> ## 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, Shopify > Customer Acquisition Trend for Shopify 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 / weekly timeseries of distinct customers placing orders. The trend version of [Customer Count](/nerve-centre/kpi-cards/shopify/unique-customers): the shape of acquisition + retention behaviour over time. | | | | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **What it counts** | `DATE_HISTOGRAM(week, CARDINALITY(customer.id))` over the 90-day window. Each week shows the distinct customer count for that week. A customer who ordered in three different weeks contributes to three weekly buckets. | | **API endpoint** | `Admin GraphQL. Order.customer.id`, bucketed by `Order.createdAt` week. | | **VAT / tax treatment** | Not applicable (count metric). | | **Shipping** | Not applicable. | | **Discounts** | Not applicable. | | **Refunds** | Refunded-order customers still count. | | **Cancelled / voided orders** | Cancelled-order customers count if customer was attached. | | **Currency** | Multi-currency safe. | | **Channels / sources** | Online + POS-with-account + B2B contribute. POS-guest excluded. | | **Aggregation note** | Weekly customers sum to MORE than monthly customers (a customer who orders in 4 weeks counts 4 times in the weekly view but once in the monthly view). Use this card for shape, not summed totals. | | **Time window** | `90D` (typically weekly buckets) | | **Alert trigger** | None on this card directly; significant drops surface via the [Customer Count](/nerve-centre/kpi-cards/shopify/unique-customers) period comparison. | | **Roles** | owner, marketing | ## Calculation ``` DATE_HISTOGRAM CARDINALITY(customer.id) WHERE date BETWEEN [period_start, period_end] ``` ## Worked example A UK DTC apparel brand on Shopify. Period: 12 Feb 26 to 12 May 26. Weekly distinct-customer counts: | Week starting | Distinct customers | New customers (subset) | Returning (subset) | Notes | | ------------- | ------------------ | ---------------------- | ------------------ | --------------------------------- | | 12 Feb 26 | 478 | 281 | 197 | Steady | | 19 Feb 26 | 502 | 298 | 204 | | | 26 Feb 26 | 511 | 305 | 206 | | | 04 Mar 26 | 1,142 | 854 | 288 | **Spring drop launch, paid push** | | 11 Mar 26 | 928 | 619 | 309 | Lift continues | | 18 Mar 26 | 612 | 312 | 300 | Tapering | | 25 Mar 26 | 521 | 214 | 307 | Returning steady, new normalising | | 01 Apr 26 | 488 | 198 | 290 | | | 08 Apr 26 | 461 | 187 | 274 | | | 15 Apr 26 | 471 | 191 | 280 | Easter push | | 22 Apr 26 | 502 | 211 | 291 | | | 29 Apr 26 | 488 | 188 | 300 | | | 06 May 26 | 491 | 189 | 302 | | Five things to notice: 1. **The 04 Mar spike is the spring drop wave.** Distinct weekly customers more than doubled, driven by ad-spend lift. The wave decayed across 3 weeks (1,142 → 928 → 612), a typical drop-launch shape. Returning-customer count remained elevated for longer than new-customer count (304 vs 854 → 191), evidence the returning cohort responded later to the email campaign than new acquisition responded to ads. 2. **The acquisition wave didn't lift the returning baseline.** Pre-drop returning was 197 to 206 / week; post-drop 274 to 302 / week. That is a 35% lift in returning customers maintained for 8 weeks. Some of those new acquisitions converted into early-repeat buyers; the rest of the lift is the existing base coming back for the new collection. Pair with [New Customers](/nerve-centre/kpi-cards/shopify/new-customers) to confirm the cohort split. 3. **Returning-customer steadiness is the retention health signal.** A flat 290 to 305 returning customers / week post-spike means the retention engine is steady. A step-down to 200 returning would be a churn alarm even with new-customer surge masking the headline. 4. **Easter (15 Apr) shows a small bump.** Acquisition campaigns produce visible week-level signatures here; you can audit campaign effectiveness against this card directly. A campaign that produces no visible lift in the weekly distinct count almost certainly did not work. 5. **POS-guest checkout invisible.** The brand's pop-up retail event on 21 Mar at a London market generated 80+ in-person sales but most without email capture; weekly distinct-customer count does not reflect them. POS-guest is a known gap for omnichannel brands. ## Sibling cards merchants should reference together Customer trend is the timeseries view of [Customer Count](/nerve-centre/kpi-cards/shopify/unique-customers). Pair with these for context: | Card | Why pair it with Customer Trend | What the combination tells you | | ------------------------------------------------------------------------------------------ | ------------------------------- | --------------------------------------------------------------------------------------------------------------- | | [Customer Count](/nerve-centre/kpi-cards/shopify/unique-customers) | The 30D summary view. | Trend reveals the shape; count gives the headline. | | [New Customers](/nerve-centre/kpi-cards/shopify/new-customers) | Acquisition subset over time. | New-vs-returning split by week shows the engine driving the headline. | | [Repeat Rate](/nerve-centre/kpi-cards/shopify/repeat-customer-rate) | Retention proportion. | A rising customer trend without rising repeat rate = pure acquisition; sustainable only with the funnel funded. | | [Orders Over Time](/nerve-centre/kpi-cards/shopify/orders-over-time) | Order timeseries counterpart. | Orders per customer per week = frequency. | | [Revenue Over Time](/nerve-centre/kpi-cards/shopify/revenue-over-time) | Revenue timeseries. | Revenue ÷ customer trend = revenue per customer per week. | | [Churn Risk](/nerve-centre/kpi-cards/shopify/customer-churn-risk) | Lapsed cohort. | A flat customer trend masking rising churn is a leaking-bucket alarm. | | [`google_ads.google_spend_over_time`](/nerve-centre/google_ads/google_spend_over_time) | Acquisition channel input. | Compare ad-spend timeseries to customer trend; the lag and lift reveal CAC efficiency. | | [`google_analytics.ga_users_over_time`](/nerve-centre/google_analytics/ga_users_over_time) | Top-of-funnel counterpart. | GA4 users vs Shopify customer trend = funnel conversion shape. | ## Reconciling against the vendor's own dashboard **Where to look in Shopify Admin:** [Analytics → Reports → "Customers over time"](https://admin.shopify.com/reports/customers_over_time) → set the date range to match this card and the resolution to weekly. Shopify's report should align week-to-week with this card. Other Shopify Admin views: * **Reports → First-time vs returning over time**: shows new vs returning segments of the same trend; companion view. * **Reports → Sales by month**: revenue companion; pair to derive revenue-per-customer trend. * **Apps like Polar Analytics, Triple Whale**: build richer customer-trend cohort views (acquisition cohorts, retention curves). Useful complements. **Why our number may legitimately differ from Shopify Admin:** | Reason | Direction | Why | | -------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | | **Time zone** | Boundary days | Shopify uses store time zone; Vortex IQ uses store time zone for daily buckets but UTC for window edges. | | **Bucket alignment** | Either | "Week" can start Mon (ISO) or Sun (US). Shopify defaults to Sun-start; Vortex IQ to Mon-start (configurable). Verify when comparing. | | **Test orders** | Ours slightly higher | The card does not yet filter `Order.test = true`. | | **Customer merging** | Transient | Pre-merge data may show duplicates; reconciles after merging. | | **Channel filter** | Either | Shopify can be filtered by sales channel; this card aggregates. | | **Sync lag** | Ours lower for "today" | 5 to 15 minute lag. The latest week's bar will rise during the day. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | ---------------------------------------------------------------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------- | | [`google_analytics.ga_users_over_time`](/nerve-centre/google_analytics/ga_users_over_time) | GA4 users >> Shopify customers | Conversion rate ties them. | | [`klaviyo.klaviyo_subscribers_over_time`](/nerve-centre/klaviyo/klaviyo_subscribers_over_time) | Email signup vs purchase trend | Often diverge; email signups are top-of-funnel, customer trend is bottom-of-funnel. | ***
Same-metric documentation cross-reference (for agencies running multiple platforms) The same definition lives on other commerce platforms. Cross-platform navigation only. * [`bigcommerce.customer_trend`](/nerve-centre/kpi-cards/bigcommerce/customer-acquisition-trend) * [`adobe_commerce.customer_trend`](/nerve-centre/kpi-cards/adobe-commerce/customer-acquisition-trend)
## Known limitations / merchant FAQs **Why does the trend not sum to the 30D customer count?** Because the same customer can order in multiple weeks. A customer who orders in 4 weeks counts 4 times in the weekly view but once in the 30D distinct view. Use this card for shape (week-over-week), not for summed totals. **Why is the latest week's bar lower?** Sync lag plus partial-week. The current week is in progress; bars are partial until end of week. Yesterday and earlier weeks are caught up. **My weekly trend has a strong day-of-week pattern, why?** Weekly bucketing usually smooths day-of-week effects, but if a brand's promotion calendar lands on specific days (e.g. always-Friday drops), week-on-week distinct customers will reflect campaign timing more than baseline behaviour. **Multi-store, can I see the combined customer trend?** No, each store is a separate integration. Multi-store rollups are on roadmap. **Multi-currency, any impact?** None on the count; weekly customer trend is currency-blind. **Shopify Plus vs basic?** No definitional difference. Plus stores typically have larger absolute numbers; the trend shape is qualitatively similar. **Refresh cadence?** Webhooks fire within seconds; index lag 5 to 15 minutes. Weekly buckets recompute on each ingest. **B2B vs DTC, any difference?** B2B customer counts are smaller and more event-driven; the trend can show large weekly swings tied to specific account activity. DTC trends are smoother. Mixed B2B + DTC stores benefit from filtering by tag (manual today; on roadmap). **The trend dropped sharply last week, what should I do?** 1. Cross-reference [Orders Over Time](/nerve-centre/kpi-cards/shopify/orders-over-time). Did orders drop too? If yes, real volume issue; if no, customers might be ordering more per visit. 2. Cross-reference [Revenue Over Time](/nerve-centre/kpi-cards/shopify/revenue-over-time). A flat customer count + flat revenue suggests this is real, not artefact. 3. Check ad spend channels: a paused or under-performing ad campaign is the most common cause of a single-week customer drop. 4. Check site performance: a site outage or checkout error in the affected week often correlates. 5. Check email programme: a missed weekly campaign can show up as a returning-customer dip. *** ### Tracked live in Vortex IQ Nerve Centre *Customer Acquisition Trend* 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.