> ## 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. # Carrier OTD by Sales Channel, DPDLocal > Joins shipment timing to commerce-sibling orders by order_ref - which sales channel is suffering most from carrier delays. Key Metric. How to read it, why it mat... **Metrics type:** [Cross-Platform Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Shipping & Courier](/nerve-centre/connectors#connectors-by-type) > Joins shipment timing to commerce-sibling orders by order\_ref - which sales channel is suffering most from carrier delays. Key Metric. ## At a glance > Cross-platform OTD split: takes every DPDLocal consignment, joins it back to the originating commerce order (Shopify / BigCommerce / Adobe / SFCC sibling) by `order_ref`, and groups by the originating sales channel. Surfaces "which channel's customers are getting the worst delivery experience" so marketing can match acquisition spend to fulfilment reality. | | | | ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `OTD% by commerce_sibling.order.channel`: the headline OTD percentage broken down per sales channel for the same period. | | **Channels typically surfaced** | Online Store (DTC), Amazon-via-3PL-orders, eBay, Faire, B2B trade portal, POS, marketplace integrations (TikTok Shop, Etsy). | | **Join logic** | `dpdlocal.shipment.actual_delivery JOIN commerce_sibling.order ON order_ref GROUP BY commerce_sibling.order.channel`. The `order_ref` field on the consignment is the merchant-supplied order reference set at label-print time. | | **Coverage caveats** | Consignments with missing or non-matching `order_ref` are excluded from the join. Coverage is typically 96 to 99% on Shopify and BigCommerce sibling stacks; lower on B2B portals where order references can be free-text. | | **`only_when: has_commerce_sibling`** | Card only appears in the Nerve Centre when at least one ecommerce connector is also live in the workspace. | | **Time window** | `30D` | | **Alert trigger** | `any channel <90%`. Channel-level threshold to surface a problem one channel that the aggregate OTD hides. | | **Roles** | owner, operations, marketing | ## Calculation Calculated automatically from your DPDLocal 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 UK fashion DTC merchant on Shopify with active TikTok Shop and a small B2B trade portal. \~10,000 DPDLocal parcels per month total. Reading taken 12 Mar 26 for the trailing 30 days. | Channel | Volume | On-time | OTD | | ------------------------------------------------------------------------------------------ | --------- | --------- | --------- | | Online Store (Shopify DTC) | 7,200 | 6,948 | 96.5% | | TikTok Shop | 1,400 | 1,221 | 87.2% | | B2B trade portal | 320 | 314 | 98.1% | | Faire wholesale orders | 180 | 178 | 98.9% | | **Aggregate** (matches [OTD Rate](/nerve-centre/kpi-cards/dpdlocal/on-time-delivery-rate)) | **9,100** | **8,661** | **95.2%** | The card flags **TikTok Shop at 87.2%, below the 90% channel threshold**. Three things to notice: 1. **Aggregate OTD looks healthy at 95.2% but TikTok is in trouble.** The xc card surfaces what the aggregate hides. TikTok-Shop customers are getting a materially worse delivery experience, which depresses repeat-purchase rates from a high-acquisition-cost channel. 2. **Why TikTok specifically?** Three usual reasons. (a) **Address-data quality is lower** on TikTok orders (in-app checkout, no address validation); ADDRESS\_INVALID exception rate is 3 times the DTC rate. (b) **Same-day-promise marketing copy** sets customers up to be disappointed by 1-day NextDay delivery. (c) **TikTok's order export** sometimes truncates or mangles the `order_ref`, causing some failures to fall out of the join (coverage gap, not real OTD drop). 3. **Action.** Add address-validation widget at TikTok Shop checkout (e.g. PostcodeAnywhere); align checkout copy with actual NextDay reality; cross-check `order_ref` integrity in the TikTok-to-Shopify integration. ## Sibling cards merchants should reference together | Card | Why pair | | ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------- | | [On-Time Delivery Rate](/nerve-centre/kpi-cards/dpdlocal/on-time-delivery-rate) | Aggregate baseline; this xc card splits it. | | [Late-Delivery Revenue at Risk](/nerve-centre/kpi-cards/dpdlocal/late-delivery-revenue-at-risk) | Quantifies the £ exposure on the worst channel. | | [3PL vs Carrier Delay Attribution](/nerve-centre/kpi-cards/dpdlocal/3pl-vs-carrier-delay-attribution) | Diagnoses warehouse vs carrier root cause. | | Cross-connector: per-channel acquisition spend (Google Ads, Facebook, TikTok Ads) | Pair to compute "acquisition cost per healthy delivery" per channel. | ## Reconciling against the vendor's own dashboard **Where to look in DPDLocal's dashboard:** DPDLocal's own portal does not split OTD by sales channel; the channel field exists only in the commerce-sibling order data. This card is a Vortex IQ-derived view, no native DPDLocal counterpart to reconcile against. The closest like-for-like is filtering the MyDPD tracking export by `order_ref` prefix per channel and computing OTD manually. **Why our split may differ from a manual calculation:** | Reason | Direction | Why | | ---------------------------------------- | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Coverage of `order_ref`** | Either | If 2 to 4% of consignments fail to join the commerce sibling (truncated or missing reference), they sit in an "Unknown channel" bucket. Channel splits exclude them. | | **Channel taxonomy on commerce sibling** | Either | Shopify's `source_name` field is sometimes free-text; the card does light normalisation (e.g. `web` and `online_store` collapse to "Online Store") but rare values may surface as their own group. | **Cross-connector reconciliation:** | Card | Expected relationship | | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Per-channel order count on commerce sibling | Joining-loss = (commerce sibling order count) - (sum of channel rows here). 96 to 99% is healthy on Shopify; below 95% indicates `order_ref` integrity issue. | ## Known limitations / merchant FAQs **My TikTok / marketplace channel always shows worse OTD. Is the carrier punishing them?** No, the carrier doesn't know which channel an order came from. Three usual real reasons. (1) **Lower address-data quality** in marketplace-app checkouts that don't run the storefront's address-validation widget. (2) **Customer expectation mismatch**: marketplace-app checkout copy often promises faster delivery than the merchant's actual SLA. (3) **`order_ref` join coverage gap**: some marketplace integrations truncate or mangle the reference, so a fraction of consignments fall into "Unknown channel" rather than the actual channel. **Why is the aggregate of channels different from the [OTD Rate](/nerve-centre/kpi-cards/dpdlocal/on-time-delivery-rate) card?** Two reasons. (1) **Coverage gap.** Consignments that don't join to a commerce-sibling order (missing `order_ref`) are excluded from the channel split but included in the aggregate OTD. (2) **Window edge effects** during sync, but these reconcile within 24 hours. **B2B trade portal shows perfect OTD. Believable?** Yes, usually. B2B accounts have clean address data (warehouse-to-warehouse delivery), generous delivery windows (`expectedDeliveryDate` set conservatively), and cooperative recipients (no recipient-not-available friction). B2B OTD typically runs 97 to 99% even when DTC is below. **Can I drill into the worst channel from this card?** Yes, click the channel bar to filter the [OTD by Route](/nerve-centre/kpi-cards/dpdlocal/otd-by-route) and [Exception Reasons](/nerve-centre/kpi-cards/dpdlocal/exception-reasons) cards to that channel only. The drill-through pre-filters by the same `order_ref ∈ channel` set. **My channel taxonomy doesn't match what the card shows. Why?** The card uses the commerce-sibling's native channel field (e.g. Shopify `source_name`, BigCommerce `channel_id`). Free-text or custom channel labels surface as-is. Set up channel-name normalisation in the connector field-mapper if you want tidier display labels. **Should I pause spend on the worst channel?** Not before fixing the root cause. The card surfaces the symptom, not the diagnosis. If the channel is structurally bad (e.g. address-data quality), pausing spend is treating the wound, not curing the disease. Fix the root cause first, then re-evaluate. *** ### Tracked live in Vortex IQ Nerve Centre *Carrier OTD by Sales Channel* is one of hundreds of KPI pulses Vortex IQ tracks across DPDLocal 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.