Reshaping e-Commerce To Minimise Returns

In an industry where fit, feel, and appearance are paramount, the inability to physically try on items before purchase has led to a culture of “bracketing”. This is where customers order multiple sizes or styles with the intention of returning those that don’t meet their expectations. This practice, while understandable from a consumer perspective, creates a ripple effect of costs and inefficiencies throughout the supply chain, as different parties work towards moving and often wildly inaccurate targets.

The impact of how retailers’ process returns is also particularly valid when it comes to retaining loyal customers. These high-value shoppers, who contribute significantly to a brand’s bottom line, expect a seamless and satisfying shopping experience. Having a simple, clear, and straightforward online return goods policy in place is key. This is in addition to reducing the amount of returns made to prevent customer’s trust and satisfaction eroding. 

Once trust is lost or customers’ return goods that don’t fit well or are losing quality, these reasons for returning goods more often will potentially drive them to competitors. However, there are great digital strategies and techniques that brands can adopt to minimise returns, with a special focus on retaining and delighting loyal customers. From advanced sizing technologies to personalised shopping experiences powered by artificial intelligence, these innovations promise to bridge the gap between online convenience and in-store confidence.

Intelligent customer service

Customer service technologies are revolutionising the way fashion brands interact with their customers, providing instant support and personalised guidance throughout the shopping journey. Virtual Assistants offer guided shopping experiences without the need for you to expand your in-house team. 

For example, Gucci offers digital advisors on their site to help customers find the right products for their needs. Luxury fashion giant Louis Vuitton also uses AI chatbots to provide historical knowledge and details about items to customers, and will even enable buyers to share the recommendations with their friends on social media before making a purchase. 

These tools can analyse a customer’s preferences, purchase history, and browsing behaviour to offer tailored product recommendations and styling advice, and walk customers through size charts, explain fabric properties, and even suggest complete outfits based on the customer’s style profile. This level of personalised attention not only enhances the shopping experience but also significantly reduces the likelihood of returns by ensuring customers make well-informed purchases. 

The integration of AI agents into the fashion industry also offers promising avenues for content creation, customer service, and other operational efficiencies. New tools boost the capability of brands to anticipate a customer’s willingness to purchase which is a huge advantage for any eCommerce and in-store retailer. AI tools also have the potential to help brands identify even more facets of a customer’s buying behaviour with predictive analytics and create actionable insights to use in a sales strategy. 

With tools like Google’s newly announced Vertex AI Agent Builder and Creative Agent, fashion brands now have access to advanced AI capabilities that are both flexible and easy to implement. These AI agents can handle a variety of tasks, from generating creative content to providing responsive customer support, potentially transforming the way fashion brands interact with customers and manage internal processes.

Advanced digital sizing solutions

Made-to-measure approaches and sizing solutions are a game-changer in the fashion e-commerce industry’s battle against returns. This process typically begins with the customer providing detailed measurements, which can be taken manually or using advanced body-scanning technology. These measurements are then used to create a digital pattern that is adjusted to fit the customer’s unique shape. Customers can specify their fit preferences, such as the desired length of sleeves or the fit around the waist, which ensures that the garment meets the customer’s expectations, further reducing the chances of returns.

Notably, though, making clothing to measure requires a fundamentally different approach to the supply chain – which is why it typically appears as just a small part of a brand’s overall business, except in cases where a company exclusively makes on-demand.

AI-powered size recommendation engines are another approach, one which has been adopted by e-commerce giants like Amazon in recent years, while Fit Analytics’ Fit Finder solution is used by major brands like Calvin Klein, The North Face, and Ganni to provide size recommendations based on customer data and garment specifications.

These sophisticated systems analyse a customer’s past purchase history, body measurements, and fit preferences to suggest the most appropriate size for each garment. By leveraging machine learning algorithms, these engines continuously improve their accuracy, learning from each successful (and unsuccessful) purchase.

Perhaps the most advanced solution in this arena is 3D body scanning coupled with digital avatars. This technology enables customers to create highly accurate digital replicas of their bodies, either through smartphone apps or in-store scanning booths. These avatars can then be used to “try on” clothes virtually, with the system simulating how fabrics will drape and move on the customer’s unique body shape.

Data analytics

Data analytics plays a crucial role in alerting retailers when return rates are high, allowing them to proactively address underlying issues. When brands and their logistics partners conduct returns analytics, they can pinpoint specific problems such as sizing inaccuracies, design flaws, damage during transit, or delays along certain delivery routes – addressing these issues promptly can lead to substantial improvements over time.

Receiving alerts about problems that cause higher returns allows brands to make data-driven decisions. Parcel shipping service providers, for example, are increasingly investing in dedicated portals that offer retail customers access to data and real-time alerts about return-related issues.

For instance, platforms like Asendia’s e-PAQ Returns provide retailers with robust data analytics tools to tackle and reduce returns effectively. These platforms offer a returns data dashboard, giving insights into specific products being returned, the regions or countries from which returns originate, and the reasons for returns. Such detailed data enables retailers to identify trends and take timely actions to minimise return rates.

Tracking improvements is also crucial. For example, if sizing issues are identified in a particular country and subsequently addressed, a reduction in return rates should follow. Similarly, if high return rates in a specific city are due to late parcel arrivals, the retailer can identify and rectify the performance issues with the outbound carrier.

Post-purchase digital engagement

Post-purchase digital engagement has become a critical strategy for fashion brands seeking to minimise returns, attract returning custom, and foster customer loyalty. At the heart of this approach is automated fit feedback collection. This process engages customers shortly after their purchase to gather valuable insights or invite them to complete a footprint calculator or survey that might also promote a brand’s unique selling point (USP), ethical value, and mission statement. 

By prompting customers to share their thoughts on their overall satisfaction with recent purchases, brands can identify potential issues early and analyse feedback to better understand buying habits, or monitor ethical values, to address them proactively. This not only helps prevent returns but also provides crucial data for improving future product designs and sizing recommendations.

Digital wardrobe management tools represent another innovative post-purchase engagement technique. This software helps consumers organise, style, and maintain their clothing collections, fostering ongoing interaction with brands and retailers. For example, Stylebook is an app that allows users to digitise their wardrobe, create outfits, and plan what to wear week by week. Another option is Indyx, where users can catalogue their wardrobe or hire an Indyx Archivist to do it for them, and build unlimited outfits in the process. 

These apps or web-based platforms allow customers to catalogue their purchases, creating a virtual representation of their wardrobe. Advanced versions of these tools can suggest new outfit combinations using items the customer already owns, recommend complementary pieces for future purchases, and even track wear frequency to help customers make more sustainable fashion choices. By increasing the utility and enjoyment customers derive from their purchases, these tools can reduce the likelihood of returns and encourage more thoughtful buying habits.

Final thoughts

From AI-powered sizing recommendations and virtual try-on experiences to personalised post-purchase engagement and mobile-first shopping solutions, these digital strategies are reshaping the e-commerce landscape. By providing customers with more accurate product information and tailored assistance, brands can significantly reduce the uncertainty that often leads to returns.

Check out The Interline’s recent AI report to explore the impact artificial intelligence is having on the fashion industry, and the positives and negatives that businesses need to consider when incorporating AI into their processes.