Strategic Responses to Rising Returns: A CX Imperative for Enterprise Retailers

The retail landscape continues to evolve, presenting both opportunities and significant challenges. One pressing concern for senior marketing and customer experience (CX) leaders in large enterprises is the escalating volume and cost of product returns. A recent analysis from Seel, an agentic post-purchase platform, highlights that holiday return activity increased by 16%, with late deliveries and product quality issues being primary drivers. This trend contributes to an annual industry cost estimated at $890 billion, representing 20-25% of retailer revenue.

This article will leverage key insights from Seel’s inaugural 2025 Returns and Refunds Report to outline actionable strategies for enterprise retailers. We will examine the financial implications of returns, identify specific operational drivers by category, and propose an advanced post-purchase experience framework designed to mitigate costs, enhance customer loyalty, and drive long-term growth.

The Escalating Cost of Returns and Shifting Consumer Expectations

Returns are no longer a peripheral operational concern; they represent a substantial financial burden and a critical factor in customer purchasing decisions. The Seel report underscores that the cost of returns for retailers reached an estimated $890 billion last year alone, eroding 20-25% of total revenue. During the peak holiday shopping period, return activity surged by 16%, driven by factors such as increased buyer remorse and “try before you buy” behaviors.

Consumers now consider a robust and transparent return policy a fundamental expectation rather than an added benefit. Data indicates that 74% of surveyed shoppers would not complete a purchase without a clear return option. This reflects a shift in consumer behavior where buying earlier, returning sooner, and expecting expedited refunds have become standard. While many consumers treat returns as a normal part of shopping—nearly one-third return at least one item annually—the overall friction points in the buying process lead to higher rates of return. This dynamic elevates the post-purchase experience to a pivotal role in shaping a shopper’s perception of a brand and influencing repeat purchases.

What this means: CX strategies must integrate returns management as a core component of brand trust and conversion, not merely an operational cost to be minimized. Ignoring this shift risks alienating a significant portion of the customer base and sacrificing potential revenue.

Root Causes of Returns: Category-Specific Drivers and Operational Gaps

A superficial approach to returns management is insufficient given the nuanced, category-specific drivers identified in the Seel report. Enterprise retailers must move beyond aggregate return rates to pinpoint the precise operational and product quality issues that contribute to returns across different product lines.

The report highlights distinct patterns:

• Fashion and Accessories: This category experienced the sharpest rise in delivery-related returns. Late deliveries jumped by 124% year-over-year, and missing packages increased by 42%. While the industry saw a 47% drop in delivering incorrect items, the overall delivery timeliness and accuracy for fashion remain critical pain points.
• Consumer Electronics: Here, product quality issues were more pronounced. Defective item returns climbed by 33% year-over-year, alongside a 13% rise in undelivered items. Conversely, this sector showed improvement in delivery timing, with a 45% drop in late deliveries.
• Secondhand/Resale Items: These products demonstrated significantly higher return rates, being returned 140% more often than new items. This indicates ongoing friction points related to condition transparency, sizing, or perceived value in the resale market.
• General Drivers: “Item Defective” increased by 14% and “Change of Mind” by 9% across categories, reflecting both quality control challenges and evolving consumer purchase confidence.

These disparate trends underscore that a one-size-fits-all return policy or operational fix will fail to address the underlying causes effectively. For instance, a telecom provider selling consumer electronics alongside services will face different return challenges for a defective mobile device versus a ‘change of mind’ return for a fashion accessory sold through a marketplace integration.

What to do:

• Implement Granular Tracking: Utilize CRM and ticketing systems to track return reasons at the SKU, vendor, and delivery partner level. For example, a major retailer should distinguish between “item damaged in transit” for a furniture delivery versus “item not as described” for an apparel item.
• Cross-Functional Analysis: Establish dedicated task forces comprising logistics, product development, CX, and marketing teams. These teams should analyze category-specific return data to identify systemic issues and implement targeted solutions. For a fashion retailer, this could involve reviewing 3PL Service Level Agreements (SLAs) for last-mile delivery performance.
• Enhance QA Protocols: For categories like electronics, implement pre-shipment testing or more rigorous quality assurance checks with manufacturing partners to reduce defective product rates. A B2B SaaS provider, by analogy, would use feature flagging and A/B testing to identify and fix software defects before wider release, preventing churn.
• Pilot Purchase Protection: Consider offering or integrating third-party purchase protection programs, especially for items above a certain value threshold (e.g., $50 and above), as the report noted a 50% increase in shoppers selecting this option. This can build confidence and mitigate perceived risk.

What to avoid:

• Homogenizing Return Policies: Do not apply identical return policies across vastly different product categories without considering their unique logistical and quality challenges.
• Solely Focusing on Containment: Resist the urge to optimize for a single metric like “return rate reduction” without understanding the underlying drivers, which can lead to punitive policies that damage customer loyalty.
• Ignoring Operational Data: Avoid making policy changes without comprehensive analysis of operational data from logistics partners, warehouse fulfillment, and product quality reports.

Architecting a Post-Purchase Experience for Loyalty and Growth

A strategic, data-driven approach to the post-purchase experience can transform returns from a significant cost center into a powerful driver of customer loyalty and long-term revenue growth. By viewing returns as an opportunity for engagement rather than mere transaction reversals, enterprises can reinforce brand trust and glean invaluable product and service insights. Seel’s platform, for instance, uses AI to analyze patterns, anticipate return spikes, detect quality issues, and facilitate refund experiences that strengthen customer relationships.

Operating Model and Roles: A robust operating model for post-purchase experience involves clear ownership and cross-functional collaboration:

• CX/Customer Service Leaders: Empower agents with streamlined tools (e.g., integrated CRM, self-service portals) and clear escalation paths for complex return scenarios (e.g., a “red-flag” protocol for high-value or repeat returns).
• Product Development Teams: Integrate return reason codes and customer feedback directly into product lifecycle management. Data on “item defective” for an electronics product should trigger engineering review for design improvements in subsequent iterations.
• Marketing & Brand Strategy: Leverage transparent and customer-friendly return policies as a key differentiator in messaging, reinforcing brand reliability and confidence.
• Data Science & Analytics: Develop predictive models to anticipate return likelihood based on purchase history, product category, and customer segmentation. This informs proactive outreach and personalized offers.
• Logistics & Fulfillment: Establish clear SLAs with 3PL partners for return processing speed and accuracy.

Governance and Risk Controls: Effective governance is crucial to balancing customer experience with financial prudence:

• Policy Clarity: Develop unambiguous, easily accessible return policies (e.g., 30-day window for unused items, 7-day for defective electronics; up to $25 credit for shipping costs on apparel returns).
• Fraud Detection: Implement AI-powered fraud detection systems that flag suspicious return patterns, such as excessive returns from single accounts or returns of incorrect items.
• Data Privacy and Consent: Ensure explicit customer consent for using purchase and return data to personalize future experiences, adhering to regional data privacy regulations.
• Performance Thresholds: Define acceptable return rate thresholds by product category and SKU. Automated alerts should trigger cross-functional reviews when these thresholds are exceeded by a defined margin (e.g., 10% above baseline).

Key Performance Indicators (KPIs): Measurement ensures continuous improvement and accountability:

• Customer Effort Score (CES): For the return process (target: >8.5/10).
• CSAT/NPS Post-Return Interaction: (target: >70% / >50% respectively).
• Repeat Purchase Rate After Return: Track customer repurchase behavior following a return, aiming for rates comparable to or exceeding general repeat purchase rates.
• Return-to-Conversion Ratio: Measure how many returns ultimately lead to an exchange or re-purchase.
• Complaint Rate Related to Returns: (target: <5% of all return transactions).
• Cost-Per-Return: Continuously track and aim to reduce the average cost associated with processing each return (e.g., target a 5-10% year-over-year reduction).

What ‘good’ looks like: A large-scale retailer with a highly effective post-purchase experience will demonstrate a seamless, transparent return process where customers feel valued, even when returning an item. Insights from returns are systematically fed back into product development and operational improvements, leading to reduced defect rates and improved delivery reliability. This creates a virtuous cycle where positive return experiences foster loyalty, drive repeat purchases, and contribute directly to the bottom line.

Immediate Priorities (First 90 Days):

1. Deep Dive Data Analysis: Conduct a comprehensive audit of existing return data, categorizing by reason, product, channel, and customer segment.
2. Customer Journey Mapping: Map the current return process from the customer’s perspective, identifying critical friction points and opportunities for self-service or expedited resolution.
3. Technology Evaluation: Assess current technology infrastructure for returns management, identifying gaps in AI-driven analytics, fraud detection, and integration capabilities with existing CRM and ERP systems.
4. Governance Framework Establishment: Convene a cross-functional leadership group to establish explicit roles, responsibilities, and KPIs for post-purchase experience and returns management.

Summary

The rising tide of returns, exacerbated by late deliveries and product quality concerns, presents a significant financial and operational challenge for enterprise retailers. However, as revealed in Seel’s 2025 Returns and Refunds Report, this challenge is also an opportunity for strategic differentiation. By embracing a data-driven approach to understanding return drivers, investing in AI-powered post-purchase solutions, and architecting an operating model focused on customer loyalty, CX and marketing leaders can transform returns from a cost center into a powerful engine for growth and brand resilience. Proactive engagement with the post-purchase experience is no longer optional; it is a critical imperative for maintaining competitive advantage and fostering enduring customer relationships in a dynamic retail environment.