify.revenue_trend, adobe_commerce.revenue_trend: same metric on other commerce platforms.
• ga_revenue_trend: GA-attributed revenue; divergence between this card and ga_revenue_trend reveals attribution gaps. If BC revenue is up 5% but GA-attributed revenue is down, GA tracking has likely broken.

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Known limitations / merchant FAQs

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Tracked live in Vortex IQ Nerve Centre