> ## 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. # Channel Mix (Amazon vs DTC), Amazon (Selling Partner) > Channel Mix (Amazon vs DTC) for Amazon (Selling Partner) stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Cross-Platform Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Marketplace](/nerve-centre/connectors#connectors-by-type) ## At a glance > Amazon revenue as a percentage of total commerce revenue (Amazon + connected DTC connectors), trailing 90 days. The headline answers "what share of the brand depends on Amazon?" with a concentration alert at 70%, the threshold above which Amazon-side disruption (Buy Box loss, account-health flags, suppression cascade) becomes existential. | | | | ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `amazon.total_revenue ÷ (amazon.total_revenue + sum_of_DTC_total_revenue)`, trailing 90 days, in unified settlement currency. DTC connectors include Shopify, BigCommerce, and Adobe Commerce when connected. | | **API endpoint + report** | SP-API Orders API for Amazon side (gross `OrderTotal.Amount`), plus the connected DTC connectors' Total Revenue for the denominator. Computed in our Vortex IQ Nerve Centre cross-platform index. | | **ASIN vs account scope** | **Account-level only.** This is a strategic concentration KPI, not a per-ASIN view. | | **Buy Box impact** | Indirect but profound. The higher the Amazon dependency, the more Buy Box state matters to brand survival. A 90% Amazon-dependent brand losing Buy Box across 5 ASINs faces revenue collapse; a 30% Amazon-dependent brand absorbs the same event with limited operational pain. | | **FBA vs FBM** | Both contribute to the Amazon side; not separated in the headline. Drill down to [Order Count](/nerve-centre/kpi-cards/amazon/orders) split by `FulfillmentChannel` for the FBA/FBM mix. | | **Fees / commission** | **Gross.** The card uses Amazon's pre-fee revenue against DTC's pre-payment-processor revenue for like-for-like comparison. Net-of-fees comparison would shift the headline by 3 to 5 percentage points (Amazon's fees are higher than DTC payment processor fees), but the strategic interpretation is the same. | | **Refunds** | **NOT deducted on either side.** Both sides are gross to keep the comparison clean. | | **Cancellations** | Included on both sides where indexed. | | **Currency** | **Settlement currency**, with FX conversion applied to all DTC-side currencies if multi-currency selling is enabled. | | **Marketplace dynamics** | Single-marketplace dependency (e.g. amazon.com only) is more concentrated than the headline suggests; even within Amazon, a single marketplace going `At Risk` collapses revenue. Drill into per-marketplace mix when total Amazon dependency is high. | | **Return-window vs refund-window** | Not applicable. | | **Time window** | `90D` (trailing, smooth enough to ignore week-to-week noise; long enough to reflect strategic mix shifts). | | **Alert trigger** | `Amazon dependency >70%`, the threshold above which Amazon disruption becomes a brand-survival risk. | | **Roles** | owner, finance. | ## Calculation Calculated automatically from your Amazon (Selling Partner) data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading. ## Worked example A US Amazon-first DTC supplements brand. Trailing 90 days, 02 Feb 26 to 02 May 26. Connectors: Amazon SP-API + Shopify. | Channel | Trailing 90D revenue (gross) | Share | Trend vs prior 90D | | ------------------- | ---------------------------- | -------- | ------------------ | | Amazon (amazon.com) | \$2,140,000 | 86.0% | +12% | | Shopify (DTC) | \$348,000 | 14.0% | +3% | | **Total commerce** | **\$2,488,000** | **100%** | +10.6% | The card reads **86% Amazon dependency**, well above the 70% threshold; the alert is firing. Five things to notice that are specific to Amazon: 1. **Buy Box loss = sales loss, instantly, and at 86% dependency it's an existential threat.** A simulation: if 5 of the brand's top ASINs lose Buy Box for 30 days (a realistic worst case during a reseller war or post-OOS recovery), the brand loses roughly 35% of Amazon revenue, equating to a 30% drop in TOTAL commerce revenue across that window. The DTC channel can't cushion the fall; it's too small. The brand needs to either grow DTC to 30%+ or accept the concentration risk and run a tighter Amazon operational playbook. 2. **Commission erodes 12 to 15% of headline, and the brand has limited leverage at 86% dependency.** Amazon-first brands can't push back on Amazon fee changes; they have nowhere to go. Brands at 30 to 50% Amazon dependency can afford to deprioritise Amazon when fees rise; brands at 86% can't. This is the strategic cost of concentration that the card surfaces. 3. **Amazon-first buyers don't migrate to your DTC site.** The brand has tried twice in the last year to push Shopify campaigns to Amazon-buyer email lists (scraped from "Request a Review" replies). Click-through both times: 2 to 3%; conversion 0.2 to 0.4%. The buyer cohorts are separate. Growing DTC requires acquiring DTC-native buyers (Meta ads, influencer, organic SEO), not converting Amazon shoppers. This is a structural insight that should inform the diversification plan. 4. **Out-of-stock punishes you for weeks, and concentrated dependency amplifies the cost.** A two-week FBA stockout on a top SKU costs an Amazon-only brand the entire SKU's revenue plus 14 to 28 days of organic-rank recovery. At 86% dependency, that single SKU's loss can move the brand's monthly P\&L by 5 to 10%. Lower-dependency brands absorb the same event more comfortably. 5. **Amazon doesn't share customer data, which limits diversification options.** Amazon does not give sellers customer email addresses or contact info. A brand at 86% dependency has 86% of its customers in a list it cannot directly market to. Diversification efforts have to start from a near-zero CRM, which is why DTC growth from a high-Amazon baseline is structurally slow. Plan for 18 to 36 months to materially shift the mix; faster shifts usually mean acquisition-channel spending that erodes margin. ## Sibling cards merchants should reference together This is the **strategic concentration KPI**. Pair with these to size diversification options: | Card | Why pair it with Channel Mix (Amazon vs DTC) | | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | [Total Revenue](/nerve-centre/kpi-cards/amazon/total-revenue) | The Amazon side of the ratio. | | [Net Revenue](/nerve-centre/kpi-cards/amazon/net-revenue) | Net-of-fees view; some brands prefer this for like-for-like with DTC. | | [Account Health Status](/nerve-centre/kpi-cards/amazon/account-health-status) | The single biggest risk to a high-dependency brand. Watch alongside this card daily. | | [Buy Box Trend](/nerve-centre/kpi-cards/amazon/buy-box-trend) | Buy Box loss disproportionately damages high-dependency brands. | | [Revenue at Risk (live)](/nerve-centre/kpi-cards/amazon/revenue-at-risk) | Operational risk size. At >70% dependency, a £10k/month at-risk reading is a \~3% hit to total commerce. | | [Shopify Total Revenue](/nerve-centre/kpi-cards/shopify/total-revenue) | The DTC denominator side. Growing this is the primary diversification lever. | | [BigCommerce Total Revenue](/nerve-centre/kpi-cards/bigcommerce/total-revenue) | Same role for BC stores. | | [Amazon Ads ACOS](/nerve-centre/amazon_ads/aads_acos) | High-dependency brands typically over-spend on Amazon Ads to defend share; watch ACOS for diminishing returns. | ## Reconciling against the vendor's own dashboard **Where to look in Amazon Seller Central:** Amazon does not publish a "share of total commerce revenue" view (Amazon doesn't know about your DTC channels). Closest reconcilable views: 1. [**Reports → Business Reports → Sales and Traffic by Date**](https://sellercentral.amazon.com/business-reports/) for the Amazon side of the ratio. Use the *Ordered Product Sales* column over the same 90-day window. 2. [**Reports → Payments → All Statements**](https://sellercentral.amazon.com/gp/payments-account/settlement-summary.html) for settled (post-fee) Amazon revenue, useful when reconciling against a DTC connector's net-of-payment-processor revenue for like-for-like. 3. Cross-reference your DTC platform's revenue dashboard for the same 90-day window to compute the ratio manually. **Why our number may legitimately differ from anything you'd build manually:** | Reason | Direction | Why | | ---------------------------------- | --------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Time zone** | Boundary days only | Amazon Business Reports run in **Pacific Time**; DTC connectors run in shop timezone; this card uses UTC. The 90-day window is long enough that boundary-day effects average out, but a manual rebuild can boundary-shift by 7 to 8 hours. | | **Settlement-period lag** | Either | If you reconcile against Amazon's *Payments* / *Settlement* view (post-fee) and your DTC against gross revenue, you'll get a different ratio than this card (which compares gross-to-gross). Pick a consistent basis for the comparison. | | **API rate limits** | Ours can lag during high-volume periods | Both SP-API and DTC connectors are throttled; the trailing 90D window is robust to small lags but bursty days near the window boundary can shift the ratio by ±1 percentage point briefly. | | **Reports API generation latency** | Up to 4 hours | Velocity inputs use the most recent successful pull; today's ratio may include up to 4 hours of stale data. | | **DTC connector coverage** | Could differ | The card uses every connected DTC connector. If you have multiple Shopify stores or a BigCommerce + Shopify combo, all connected stores feed the denominator. Manually building the ratio on one DTC store will produce a higher Amazon dependency. | **Cross-connector reconciliation:** This card is **inherently cross-connector**. It does not exist without both an Amazon connector AND at least one DTC connector. The reconciliation IS the metric. | Card | Expected relationship | What causes legitimate divergence | | ----------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [`shopify.total_revenue`](/nerve-centre/kpi-cards/shopify/total-revenue) | Shopify Total Revenue feeds the DTC denominator. | Different stores in different geographies aggregate together; if you want region-specific dependency, configure per-region pairing in *Nerve Centre → Channel Mix Settings*. | | [`bigcommerce.total_revenue`](/nerve-centre/kpi-cards/bigcommerce/total-revenue) | As above. | Same dynamic. | | [`amazon_ads.aads_acos`](/nerve-centre/amazon_ads/aads_acos) | Indirect: high-dependency brands typically run higher Amazon Ads spend defending share, raising ACOS. | Cross-check on a 90D correlation between Amazon dependency and ACOS. | | [`stripe.stripe_total_revenue`](/nerve-centre/kpi-cards/stripe/total-charge-volume) | **Zero overlap by design.** | Amazon orders settle through Amazon's payment rails, never through Stripe. Stripe sees only DTC orders. So `stripe ≤ DTC denominator`. | ***
Marketplace-peer documentation cross-reference (for sellers on multiple marketplaces) The same dependency calculation extends naturally to multi-marketplace brands: a brand could compute eBay share, Etsy share, and Amazon share separately, against the same DTC denominator. The 70% concentration threshold is a useful generic, but each marketplace has its own risk profile (Amazon's account-suspension risk is highest among the major marketplaces). * [`ebay.ebay_share_of_channel_revenue`](/nerve-centre/ebay/ebay_share_of_channel_revenue) (when connected)
## Known limitations / merchant FAQs **My dependency is 86% and the alert is firing. What do I do?** You can't move the dependency below 70% in a quarter; this is an 18 to 36 month strategic effort. Start with: (1) acknowledge the risk operationally (run a tighter Amazon ops playbook, monitor [Account Health Status](/nerve-centre/kpi-cards/amazon/account-health-status) daily, eliminate avoidable suppressions and Buy Box losses), (2) build DTC-native acquisition (Meta + Google Ads aimed at first-time DTC buyers, not Amazon-buyer remarketing), (3) consider eBay or another marketplace as a near-term diversification (faster than DTC growth, similar margin profile), (4) set a dependency target (e.g. 60% in 24 months) and track the trajectory monthly on this card. Don't try to "stop selling on Amazon", that's almost always the wrong answer; the goal is to grow other channels, not shrink Amazon. **Should I reconcile gross or net of fees?** The card uses gross on both sides. Net comparison is a different lens: Amazon's \~13% blended fee is higher than DTC's \~3% payment processor fee, so a net-net comparison shifts Amazon's effective share down by 5 to 10 percentage points. Both views are valid; gross is the default because it answers "how much of my customer-facing brand depends on Amazon", which is the strategic question. Net is the right view for margin and operating leverage discussions. **Why doesn't Stripe revenue feed the DTC side?** Because Stripe is a payment processor, not a sales channel. The DTC sales channels (Shopify, BigCommerce, Adobe) are the source of truth; Stripe is downstream of them. Adding Stripe to the DTC denominator would double-count the same revenue. Use Stripe Total Revenue for processor reconciliation only. **ACOS observations, why does the card mention them?** Because high-dependency brands typically over-invest in Amazon Ads to defend market share. They run ACOS at 25 to 35% (where 15 to 20% is healthy), spending more per dollar of revenue to maintain volume in a competitive marketplace. This pattern is so consistent that an unusually low ACOS at high dependency often signals that the brand has a strong organic position, while an unusually high ACOS at high dependency signals competitive pressure. Cross-check on [Amazon Ads ACOS](/nerve-centre/amazon_ads/aads_acos). **FBA vs FBM, do they affect the dependency calculation?** No. The card uses gross Amazon revenue regardless of fulfilment channel. But high-dependency brands tend to be FBA-heavy (FBA fees are higher but Buy Box-protective), which compounds the strategic risk: not only are you dependent on Amazon for revenue, you're also dependent on Amazon for warehousing and fulfilment. **Multi-marketplace, does the card aggregate amazon.com + amazon.co.uk + amazon.de?** Yes, all connected Amazon marketplaces sum into the Amazon side. If you're 50% amazon.com and 30% amazon.de, your Amazon side is 80% of total commerce, with 50/30 mix internally. Drill down to per-marketplace if the diversification strategy needs to address marketplace-specific dependency (e.g. amazon.com is a different account from amazon.de; an account suspension hits one but not the other). **Settlement timing, does this affect the ratio?** On the headline, no, the 90-day window is long enough to absorb settlement timing differences. But on a "today" or "yesterday" view, Amazon's settlement lag can make recent-period numbers look slightly low relative to DTC; the ratio understates true Amazon dependency by 1 to 2 percentage points for the most recent 14 days. Use the trailing 90D headline, not "today". **Return-window confusion, do return-window differences between Amazon and DTC affect the ratio?** Slightly. Amazon's standard 30-day return window means more refunds happen within the 90-day measurement period; DTC return windows vary (60 days, 90 days, lifetime depending on policy). On a gross basis (this card's default), refunds don't enter the calculation. On a net basis, Amazon's higher in-window refund rate slightly reduces Amazon's share of net revenue. The strategic interpretation is unchanged. **Why isn't the Shopify-Amazon channel app data the source of truth?** Because the channel app is a publishing tool that pushes Shopify product data to Amazon listings; it does not measure or reconcile total commerce revenue. The SP-API gives us Amazon revenue; the Shopify Admin API gives us Shopify revenue; the card joins them. Disable the channel app entirely and the card calculation is unaffected. If you do use it, channel-app-routed Amazon orders may also appear in Shopify Total Revenue (double-counting on the DTC side), in which case filter `tags = "amazon"` out of Shopify revenue before reconciling. See the FAQ on [Total Revenue](/nerve-centre/kpi-cards/amazon/total-revenue) for the full channel-app caveat. **Why does today's number rarely change?** Because the trailing 90-day window dilutes recent days heavily. A single big day shifts the ratio by \<0.5 percentage points. The card is intentionally smooth to support strategic planning; for daily Amazon-side volatility use [Total Revenue](/nerve-centre/kpi-cards/amazon/total-revenue) directly. The ratio is meant to be checked monthly against the 70% threshold and quarterly against the diversification trajectory, not daily. *** ### Tracked live in Vortex IQ Nerve Centre *Channel Mix (Amazon vs DTC)* is one of hundreds of KPI pulses Vortex IQ tracks across Amazon (Selling Partner) 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.