> ## 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. # Customer Acquisition Trend, Adobe Commerce > Customer Acquisition Trend for Adobe Commerce stores. Tracked live in Vortex IQ Nerve Centre. 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) ## At a glance > Daily distinct-customer count over trailing 90 days. The trend chart for [Customer Count](/nerve-centre/kpi-cards/adobe-commerce/unique-customers), with optional new-vs-returning split. Adobe Commerce merchants often see a 7-day weekly pattern (B2B procurement-heavy weekday peaks, consumer weekend dips) plus monthly cycles (B2B end-of-month PO clustering). | | | | ------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | Daily `CARDINALITY(customer_email)` over 90 days. Each day is one bucket showing distinct customers who placed at least one order that day. Optional decomposition: new (first-ever order) vs returning. | | **API field** | `customer_email`, `created_at`, optional `customer_id` from `GET /rest/V1/orders`. New-vs-returning detection via per-customer `created_at` history. | | **VAT / tax treatment** | n/a, count metric. | | **Shipping inclusion** | n/a. | | **Discounts** | n/a. | | **Credit Memo refund treatment** | A customer who later got a full refund still counts on the original-order day. | | **`state` machine inclusion** | All states except `canceled`. | | **`pending_payment` quirk** | Included. | | **Multi-currency `grand_total` vs `base_grand_total`** | n/a for the count. | | **Store View scope (`store_id`)** | All Store Views; same email across Store Views deduped per day. | | **Time window** | `90D` daily granularity. 7-day moving average overlay typical. | | **Alert trigger** | None by default. | | **Roles** | owner, marketing | ## Calculation ``` DATE_HISTOGRAM CARDINALITY(customer_email) WHERE date BETWEEN [period_start, period_end] ``` ## Worked example A homewares brand on Adobe Commerce 2.4.6, US/UK/B2B Store Views. 90-day window ending Monday 4 May 26. **90-day overview:** | Metric | Value | | -------------------------------- | ----------------------------- | | Total distinct customers (90D) | 11,420 | | Average daily distinct customers | 320 | | Standard deviation | 65 | | Highest single-day count | 542 (after Mother's Day push) | | Lowest single-day count | 184 (Christmas Day) | **Weekly pattern (median day-of-week):** | Day | Median customers | | --- | ---------------- | | Mon | 380 | | Tue | 410 | | Wed | 405 | | Thu | 395 | | Fri | 350 | | Sat | 240 | | Sun | 220 | **New-vs-returning split (90D total):** | Cohort | Customers | % of total | | ---------------------- | --------- | ---------- | | New (first-ever order) | 4,640 | 40.6% | | Returning | 6,780 | 59.4% | What this is telling marketing: 1. **Weekday-heavy customer pattern.** Tuesday and Wednesday are peak; weekend dips reflect B2B-mix dominance (B2B is overwhelmingly weekday). 2. **40.6% new customer rate over 90 days** is a healthy acquisition cadence. Consumer-side; on B2B it's typically \<5%. 3. **The Mother's Day spike** (542 on the peak day, \~70% above 7-day moving average) is a campaign-driven event. Useful to see how the cohort acquired performs over the next 90 days for ROAS attribution. 4. **The Christmas Day trough** (184) is the lowest single day. Expected: most B2B and consumer activity pauses Christmas Day. Use this baseline rather than the moving average for "is the business operating normally" detection on holidays. 5. **Cross-checking [New Customers](/nerve-centre/kpi-cards/adobe-commerce/new-customers) trend**: new-customer rate has been stable at \~40% for 6 of the 12 weeks; rose to \~50% in weeks 8 and 9 (Mother's Day campaign push) then receded. The campaign created a transient lift in new-customer share. 6. **Cross-checking [Repeat Customer Rate](/nerve-centre/kpi-cards/adobe-commerce/repeat-customer-rate)**: 59.4% returning over 90 days is in the healthy range; well-retained brands run 55-70%, struggling ones below 50%. 7. **Strategic question**: should the merchant invest in a Saturday-Sunday weekend campaign to lift weekend customer count? The 40% weekday-vs-weekend gap is a B2B effect; converting consumer weekend traffic better could close the gap. Cross-check with paid-traffic conversion rates by day-of-week. ## Sibling cards merchants should reference together | Card | Why pair it with Customer Trend | | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------- | | [Customer Count](/nerve-centre/kpi-cards/adobe-commerce/unique-customers) | Single-period summary; this card is its trend chart. | | [New Customers](/nerve-centre/kpi-cards/adobe-commerce/new-customers) | The first-time-orderer subset. | | [Repeat Customer Rate](/nerve-centre/kpi-cards/adobe-commerce/repeat-customer-rate) | The returning subset. | | [Daily Order Trends](/nerve-centre/kpi-cards/adobe-commerce/daily-order-trends) | Sibling chart at the order level (not deduped to customer). | | [Revenue Over Time](/nerve-centre/kpi-cards/adobe-commerce/revenue-over-time) | The dollar-weighted view of the same period. | | [Customer Segments](/nerve-centre/kpi-cards/adobe-commerce/customer-spend-segments) | Per-cohort rollup. | | [`google_analytics.ga_users_over_time`](/nerve-centre/google_analytics/ga_users_over_time) | Top-of-funnel comparison. | | [`shopify.customer_trend`](/nerve-centre/kpi-cards/shopify/customer-acquisition-trend) | Cross-platform peer. | ## Reconciling against the vendor's own dashboard **Where to look in Adobe Commerce Admin:** > **Reports > Customers > Customers by Number of Orders** with a date range gives the per-customer activity, not a daily trend. Manual aggregation needed for the daily series. > **Sales > Orders** with date filter, group by date manually via export. > **Dashboard > Last Orders** is real-time but only shows the last 5 orders. For B2B (Adobe Commerce paid edition): > **Customers > Companies** with order history, manual per-Company timeline. **Why our number may legitimately differ from a manual Admin computation:** | Reason | Direction of divergence | | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------- | | **Time-zone**. Admin in Store View timezone; card UTC. Day boundaries shift. | ±1 day per bucket | | **Email vs `customer_id`**. Card uses email which captures both registered and guests; Admin's customer-grid is registered only. | Card count higher | | **Multi-Store-View dedup**. Card dedupes the same email across Store Views. | Card slightly lower than Store-View-summed manual | | **`canceled` exclusion**. Card excludes; Admin includes unless filtered. | Card count slightly lower | **Cross-connector reconciliation (when these connectors are connected for this merchant):** | Pair | Expected relationship | What divergence tells you | | ------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------- | -------------------------------------------------------------- | | [`google_analytics.ga_users_over_time`](/nerve-centre/google_analytics/ga_users_over_time) | GA4 users with `purchase` event ≈ Adobe customer trend × (1 - tracking gap) | Material divergence indicates GA4 tag-fire issues at checkout. | | ESP active-recipient daily count | ESP "active" definition varies; usually broader | Trend correlation more useful than absolute alignment. | ***
Same-metric documentation cross-reference (for agencies running multiple platforms) * [`shopify.customer_trend`](/nerve-centre/kpi-cards/shopify/customer-acquisition-trend) * [`bigcommerce.customer_trend`](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend)
## Known limitations / merchant FAQs **Why does my customer count drop on weekends?** Adobe Commerce stores typically run B2B-heavy or B2B-mixed; B2B procurement is weekday-only. The weekend dip reflects the weekday-weekend B2B/consumer split. Pure-DTC consumer brands without B2B see weekend peaks instead. **The card shows 320/day average but my CRM shows 5,000 active customers, contradiction?** No. 320/day average means about 320 distinct customers order on a typical day; over 90 days that aggregates to \~11,400 distinct customers (because most are repeat). Both numbers are correct; they answer different questions. **Adobe Commerce vs Magento Open Source: any difference?** None at the calculation. The card runs identically. **My multi-store Adobe Commerce, can I see per-Store-View trends?** Yes, configure per-Store-View variants. Useful for spotting region-specific patterns (US Memorial Day spike, UK Bank Holiday dip). **A specific day shows zero customers, what happened?** Two causes: (a) actual zero-business day (Christmas, New Year on certain configurations), (b) data sync gap (the OpenSearch index missed orders for that day, usually self-resolves at next sync). The shape of the surrounding days disambiguates. **The new-vs-returning split shows 40% new, but my acquisition spend isn't growing, sustainable?** 40% new on 90-day window means new acquisition is keeping up with churn-and-growth. If acquisition spend is flat but new-customer share is rising, your acquisition efficiency is improving. If new share is rising AND retention is dropping, you're acquiring faster than you're retaining (a leaky bucket). **Why doesn't this card show acquisition channel?** Acquisition channel attribution requires marketing-platform integration (Google Ads, Meta) cross-tabbed with Adobe order data. This card is the count view; for channel attribution see GA4-side and ad-platform-side cards. **B2B Companies cross weekday peaks but my consumer side is steady, normal?** Yes. The aggregate trend has both signals; per-segment trends look very different. B2B mid-week spikes (Tue, Wed) and consumer Saturday peak balance out the blended view. *** ### Tracked live in Vortex IQ Nerve Centre *Customer Acquisition Trend* is one of hundreds of KPI pulses Vortex IQ tracks across Adobe Commerce 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.