How Analytics and AI are Gamechangers for Preventing Merchandise Returns

Digital shopping makes it easy for consumers to save time and effort. This convenience comes with a drawback — the tactile experience of in-person shopping. How often have you tried on several items to find the right fit? Or wanted to make sure those new windshield wipers will fit your car before buying them? Because it's harder to provide those experiences virtually, and the numbers support that narrative: The current ecommerce return rate is around 16.5%, which adds up to a $213 billion USD loss — ultimately hurting retailers' bottom line.

But there's an upside. New technology is a game changer for preventing high merchandise return losses. By using analytics and AI tools, brands can discover the information consumers want and find the right ways to provide it. And by using both, they can create a seamless journey that results in product purchases that meet consumer needs.

The retail world has seen enormous amount shifts over the past few years. While it's been challenging, it resulted in some meaningful strategies. The customer experience has always been front and center, but the shift in purchasing patterns has forced brands to consider every channel their customers shop on and learn lessons from each one. When losses due to returns occur, it tells brands there's still work for them to do.

Analytics plays a key role in determining satisfaction or dissatisfaction. For example, it can tell you the top reasons people returned a product. It could be the wrong product or size. Or items arrived later than expected or were damaged. The more data you collect, the better your ability to prevent those issues from happening.

AI in analytics is helping retailers discover return issues. It can:

• Analyze transactions and reviews to pinpoint the root cause of a product return. Let's say a shirt was frequently returned and then ordered by the customer in the next size up. AI could help your team swiftly identify that the product runs small — and include that in your descriptions.

• Detect anomalies. If you project a certain number of returns during a high-shopping season, any anomalies would be flagged so you could correct issues before they take hold.

• Compare kept vs. serial return products. By evaluating kept and returned items, brands can assess the quality and prevent behaviors like bracketing.

• Review and reinforce returns fraud protection. The usual signs of fraud can be reviewed by AI on a much larger and faster scale than traditional methods.

The data you gather is essential to creating strategies and content that prevent returns.

The more accurate product information you provide, the better your chance of a strong sale. Offer consistent information throughout all channels, and provide tools that let consumers see a product in action. Brands can do things like:

• Include sizes, specifications, images, videos, and detailed descriptions. You can also list alternatives — like similar products people ended up purchasing. The goal is to guide customers to products that will best suit their needs.

• Embed feedback from other customers. Adding size ratings (true-to-fit, runs small or large), reviews, testimonials, or Q&As satisfies consumers' need to see what others say about a product without leaving the channel. That same data set can be combined with past purchasing patterns to let AI predict how a clothing item will fit a customer, or tell a brand how likely it is to be returned. In today's world, that's essential, given that 49% of consumers trust reviews as much as personal recommendations from friends and family members.

• Implement AI tools that give consumers an experience beyond typical content. AI can help you view products in 3D or present an augmented reality that gives a more detailed representation.

An AI-powered content effort

One challenge brands encounter is the time and cost of generating the needed content. But AI can help with the effort to get accurate, detailed product information online. It can draw information from manuals, instructions, training materials, video transcripts, reviews, FAQs, and more, pulling it into one central hub. From there, it can boost your ability to create SEO-optimized, informative product descriptions.

Returns will always be a part of commerce, which makes it essential to keep the customer journey in mind. If someone is dissatisfied with a product, you can still inspire loyalty in your brand by making the experience simple and effortless. Look at returns as another opportunity to engage with your customers. Try:

• Simplifying the return processes by recommending the nearest dropoff or help you schedule a pickup

• Helping customers exchange for an item that works, and using AI to quickly suggest relevant products with high user ratings, sale prices, or both.

• Using analytics and personalization to suggest potential replacements.

• Offering omnichannel returns: In-store, through a return bar, or a digital process.

Your commerce strategy includes return prevention and processes that enhance the customer experience. Learn how you can deliver a better experience throughout the funnel and generate content. Or, level up your commerce strategy using AI-powered solutions like Yext's Commerce, and Analytics.