The Future of Returns: Turning a Trillion-Dollar Problem into a Competitive Advantage

E-commerce has revolutionized retail, but it has also unleashed a returns tsunami that is eroding margins, straining supply chains, and challenging even the most sophisticated retailers. With global returns now exceeding $1 trillion annually—and online purchases returned up to three times more often than in-store buys—returns are no longer a back-office nuisance. They are a strategic imperative and a defining battleground for customer loyalty and profitability.

Yet, for forward-thinking retailers, the returns challenge is also a powerful opportunity. By leveraging data, artificial intelligence (AI), and operational innovation, leading brands are transforming returns from a cost center into a source of competitive advantage. Here’s how.

The Returns Dilemma: Margin Erosion and Operational Complexity

Returns are costly. Every returned product can result in a loss of 5 to 8 margin points, even if it is resold. The pandemic accelerated online shopping and, with it, a surge in returns—creating backlogs that rival the post-holiday rush, but without the corresponding sales boost. For many retailers, the cost and complexity of returns threaten overall profitability, with some reporting that e-commerce is less profitable than brick-and-mortar precisely because of returns and fulfillment costs.

But the impact goes beyond the bottom line. Returns are a critical touchpoint in the customer journey. Nearly half of consumers say that an easy returns process is a deciding factor when choosing where to shop online. A poor experience can erode trust and drive customers to competitors, while a seamless, transparent process can build loyalty and repeat business.

A Two-Pronged Approach: Prevention and Optimization

To turn returns into a competitive advantage, retailers must address both sides of the equation:

1. Reducing Preventable Returns with Data and AI

The best return is the one that never happens. Many returns are preventable, stemming from mismatched expectations, poor product information, or sizing issues—especially in categories like apparel, where “bracketing” (buying multiple sizes with the intent to return) is common.

Best Practices:

Enhanced Product Information: Rich, accurate product descriptions, high-quality images, and detailed sizing guides help set clear expectations and reduce uncertainty.
AI-Powered Fit and Recommendation Tools: Solutions like TrueFit use customer data (height, weight, past purchases) to recommend the right size, reducing bracketing and fit-related returns.
Personalized Guidance: AI-driven chatbots and virtual assistants can answer product questions in real time, boosting confidence and reducing the likelihood of returns due to misunderstanding or lack of information.
Customer Segmentation: By analyzing returns history, retailers can identify high-return customer segments and tailor marketing, incentives, or even restrict certain behaviors (e.g., limiting multi-size purchases or incentivizing in-store returns for frequent returners).
Dynamic Policies: For seasonal or high-return items, dynamic pricing or return windows can encourage faster returns, improving resale opportunities and reducing markdowns.

Technology Enablers:

Centralized product data platforms
AI/ML models for fit prediction and personalization
Augmented reality (AR) for virtual try-ons or in-home visualization
Real-time analytics to monitor and act on return trends

2. Optimizing Reverse Logistics and In-Store Experiences

Even with the best prevention, returns will happen. The key is to minimize their cost and maximize value recovery.

Best Practices:

Reverse Logistics Optimization: Use data to route returns to the optimal location—whether a warehouse, store, or third-party partner—based on inventory needs, resale potential, and cost. Dynamic shipping labels can direct returns to where they are most needed, reducing unnecessary transport and handling.
Speed to Resale: The faster a returned item is processed and made available for resale, the higher the margin. AI-powered inventory visibility and automation can accelerate this process, especially for fast-moving or seasonal goods.
In-Store Returns as an Experience: Designate dedicated spaces and equip associates with digital tools to process returns efficiently. In-store returns offer a chance to engage customers, resolve issues, and even drive incremental sales through personalized offers or exchanges.
Sustainability and Circularity: Integrate returns into broader sustainability initiatives—such as resale, refurbishment, or recycling programs—to recapture value and meet growing consumer expectations for responsible retail.

Technology Enablers:

Integrated order and inventory management systems
Robotics and automation in warehouses
Mobile POS and digital associate tools for in-store returns
Data-driven decision engines for routing and disposition

Leading by Example: Retailers Transforming Returns

Apparel and Footwear: Brands leveraging AI-powered fit tools and AR try-ons have seen significant reductions in size-related returns and improved customer satisfaction.
Grocery and General Merchandise: Retailers with robust click-and-collect and in-store return options are not only reducing last-mile costs but also increasing store footfall and cross-sell opportunities.
Marketplaces: Some leading platforms use AI to segment customers by return behavior, offering incentives for low-return shoppers and nudging high-return segments toward more cost-effective return channels.

Returns as a Loyalty and Profitability Driver

Returns optimization is not just about cost-cutting—it’s about building trust, loyalty, and long-term value. Retailers that balance operational efficiency with customer-centric experiences are seeing:

Higher customer retention and repeat purchase rates
Improved margins through reduced return rates and faster resale
Enhanced brand reputation and differentiation in a crowded market

The Path Forward: Actionable Steps for Retail Leaders

Invest in Data and AI Foundations: Build the infrastructure to collect, analyze, and act on product, customer, and returns data across channels.
Break Down Silos: Integrate returns management with supply chain, store operations, and customer experience teams for a holistic approach.
Pilot and Scale Innovations: Start with micro-experiments—such as AI fit tools or dynamic return routing—and scale what works.
Measure What Matters: Track not just return rates and costs, but also customer satisfaction, loyalty, and value recovery.
Communicate Transparently: Set clear expectations with customers about return policies, timelines, and options—transparency builds trust.

Why Publicis Sapient?

Publicis Sapient partners with leading retailers to reimagine the returns journey—combining strategy, technology, and operational expertise to unlock new value. Our experience spans AI-driven personalization, supply chain transformation, and omnichannel experience design, helping clients turn the returns challenge into a source of growth and differentiation.

Ready to transform your returns process into a competitive advantage? Let’s start the conversation.