Jul 7, 2026
5 min Read
Table of Content
Zilo created a new customer promise.
Pidge created the operating layer that made it scalable.
Try-and-buy fashion delivery is not a normal last-mile operation. It requires speed, rider discipline, item-level control, customer doorstep coordination, payment closure, partial acceptance handling, reverse pickup, and live visibility.
For Zilo, the promise was clear: enable customers to try apparel at their doorstep within a 60-minute delivery window.
But making that promise work at scale required more than riders.
It required a delivery operating layer that could manage dedicated rider supply, regional deployment, order visibility, doorstep workflows, reverse movement, and operational control.
That is where Pidge helped Zilo scale 60-minute try-and-buy delivery across Mumbai regions.
Zilo’s delivery promise needed operational precision
Logistics Insight: A 60-minute try-and-buy model needs speed and control across both delivery and reverse logistics.
Zilo’s model depends on a high-touch customer experience.
The customer does not simply receive an order.
The customer tries the product, accepts selected items, rejects others, and completes payment only for what is accepted.
This means the delivery workflow includes:
Fast order movement
Rider assignment
Store pickup
Customer doorstep coordination
Product trial support
Partial acceptance handling
Rejected item return
COD or payment closure
Proof and status updates
Reverse logistics visibility
This makes the operating model more complex than standard ecommerce delivery.
Zilo needed a partner that could support both speed and workflow discipline.
The challenge was not only delivery speed
Logistics Insight: In try-and-buy fashion, speed matters only when the full doorstep workflow is completed correctly.
A 60-minute delivery promise cannot be measured only by rider movement.
The order must be picked, packed, assigned, delivered, tried, accepted or rejected, closed financially, and returned where required.
If the rider reaches the customer but the accepted and rejected items are not captured properly, the operation is incomplete.
If the rejected item return is unclear, inventory visibility suffers.
If payment does not match accepted items, reconciliation becomes difficult.
If the failed attempt is not genuine or properly recorded, customer experience and operational trust weaken.
For Zilo, scaling delivery meant building control across the full try-and-buy journey.
Pidge supported Zilo with a PDR-led operating model
Logistics Insight: Dedicated rider deployment helps high-touch delivery models maintain better control over speed, reliability, and workflow discipline.
Zilo’s 60-minute try-and-buy model needed a delivery layer that could operate with consistency.
Pidge supported this through a PDR-led model.
This helped Zilo create more dependable execution across active regions.
The operating model supported:
Dedicated rider deployment
Store-level pickup coordination
Rider assignment
Order movement visibility
Customer doorstep workflow
Partial acceptance support
Reverse pickup handling
COD and payment workflow support
Operational monitoring
Regional scale-up
For a specialized fashion delivery model, this kind of dedicated operating control becomes important.
It helps reduce dependency on fragmented delivery coordination.
Zilo scaled from 70 OPD to 600–700 OPD
Logistics Insight: Try-and-buy delivery becomes scalable when the operating layer can support growth without losing workflow control.
Zilo’s delivery scale grew from around 70 orders per day to 600–700 orders per day.
This growth was not only about adding delivery capacity.
It required a system that could support more orders, more riders, more regions, more customer interactions, and more reverse movement.
As order volume increased, the operation needed better visibility into:
Active orders
Rider assignment
Pickup status
Delivery progress
Partial acceptance cases
Rejected item movement
Failed attempts
COD and payment closure
Regional performance
Exception cases
Scaling try-and-buy delivery requires every part of the operation to remain visible and accountable.
Zilo reached a peak of 731 orders in a day
Logistics Insight: Peak-day performance depends on rider readiness, allocation control, monitoring, and exception response.
Peak days create pressure on delivery operations.
For Zilo, peak execution required the network to handle high order movement without losing control over the try-and-buy workflow.
During peak volume, delivery teams need to manage:
Rider availability
Pickup readiness
Allocation speed
Delivery timelines
Customer coordination
Doorstep trial time
Reverse item handling
Payment closure
Failed attempt tracking
Operational escalations
Zilo reached a peak of 731 orders in a day.
This shows the importance of a delivery operating layer that can support both volume and workflow complexity.
Zilo completed 17,134 monthly orders in February
Logistics Insight: Monthly scale shows whether the delivery model is repeatable, not just successful on one peak day.
A peak day can show capacity.
Monthly order volume shows operating consistency.
Zilo completed 17,134 orders in February.
For a try-and-buy apparel model, this level of execution needs repeatable daily control across riders, stores, customers, payments, accepted items, rejected items, and reverse movement.
The challenge is not only completing orders.
The challenge is completing them with the right workflow.
That is why structured delivery visibility becomes important for brands like Zilo.
Regional expansion needed city-level execution control
Logistics Insight: Specialized delivery models become harder to manage when they expand across multiple city regions.
Zilo’s delivery operation expanded across key Mumbai regions including Malad, Ghatkopar, Lower Parel, Thane, and Vashi.
Each region has different demand patterns, rider availability, traffic movement, store coordination requirements, and customer behaviour.
Regional scale creates challenges such as:
Rider deployment by region
Store pickup coordination
Route movement
Customer availability
Failed attempt management
Reverse item return
SLA consistency
Region-wise performance visibility
Operational escalation
Pidge helped support regional execution through managed operations, dedicated resources, and platform visibility.
This helped Zilo move from a limited operating base to a broader Mumbai delivery footprint.
Partial acceptance needed item-level discipline
Logistics Insight: Try-and-buy delivery needs item-level clarity because the customer may accept only part of the order.
In Zilo’s model, customers may not keep every item.
They may accept some products and reject others.
This makes partial acceptance a critical workflow.
The delivery system needs to capture:
Items sent to customer
Items accepted by customer
Items rejected by customer
Payment linked to accepted items
Rejected items collected by rider
Return movement status
Final closure
Without item-level discipline, the business may face inventory mismatch, payment confusion, customer disputes, and operational gaps.
Pidge helped support the delivery workflow where accepted and rejected items needed clearer operational handling.
Reverse logistics was part of the core delivery journey
Logistics Insight: For try-and-buy brands, reverse logistics is not an after-sales process. It is part of the same delivery interaction.
In a normal ecommerce delivery, reverse logistics may happen later through a separate return request.
In try-and-buy, reverse logistics begins at the customer doorstep.
When the customer rejects an item, the rider must carry it back through the operational flow.
This creates a connected forward and reverse journey.
Zilo needed visibility across:
Forward order delivery
Customer trial
Accepted products
Rejected products
Reverse pickup
Return movement
Payment closure
Final order status
Pidge helped Zilo manage this specialized delivery flow with better control than manual coordination alone could provide.
Rider productivity became central to scale
Logistics Insight: High-touch delivery models need riders who are not only available, but productive, visible, and workflow-compliant.
Zilo’s delivery model required riders to follow a more detailed workflow than standard handover delivery.
Riders needed to manage:
Timely pickup
Customer doorstep coordination
Trial-time handling
Accepted and rejected item updates
COD or payment-related closure where relevant
Reverse item handling
Failed attempt reporting
Status updates
As Zilo scaled, rider productivity became important.
The operation needed to understand which riders were active, where orders were moving, where delays were happening, and which regions needed better deployment.
Pidge helped create stronger rider and operational visibility for this scale-up.
Tracking and monitoring helped protect the customer promise
Logistics Insight: A 60-minute promise needs live visibility because teams must identify risks before the customer experience breaks.
When the delivery promise is time-sensitive, teams cannot wait for delayed updates.
They need live visibility into:
Assigned riders
Pickup status
Delivery movement
Active orders
Delayed orders
Failed attempts
Regional performance
Customer doorstep status
Reverse movement
Exception cases
Pidge TRACE helps improve rider and fleet visibility.
WatchTower helps operations teams monitor delivery movement and operational risks.
For a brand like Zilo, this visibility helped support better control across the delivery journey.
Exception handling was critical for reliability
Logistics Insight: Try-and-buy delivery exceptions affect customer experience, inventory, payment, and reverse logistics at the same time.
Zilo’s model could face exceptions such as:
Rider delay
Pickup delay
Customer unavailable
Trial not completed
Partial acceptance mismatch
Rejected item handling issue
Payment mismatch
Failed attempt
Reverse pickup gap
Regional capacity issue
These exceptions need quick detection and structured resolution.
Pidge SmartShape helps automate delivery exception workflows by triggering corrective actions when disruptions occur.
This helps reduce manual firefighting and supports more reliable execution.
How Pidge helped Zilo scale 60-minute try-and-buy delivery
Logistics Insight: Pidge helped Zilo scale by combining dedicated rider deployment, platform visibility, operational monitoring, reverse workflow support, and managed delivery execution.
Pidge supported Zilo through a connected logistics operating layer.
It helped with:
PDR-led dedicated rider deployment
Regional delivery execution
Rider and fleet visibility
Order tracking
Store pickup coordination
Doorstep workflow support
Partial acceptance handling
Reverse pickup movement
Exception handling
Operational monitoring
COD and payout-related visibility where relevant
Regional scale-up across Mumbai
Pidge did not just help Zilo deliver faster.
It helped Zilo make 60-minute try-and-buy fashion operationally scalable.
What try-and-buy brands should track
Logistics Insight: Try-and-buy brands need to track both delivery performance and reverse workflow performance to scale reliably.
Important metrics include:
Orders per day
Monthly order volume
Peak-day orders
Region-wise order volume
Rider availability
Rider productivity
Pickup delay
Delivery delay
SLA adherence
Partial acceptance rate
Rejected item count
Reverse pickup completion
Failed attempt rate
Customer unavailable cases
Payment mismatch cases
Exception count
Exception resolution time
Region-wise performance
Rider-wise performance
These metrics help try-and-buy brands understand whether the operating model is ready to scale across more regions and cities.
What try-and-buy brands should track
Logistics Insight: Try-and-buy brands need to track both delivery performance and reverse workflow performance to scale reliably.
Important metrics include:
Orders per day
Monthly order volume
Peak-day orders
Region-wise order volume
Rider availability
Rider productivity
Pickup delay
Delivery delay
SLA adherence
Partial acceptance rate
Rejected item count
Reverse pickup completion
Failed attempt rate
Customer unavailable cases
Payment mismatch cases
Exception count
Exception resolution time
Region-wise performance
Rider-wise performance
These metrics help try-and-buy brands understand whether the operating model is ready to scale across more regions and cities.
Final takeaway
Logistics Insight: Zilo scaled 60-minute try-and-buy delivery by pairing a strong customer promise with a structured logistics operating layer.
Try-and-buy fashion is difficult to scale because the delivery journey does not end with handover.
It includes trial, partial acceptance, payment closure, rejected item return, reverse movement, and customer experience control.
Zilo’s growth from 70 OPD to 600–700 OPD, with a peak of 731 orders in a day and 17,134 monthly orders in February, required more than delivery supply.
It required operational orchestration.
Pidge helped Zilo create that operating layer across riders, regions, workflows, visibility, exceptions, and reverse logistics.
That is how Zilo scaled 60-minute try-and-buy delivery with Pidge.
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