AI-Powered Prediction: Return Rates and Cutting Costs

To get our insights in your email, Subscribe to our Newsletter.

Did you know that a whopping 30% of all eCommerce purchases are returned? This translates to a mountain of lost sales and frustrated customers for online retailers. The return process is a hassle for everyone involved, and it can leave a negative impact on your brand. But what if there was a way to prevent these returns from happening in the first place?

Enter the power of Artificial Intelligence (AI). eCommerce giants like Amazon are leading the charge by utilizing AI and Machine Learning (ML) models to revolutionize product recommendations. For instance, Amazon's AI tailors "fit wardrobes" based on a customer's body shape, significantly reducing the risk of returns in the fashion category.

This is just one example of how AI is transforming the eCommerce landscape. By harnessing AI's data analysis and predictive capabilities, businesses can personalize the customer experience, optimize inventory, and streamline operations. The result? Increased customer satisfaction, operational efficiency, and a clear competitive edge.

Whether you're a seasoned eCommerce veteran or a startup venturing online, AI-powered solutions are no longer a luxury – they're a necessity. This article will delve into the world of AI and eCommerce returns, exploring how to leverage AI to predict returns, minimize return rates, and ultimately, boost your bottom line.

Deciphering Returns: Boosting Sales vs. The Logistics Labyrinth

For eCommerce businesses, returns are an inevitability. But why do returns occur?

One reason is the impersonality. The digital marketplace allows customers to browse, compare, and shop products online, but they lose in-person interaction with the product before purchase. Customers are not likely to feel a personal connection to the product, resulting in returns if they don’t like the delivered item, or just due to a change of heart.

Consumers also expect shopping to be seamless and easy. They expect to be able to complete purchases within a few clicks. This results in more impulse purchases, but a higher shopping rate leads to higher returns.

eCommerce returns can result in financial losses for the retailer caused by the cost of inspection, restocking, and often refurbishing too. In addition, returns can also increase costs through operational efficiency, as they strain operations teams and disrupt the overall workflow. Worst of all, lengthy return and replacement processes can result in diminished customer satisfaction.

US eCommerce businesses aren’t legally mandated to accept returns, though they are required to post their return policy prominently. A liberal return policy increases the comfort of first-time purchasers, reducing the barriers to purchase. That’s great from the customer acquisition and customer satisfaction perspectives. However, there’s no denying that returns significantly complicate logistics, accounting and operations.

This is further complicated by the fact that return rates vary based on a large number of factors. The best product, well-discounted and delivered promptly, could still be returned. The client may have made a wrong measurement. They may feel the delivered product doesn’t precisely match what they remember seeing on the screen. They may have simply changed their mind! These uncontrollable factors, and others, contribute to returns - and how can one plan for or prevent such returns.

eCommerce returns also vary by product category. For example, Prime AI size finder says that luxury apparel, swimwear, and bra retailers see higher returns, even up to 50%, as these clothes are more likely to fit improperly.

All this means is that there’s no such thing as a “typical” return rate. Each business’ rate will vary based on a range of factors, including product category.

Liberal return policies, while great for customer acquisition and satisfaction, create logistical headaches for eCommerce businesses. Return rates vary wildly, influenced by factors like incorrect measurements, product misrepresentation, or simple buyer's remorse. Predicting and preventing these returns remains a challenge.

How can businesses improve return rates without having to look at competitors?

How does AI/ML help in Return predictions?

AI Steps In: How Machine Learning Predicts Returns

As online shopping continues to surge, the challenge of managing returns grows alongside it. However, the integration of AI/ML algorithms presents a promising avenue for effectively predicting and handling returns.

Consider ThredUp, a leading online marketplace for secondhand clothing, which has embraced AI/ML algorithms to predict and manage returns effectively. Through a meticulous analysis of extensive historical data encompassing customer preferences, item characteristics, purchase behaviors, and seasonal trends, ThredUp's AI system uncovers intricate patterns and correlations.

For instance, ThredUp discovered that certain clothing categories, like designer handbags or vintage dresses, exhibit higher return rates due to factors such as fit uncertainty or discrepancies in item condition. Moreover, the algorithm identifies peak return periods coinciding with events like holiday seasons or promotional campaigns.

Leveraging these insights, ThredUp can proactively adjust its inventory assortment, refine its pricing strategies, and enhance its recommendation engine to minimize return rates. By harnessing the predictive prowess of AI/ML algorithms, ThredUp not only optimizes its operational efficiency but also delivers a more personalized and satisfying shopping experience to its customers.

Product-Level Predictions

AI algorithms learn the product data in detail. They study all attributes, including category, brand, price, history of return patterns, and more. That means that each product is flagged by the AI model with a predict of the possibility of return. This granular level of prediction helps businesses maintain their inventories effectively. Businesses can also implement suitable strategies such as pricing adjustments or refined descriptions to reduce the risk of return.

Customer Segmentation

By leveraging machine learning techniques such as clustering, your business can identify customer segments that are at high risk of returning products. For example, customers who purchase only (or most often) during promotional periods may also return the products at a higher rate. When you have this data, you can tailor your marketing strategies to suit and thus improve your return rate.

Real-Time Monitoring and Proactive Adjustments

AI models help with real-time data monitoring. These models integrate with your eCommerce platform to identify customer preferences, any shift in return patterns, and other external factors that influence these returns. Based on this information, AI helps businesses take a proactive approach and adjust accordingly.

Implementing AI to predict Returns: How to Get Started?

There are two ways that you can leverage the power of AI to predict returns:

Cloud-Based AI Solutions

The first method is to opt for cloud-based AI solutions that predict return probabilities and streamline operations. These solutions come with pre-built models that have already learned from large datasets.

This means that businesses can deploy these solutions quickly, without extensive expertise in the field. Cloud-based solutions are also scalable and flexible, so businesses can utilize them based on their requirements and operational capabilities.

Integrating AI with Existing Data Analytics Platforms

Those who already employ data analytics platforms can integrate the power of AI-based eCommerce return management into their existing environment. Here, businesses use AI solutions in coordination with their existing analytics tools to enhance predictive capabilities even more.

Benefits of AI-Powered Return predicting

By leveraging advanced analytics and predictive capabilities, AI can benefit businesses in many ways. The primary benefit is, of course, cost savings. With accurate return predicts, AI helps you utilize your resources more effectively. This reduces unnecessary processing and cost markdown, minimizing the negative impact of returns. In addition, here are some other benefits of integrating AI-powered tools into your eCommerce ecosystem.

Improved Inventory Management

One of the significant benefits of AI-powered eCommerce return management is the insight into the likelihood of returns. If you know the chances of product returns, companies can manage inventory better.

Optimized Product Offerings

When you know which products are going to be returned, and why, you can calibrate your offerings. For example, you can prioritize products with low return rates, or address issues with high-return items. This also saves return-related expenses.

Streamlined Return Processes

AI-based predictions also help businesses optimize the return process, resulting in lower turnaround times and faster refunds. AI can automate return authorizations, which ensures less human error and improves operational efficiency.?

Enhanced Customer Experience

By addressing return-related issues ahead of time and letting businesses take a proactive approach to product and returns optimization, AI improves overall customer satisfaction. This leads to higher brand trust, long-term relationships and customer loyalty.

AI and ML truly hold the power to transform eCommerce businesses. AI unlocks valuable insights and can recognize return patterns. This helps improve inventory management and cost savings.?

Whether you choose platform-based AI solutions or third-party cloud-based tools, your business reaps the benefits in the form of improved return rates.?

At Ziffity, our eCommerce managed services include AI-powered solutions. Work with us on return predicting, resolving return-related challenges, and driving operational efficiency. Work with a solution provider that handles AI implementation from start to finish. Choose Ziffity and unlock a new competitive edge.

Follow us for more technology insights!