> ## 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 Spend Segments, Adobe Commerce > Customer Spend Segments 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 > Customer base distributed into LTV cohorts (top 10%, mid 60%, bottom 30%) plus B2B-vs-consumer split. Adobe Commerce paid edition has a native "Customer Segments" feature for marketing rules; this card is the analytical complement, surfacing how the cohort mix evolves and where revenue is concentrated. The 80/20 rule is usually 70/30 on Adobe Commerce because B2B Companies dominate the top. | | | | ------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **What it counts** | For each customer (email-based identity, optionally Company-grouped on B2B), compute trailing-90-day `SUM(grand_total)` and rank. Bucket into LTV deciles, then aggregate to top-10% / mid-60% / bottom-30%. Cross-tab against B2B/B2C segment. Returns the count and revenue contribution per cohort. | | **API field** | `customer_email`, `customer_id`, `grand_total`, `customer_group_id`, `extension_attributes.company_attributes.company_id` from `GET /rest/V1/orders`. | | **VAT / tax treatment** | Tax-inclusive on B2C; exempt on most B2B. Cohort ranking is by `grand_total`, so B2C customers in the UK appear slightly higher in the LTV ranking than equivalent-revenue US B2B customers. | | **Shipping inclusion** | Included via `grand_total`. | | **Discounts** | Deducted (post-promotion). | | **Credit Memo refund treatment** | NOT subtracted. Gross LTV. A high-refund customer may rank above their net contribution. | | **`state` machine inclusion** | All states except `canceled`. | | **`pending_payment` quirk** | Included. | | **Multi-currency `grand_total` vs `base_grand_total`** | Uses `base_grand_total` for cross-currency LTV ranking. | | **Store View scope (`store_id`)** | All Store Views by default. Per-Store-View variants useful when consumer cohort is regionally heterogeneous. | | **B2B Company aggregation** | Each Company aggregated as one customer for the B2B segment by default. | | **Time window** | `90D` | | **Alert trigger** | None by default. | | **Roles** | owner, marketing | ## Calculation ``` RANGE(grand_total) by spend bucket WHERE date BETWEEN [period_start, period_end] ``` ## Worked example A homewares brand on Adobe Commerce 2.4.6, B2B Companies module enabled. Snapshot Monday 4 May 26. **Trailing 90-day customer base, 9,820 active customers (180 B2B Companies + 9,640 consumers):** | Cohort | Customer count | 90D revenue | Revenue share | Avg per customer | | -------------------------------- | -------------- | ----------- | ------------- | ---------------- | | **Top 10%** (982 customers) | 982 | \$1,640,000 | 65% | \$1,670 | | - of which B2B Companies | 142 | \$1,210,000 | 48% | \$8,520 | | - of which consumer | 840 | \$430,000 | 17% | \$512 | | **Mid 60%** (5,892 customers) | 5,892 | \$720,000 | 28% | \$122 | | **Bottom 30%** (2,946 customers) | 2,946 | \$172,000 | 7% | \$58 | | **Total** | 9,820 | \$2,532,000 | 100% | \$258 | What this is telling marketing: 1. **The top 10% generates 65% of revenue.** Classic Pareto. On Adobe Commerce, the top decile is dominated by B2B (142 of 982 customers but $1.21m of $1.64m). 2. **142 B2B Companies generate 48% of total revenue.** Each is worth \~$8,500 of trailing-90-day revenue, ~$34,000 annualised. Loss of any single top-decile B2B account is a 0.3% hit to total revenue. 3. **The mid-60% consumer cohort** (mostly mid-LTV repeat consumers) generates 28% of revenue from 60% of customers. Healthy retention layer. 4. **Bottom 30% of customers contribute only 7% of revenue.** Mostly one-time gift buyers, single-purpose shoppers, and recently-acquired-not-yet-repeat. Marketing investment in this cohort is generally unprofitable; a "first repeat purchase" cadence may be worth running, but acquisition spend on lookalikes of this cohort is wasteful. 5. **Cross-checking [Churn Risk](/nerve-centre/kpi-cards/adobe-commerce/customer-churn-risk)**: top-10% has 8% churn risk, mid-60% has 32%, bottom-30% has 44%. The retention investment priority is mid-60%: high churn risk, material revenue contribution, manageable count for an email-cadence intervention. 6. **Cross-checking [B2B Revenue Share](/nerve-centre/kpi-cards/adobe-commerce/b2b-revenue-share)**: 48% B2B share is consistent with this cohort decomposition (the Companies are mostly in the top decile). 7. **Strategic implications**: * B2B account-based marketing focus on top-10% B2B Companies (142 accounts, \~\$1.2m revenue at stake). * Consumer retention focus on mid-60% (5,892 customers, \~\$720k revenue at stake). * Acquisition focus reviews the lookalike profile of top-10% consumers (840 customers, \$430k revenue) rather than blanket lookalikes of all-customers. The point: customer base on Adobe Commerce is heavily concentrated. Cohort decomposition reveals where to invest marketing effort. ## Sibling cards merchants should reference together | Card | Why pair it with Customer Segments | | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------ | | [Customer Count](/nerve-centre/kpi-cards/adobe-commerce/unique-customers) | The denominator. | | [Churn Risk](/nerve-centre/kpi-cards/adobe-commerce/customer-churn-risk) | Per-cohort churn prioritisation. | | [B2B Revenue Share](/nerve-centre/kpi-cards/adobe-commerce/b2b-revenue-share) | Top-decile dominance comes from B2B. | | [B2B Accounts Gone Quiet](/nerve-centre/kpi-cards/adobe-commerce/b2b-accounts-gone-quiet) | Top-decile B2B silence is the highest-priority Sales action. | | [Repeat Customer Rate](/nerve-centre/kpi-cards/adobe-commerce/repeat-customer-rate) | Lifetime repeat behaviour underlies cohort stability. | | [Total Revenue](/nerve-centre/kpi-cards/adobe-commerce/total-revenue) | The aggregate. | | [AOV](/nerve-centre/kpi-cards/adobe-commerce/average-order-value) | AOV varies materially across cohorts. | | [`shopify.customer_segments`](/nerve-centre/kpi-cards/shopify/customer-spend-segments) | Cross-platform peer. | ## Reconciling against the vendor's own dashboard **Where to look in Adobe Commerce Admin:** > **Customers > Segments** (Adobe Commerce paid edition only) lets you define rule-based segments for marketing automation. The Admin Segments feature is rule-driven (e.g. "customers with >5 orders in last 12 months"); this card uses LTV ranking which the Admin doesn't natively compute. > **Reports > Customers > Customers by Orders Total** ranks customers by lifetime revenue. Decile-bucketing requires CSV export and spreadsheet computation. > **Reports > Customers > Customers by Number of Orders** ranks by order count, complementary to LTV ranking. For B2B (Adobe Commerce paid edition): > **Customers > Companies** with sort by 90-day revenue (manual via export-and-pivot). **Why our number may legitimately differ from Admin:** | Reason | Direction of divergence | | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | | **LTV vs Order Count ranking**. Card uses LTV (sum of grand\_total). Admin's order-count-rank shows different ordering. | Material if a few large orders skew LTV | | **B2B Company aggregation**. Card aggregates by Company; Admin shows by buyer-email. | Material if Companies have multiple buyers | | **`canceled` exclusion**. Card excludes; Admin includes unless filtered. | Card LTV slightly higher | | **Time window**. Card uses 90 days; Admin reports default to all-time. | Different cohorts entirely | | **Refund-adjustment**. Card uses gross LTV (no refund subtraction). | Material for high-refund customers | **Cross-connector reconciliation (when these connectors are connected for this merchant):** | Pair | Expected relationship | What divergence tells you | | -------------------------------------------------------------- | ------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | [`klaviyo.list_segments`](/nerve-centre/klaviyo/list_segments) | Klaviyo's RFM segments should overlap with this card's LTV cohorts | Material divergence indicates Klaviyo's segmentation criteria don't reflect dollar-weighted reality. | | ESP top-spender list | Should match top-decile within sync lag | Klaviyo top-spender lists are usually current; this card uses 90-day; diff = customers outside the 90D window. | ***
Same-metric documentation cross-reference (for agencies running multiple platforms) * [`shopify.customer_segments`](/nerve-centre/kpi-cards/shopify/customer-spend-segments) * [`bigcommerce.customer_segments`](/nerve-centre/kpi-cards/bigcommerce/customer-spend-segments)
## Known limitations / merchant FAQs **Why does the card use LTV ranking rather than the Adobe Commerce Customer Segments feature?** Adobe Commerce's native Customer Segments is rule-driven (predicate logic for marketing automation: "customers in California with abandoned carts in last 7 days"). It's optimised for marketing rules, not analytical decomposition. This card complements rather than replaces; use the native feature for marketing-rule targeting and this card for cohort-revenue analysis. **Adobe Commerce vs Magento Open Source: any difference?** Open Source lacks the Customer Segments rule engine but has the same underlying customer/order tables. The card runs identically. Open Source merchants who want rule-based marketing typically install a third-party module or use ESP segmentation (Klaviyo, Mailchimp). **Why are top-decile thresholds different across stores?** The card computes deciles on the merchant's own customer base. A high-AOV B2B distributor's top decile threshold may be $20,000 of trailing 90-day revenue; a DTC fashion store's may be $400. Each store's thresholds reflect its own business shape. **Are B2B Companies really worth aggregating to one customer?** Depends on the question. For "where is revenue concentrated", yes (one Company is one decision-making unit). For "how many people log into our portal", no (each buyer-email is a user). Configure the manifest aggregation to match the question. **Why include `pending_payment` orders in LTV?** B2B net-30 pipelines depend on `pending_payment`-state orders. Excluding them would understate B2B LTV by a third or more on net-30-heavy stores. **My multi-store Adobe Commerce, can I see per-region cohorts?** Yes, configure per-Store-View variants. Useful when consumer cohort is regionally heterogeneous (UK is mostly mid-LTV, US has more long-tail bottom-decile). **Why is the bottom decile so big (30% of customers, 7% of revenue)?** Common pattern. New customer acquisition floods the bottom; gift buyers, one-time-purpose shoppers, and recently-acquired-not-yet-repeat customers all pile into the lowest decile. This is normal and useful as the "convert to repeat" pool, but generally not where retention investment pays back. **A specific B2B Company isn't appearing in top-10% even though they spent a lot, why?** Most likely cause: the 90-day window doesn't include their order. B2B accounts on quarterly cadence may have placed their last \$50k PO 100 days ago, putting them outside the window. Consider a longer window (180D, 365D) for B2B-LTV ranking specifically. **The card and ESP segmentation disagree on top spenders, who's right?** Both, for different definitions. Card uses 90-day Adobe order data; ESP often uses a longer window plus marketing-engagement signals. Align the windows before comparing. *** ### Tracked live in Vortex IQ Nerve Centre *Customer Spend Segments* 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.