Fulfilment & logistics

For each trigger below: what happened, where to go, how to read it, the decision you’re making.

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Which courier partner is costing us the most in failed deliveries?

Trigger: RTO rates climbing or delivery complaints rising. You need to know which courier — to renegotiate, reallocate, or escalate. Chart Builder: Fulfilment · Measures: rto_rate, delivery_rate, tat_breach_rate, avg_days_to_deliver · Dimension: courier_partner · Table sorted by rto_rate desc. A courier with high RTO and high TAT breach simultaneously → highest-risk partner. Every failure is an order lost plus reverse logistics cost. Add financial context: avg_shipment_cost alongside. Expensive and unreliable → strong candidate for reduced allocation. Decision: Reduce allocation, renegotiate terms, or escalate for performance review?

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Which cities or pincodes cause the most delivery failures?

Trigger: Overall RTO is high — is it concentrated in specific geographies (courier coverage issue) or spread evenly (payment method or product quality issue)? Chart Builder: Fulfilment · Measure: rto_rate · Dimension: drop_city · Breakdown: reason_for_first_failed_delivery · Pivot. The city × failure-reason matrix tells you whether failures are addressable (Customer Unavailable, Wrong Address — fixable with address validation, retry logic) or structural (courier doesn’t service the area — fixable only by changing allocation). Pincode granularity: Filter to drop_pincode for the highest-RTO cities. Specific pincodes often drive disproportionate failure shares. Decision: Address validation, switch courier for specific pincodes, or flag high-risk geos for COD?

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Are we being overbilled by courier partners?

Trigger: Logistics costs higher than expected, or WMS-declared vs. courier-invoiced weights are off. Quantify the gap and find responsible partners and zones. Chart Builder: Fulfilment · Measures: cost_discrepancy_rate, avg_weight_discrepancy, avg_shipment_cost · Dimension: courier_partner · Breakdown: zone · Table.

• cost_discrepancy_rate — % shipments with billing mismatch
• avg_weight_discrepancy — gap between WMS-declared and courier-billed weight

Couriers with high discrepancy rates in Metro and Zone A (where volumetric weight billing kicks in differently) → biggest source of unbilled cost. Decision: Which courier-zone combinations to flag for billing disputes, and do I need to recalibrate WMS weight declarations?

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How does COD delivery performance compare to prepaid?

Trigger: Considering tightening COD availability (specific pincodes, order values). Quantify the RTO penalty of COD vs. prepaid, broken down by courier. Chart Builder: Fulfilment · Measures: delivery_rate, rto_rate · Dimension: payment_method · Breakdown: courier_partner · Grouped Bar. COD has higher RTO by nature — buyers haven’t paid upfront. Interesting question: is the COD penalty courier-specific? If one courier has a dramatically higher COD RTO for the same zones, they’re not following up on failed COD deliveries. Decision: Restrict COD by pincode or order threshold, and stop assigning COD to specific couriers?

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What are the most common return and exchange reasons?

Trigger: High return volumes — is it sizing, quality, or misleading PDPs? The answer decides whether to fix the product, the listing, or fulfilment. Chart Builder: Fulfilment · Measure: returned_shipments · Dimension: return_reason · Bar sorted desc. Cross-reference with: Sales · Measure: percentage_orders_returned · Dimension: product_name, filtered to the high-return-reason categories. This connects the logistics reason (Clickpost) with the product dimension (Sales) — so you can see whether “Size issue” or “Not as described” concentrates in specific SKUs. Decision: Product issue (fix sizing, quality), listing issue (update PDP, images, size guide), or fulfilment issue (wrong items, packaging damage)?

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