Attempt Rate: How Many Tries Does It Take to Deliver?
- Average Indian attempt rate ≈ 2.3 tries per parcel – a key driver of RTO costs.
- Urban vs. Tier‑2/3 : Mumbai ≈ 2.0, Guwahati ≈ 2.7 – geography matters.
- EdgeOS + Dark Store Mesh + NDR Management can cut attempts by 15‑20 %, slashing RTO and boosting CSAT.
Introduction
India’s e‑commerce boom has turned every street into a delivery battleground. For every COD order that lands on a doorstep, there are multiple silent attempts behind the scenes. In Tier‑2 and Tier‑3 cities, a missed delivery can mean a whole day’s worth of extra cost, not to mention the dreaded RTO (Return to Origin). As merchants chase margins and consumers demand instant gratification, the number of tries it takes to deliver—the *attempt rate*—has become a KPI that can make or break logistics performance.
Let’s dig into the data, understand why this metric matters, and see how a tech‑driven stack—EdgeOS, Dark Store Mesh, and NDR Management—can help you win the race.
What Is Attempt Rate?
The attempt rate is the average number of delivery attempts made per parcel before the order is either successfully delivered or returned to the warehouse.
| Metric | Definition | Formula |
|---|---|---|
| Average Attempt Rate (AAR) | Total attempts ÷ Total parcels | AAR = ΣAttempts / ΣParcels |
| RTO Rate | Parcels returned to origin | RTO = Parcels Returned ÷ Total Parcels |
| Successful Delivery Rate (SDR) | Parcels delivered successfully | SDR = Parcels Delivered ÷ Total Parcels |
Why it matters:
- Each attempt incurs driver wages, fuel, and vehicle wear.
- Multiple attempts increase *delivery windows*, causing customer dissatisfaction.
- High attempt rates inflate RTO costs—often 3–4× the original shipment cost.
Why Attempt Rate Matters in India
| Factor | Impact on Attempt Rate |
|---|---|
| COD dominance | 70+% of orders rely on cash at delivery, increasing the likelihood of unavailability. |
| RTO culture | Consumers expect a return if they can’t receive the parcel, encouraging merchants to accept returns. |
| Urban density | In metros like Mumbai, traffic congestion often forces multiple stops. |
| Tier‑2/3 logistics | Sparse address verification and limited delivery hubs mean more attempts per parcel. |
| Festive rush | Diwali, Christmas, and local festivals spike order volumes, stressing delivery networks. |
Key insight: *Reducing the number of attempts directly cuts RTO, saves money, and improves CSAT.*
Statistical Breakdown: Delivery Attempts Across Indian Cities
| City | Total Parcels | Total Attempts | Average Attempt Rate | RTO Rate |
|---|---|---|---|---|
| Mumbai | 1,200,000 | 2,410,000 | 2.01 | 3.2 % |
| Bangalore | 950,000 | 1,870,000 | 1.97 | 2.8 % |
| Guwahati | 380,000 | 1,030,000 | 2.71 | 4.6 % |
| Lucknow | 420,000 | 1,190,000 | 2.83 | 5.0 % |
| *Average* | – | – | 2.28 | 4.0 % |
Observations:
- Tier‑2/3 cities consistently show higher average attempt rates due to logistics gaps.
- The RTO rate correlates strongly with the attempt rate—each extra attempt increases RTO probability by ~0.6 %.
Problem‑Solution Matrix: Reducing RTO via Attempt Rate Optimization
| Problem | Root Cause | Solution (Tech + Process) | Impact |
|---|---|---|---|
| Frequent failed first attempts | Poor address verification | EdgeOS real‑time geocoding & address validation | ↓ 1.2 attempts/parcel |
| Multiple attempts due to driver inefficiency | Inadequate route optimization | Dark Store Mesh local hubs + AI‑driven routing | ↓ 0.8 attempts/parcel |
| Late deliveries causing RTO | Inefficient last‑mile scheduling | NDR Management for real‑time driver status & customer alerts | ↓ 0.5 attempts/parcel |
| COD unavailability | Lack of real‑time payment confirmation | EdgeOS COD‑status sync with merchant APIs | ↓ 0.3 attempts/parcel |
EdgeOS: The Data Backbone for Real‑Time Attempt Tracking
EdgeOS aggregates delivery data from thousands of couriers (Delhivery, Shadowfax, Gati, Blue Dart) and feeds it into a unified analytics platform.
Key Features:
- Live Attempt Counter : Updates after every driver stop.
- Geofence Alerts : Detects when a driver is stuck in traffic or deviating from route.
- COD Sync : Immediate payment confirmation or failure flag.
Result: Merchants can see, in real time, which parcels are stuck and intervene—contacting the customer, re‑routing the driver, or scheduling a re‑delivery with minimal delay.
Dark Store Mesh: Localizing Deliveries to Cut Attempts
A *dark store* is a fulfillment center that serves only online orders. By deploying a mesh of dark stores across Tier‑2/3 cities, Edgistify reduces the distance between the warehouse and the customer.
Benefits:
- Shorter routes ⇒ fewer traffic delays.
- Higher delivery density ⇒ batch deliveries reduce per‑parcel cost.
- Local driver knowledge ⇒ better address resolution.
Case Study: In Guwahati, the addition of a dark store mesh in 2024 cut the average attempt rate from 2.71 to 2.35— a 13 % improvement.
NDR Management: Handling Non‑Delivery Requests Efficiently
Non‑Delivery Requests (NDRs) occur when a parcel cannot be delivered due to reasons like “no one at home” or “wrong address.”
NDR Management Module offers:
- Automated customer communication (SMS/WhatsApp) to reschedule.
- Dynamic re‑dispatch—if a driver is already en route, the parcel can be handed over to the next available driver.
- Return‑to‑Origin (RTO) optimization—automatic routing for parcels that must be returned, reducing extra mileage.
By integrating NDR Management with EdgeOS, merchants can reduce the *average attempts per parcel* by an additional 0.4, bringing the total down to ≈ 2.0 attempts.
Conclusion
In the high‑velocity world of Indian e‑commerce, the attempt rate is more than a number—it’s a lever for cost, speed, and customer delight. By marrying data‑driven insights (EdgeOS), localized fulfillment (Dark Store Mesh), and intelligent exception handling (NDR Management), merchants can slash delivery attempts, shrink RTO, and keep the ₹ in the pocket.
Future‑proofing logistics isn’t optional; it’s a necessity. Start measuring, start optimizing, and let the data guide you to a smoother, cheaper, and happier delivery experience.
FAQs (Voice‑Search Friendly)
- 1. What is the average attempt rate for e‑commerce deliveries in India?
– It averages around 2.3 attempts per parcel, varying by city and city tier.
- 2. How does a high attempt rate affect RTO costs?
– Each extra attempt adds fuel, driver wages, and can increase RTO by about 0.6 % per attempt.
- 3. Can dark stores reduce delivery attempts?
– Yes, by localizing fulfillment, dark stores cut route distance and traffic delays, lowering attempts.
- 4. What is EdgeOS and why is it useful?
– EdgeOS is a real‑time analytics platform that tracks delivery attempts, driver status, and COD payments, enabling proactive intervention.
- 5. How does NDR Management help with failed deliveries?
– It automates customer communication, dynamic re‑dispatch, and RTO routing to minimize unnecessary attempts.