The Future of Returns Management—Minimizing Margin Erosion in Peak E-Commerce Seasons

The Trillion-Dollar Returns Challenge

As e-commerce continues its rapid ascent, retailers are facing a persistent and costly challenge: product returns. During peak seasons—especially the holidays—return rates can soar, threatening to erode already thin margins. In fact, industry data shows that 5–10% of all sales are returned, with the cost of processing these returns often outweighing the value of the goods themselves. For many retailers, the returns problem is no longer just a logistical headache; it’s a strategic imperative that can make or break profitability.

The stakes are even higher as consumer expectations for seamless, low-cost returns collide with inflationary pressures, supply chain disruptions, and the ongoing shift to digital-first shopping. Retailers must now balance the need to deliver a positive customer experience with the imperative to protect their bottom line.

Two Sides of the Returns Equation

Solving the returns issue requires a dual approach:

Minimizing the volume of returns—ensuring customers get what they want the first time.
Minimizing the cost and impact of returns—making the reverse logistics process as efficient and intelligent as possible.

Let’s explore actionable strategies for both.

1. Minimizing Returns: Getting It Right the First Time

Product Data Enrichment

The foundation of reducing returns is ensuring customers know exactly what they’re buying. This means investing in rich, accurate product data:

Detailed descriptions that go beyond marketing copy to set clear expectations.
High-definition images and videos that show products from multiple angles and in real-life contexts.
Contextual information such as model sizing, dimensions, and user-generated content (e.g., reviews, fit feedback).

Retailers who leverage robust product data not only reduce the likelihood of “not as described” returns, but also build trust and confidence with shoppers.

Sizing Tools and Fit Intelligence

Apparel and footwear are notorious for high return rates, often due to sizing uncertainty. Leading retailers are deploying:

AI-powered fit recommendation engines that use past purchase and return data to suggest the best size for each customer.
Virtual try-on tools and detailed fit guides, including model measurements and customer-submitted photos.
Personalized size suggestions based on previous purchases across brands and categories.

By helping customers choose the right size the first time, retailers can dramatically reduce “bracketing” (ordering multiple sizes with the intent to return most) and the associated costs.

Customer Segmentation for Returns Policies

Not all customers are created equal when it comes to returns. Some are serial returners, while others rarely send items back. Retailers are increasingly using data to:

Identify high-return-rate customers and tailor returns policies accordingly (e.g., charging for returns after a threshold, or limiting free returns for chronic returners).
Reward low-return customers with perks like free returns or expedited refunds.
Encourage in-store returns for certain segments, which can reduce shipping costs and create upsell opportunities.

Dynamic, data-driven returns policies allow retailers to balance customer satisfaction with profitability, rather than applying a one-size-fits-all approach.

2. Minimizing the Cost and Impact of Returns

Intelligent Reverse Logistics

Returns are inherently costly, but technology can help optimize the process:

Dynamic routing: Use real-time inventory and demand data to direct returns to the location where they’re most needed—whether that’s a warehouse, a store, or even directly to another customer.
Automated decisioning: For low-value or hard-to-resell items, it may be more cost-effective to let the customer keep the product, donate it, or facilitate a peer-to-peer return, rather than pay for shipping and processing.
Consolidated returns: Encourage customers to bundle multiple returns into a single shipment, reducing transportation costs and environmental impact.

AI-Driven Returns Optimization

Artificial intelligence is transforming returns management by:

Predicting return likelihood at the point of sale, enabling proactive interventions (e.g., additional product information, size guidance, or post-purchase support).
Automating return approvals and routing based on profitability, product condition, and customer history.
Optimizing restocking and resale by quickly assessing returned items and determining the best disposition (resale, refurbishment, donation, or recycling).

Innovative Approaches: Dynamic and Peer-to-Peer Returns

Forward-thinking retailers are experimenting with:

Dynamic returns policies that adjust in real time based on customer behavior, product type, and margin impact.
Peer-to-peer returns models, where returned items are sent directly from one customer to another, bypassing central warehouses and reducing costs.
Community drop-off points or neighborhood collection hubs, consolidating returns and streamlining logistics.

Balancing Customer Experience and Profitability

A frictionless returns experience is a key driver of customer loyalty—84% of shoppers say they would reject a retailer after a bad returns experience. However, overly generous returns policies can quickly erode margins, especially when abused by a small subset of customers.

The future of returns management lies in striking the right balance:

Transparency: Clearly communicate returns policies and set expectations at every stage of the customer journey.
Personalization: Use data to tailor the returns experience, offering flexibility to high-value, low-return customers while managing risk with stricter policies for others.
Sustainability: Reduce the environmental impact of returns by minimizing unnecessary shipments, encouraging in-store or consolidated returns, and promoting donation or recycling options.

The Path Forward: Returns as a Strategic Lever

Returns are no longer just a cost center—they are a strategic lever for differentiation and profitability. Retailers who invest in data enrichment, AI-driven optimization, and dynamic policy management will not only reduce the volume and cost of returns, but also build stronger, more loyal customer relationships.

As peak e-commerce seasons continue to test the limits of retail operations, the winners will be those who treat returns management as a core pillar of their digital strategy—one that is agile, intelligent, and relentlessly focused on both customer experience and the bottom line.

Ready to transform your returns management? Publicis Sapient partners with leading retailers to design and implement technology-enabled solutions that minimize margin erosion and unlock new value from the returns process. Connect with us to learn more about how we can help you turn returns into a competitive advantage.