Customer Care & Returns Management

An OMS improves customer experience by showing accurate inventory and delivery promises, routing orders to the best fulfilment location, and providing clear tracking and updates. It also detects issues early (like allocation problems or delays) so teams can fix them before customers feel the impact. This reduces backorders, improves on-time delivery, and increases trust, especially for omnichannel, subscription, and high-volume operations.

Infios OMS manages the full returns lifecycle, not just the inbound movement of stock. When a return is initiated, the OMS applies configurable rules to determine the right outcome, whether that's a refund, exchange, credit, or re-fulfilment, based on factors like return reason, product condition, customer segment, and channel. Returned inventory is assessed and routed appropriately, whether back to sellable stock, to quarantine, or to disposal, keeping inventory positions accurate throughout. For refusals and failed deliveries, the OMS can trigger automated workflows to rebook, redirect, or escalate based on predefined logic, reducing manual intervention and protecting service levels.

Most customers go live within 8–16 weeks thanks to pre-built adapters and accelerators.

Yes. Our modules integrate with any ERP, WMS or legacy OMS via our native Integration Platform-as-a-Service (iPaaS) through various avenues like EDI, API, SFTP. 

It analyzes SKU attributes, location costs, resale probability and fraud signals to automatically select the fastest, most cost-effective disposition.

Infios Order Management is SOC 2 Type II certified and fully compliant with GDPR and CCPA requirements.

Customer care and returns management refers to how businesses handle post-purchase interactions, including inquiries, returns, refunds and exchanges. It connects order information, inventory and customer communication in a single source of truth. When deployed, it reduces friction for customers, improves operational efficiency and drives brand loyalty. The result is faster resolution times and stronger customer retention.

Automation reduces returns handling time by removing the manual steps that slow the process down: policy eligibility checks, fraud screening, label generation, carrier selection and inventory routing can all be triggered automatically at the point of return initiation rather than queued for human review.

When the returned item arrives at the warehouse, real-time updates mean it can be restocked and made available for resale immediately, rather than sitting in a processing queue.

The combined effect is faster refunds, lower handling cost and quicker recovery of sellable inventory.

Infios connects through native iPaaS, supporting API, EDI, SFTP and a variety of other communication mediums and SFTP connections to e-commerce platforms, ERPs, warehouse management system (WMS) platforms and carrier networks.

This means real-time order, shipment and payment data flows into a single agent view without requiring agents to log into multiple systems to answer a single inquiry. For retailers already running Infios Order Management (OM) or Infios WM (WM), the integration is native, eliminating the data silos that typically slow returns processing. For those on third-party systems, pre-built adapters allow connection without a full rip and replace of existing technology.

ROI comes from several directions: reduced reverse logistics spend through smarter routing, faster restock of sellable inventory, lower agent handle time from unified order visibility, reduced refund leakage from fraud controls and higher revenue retention from exchanges over refunds.

Infios customers typically see reverse logistics spend reductions of up to 25 percent. The loyalty impact is significant too: 76 percent of consumers return to brands that offer a hassle-free returns experience, making post-purchase experience a direct driver of lifetime customer value.

Machine learning (ML)-driven disposition models evaluate a return against multiple signals simultaneously:

• SKU resale probability

• Location-level storage costs

• Distance to the nearest distribution center

• Fraud risk indicators

Where a rules-based system would apply a fixed hierarchy (always route to the DC closest to the customer, for example), a machine learning model can weigh those factors dynamically and select the routing that minimizes total landed cost per return.

Over time, the model improves as it accumulates more return outcome data, making disposition recommendations more accurate without manual reconfiguration.