Australian E-commerce Businesses Reduce Return Rates by 18% with AI
AI workflows to predict returns, improve product descriptions
Mirai Team
July 25, 2026
Australian e-commerce businesses are constantly looking for ways to improve their bottom line, and one key area of focus is reducing return rates. Returns can be a significant expense for online retailers, with the average return rate in Australia sitting at around 20%. However, by leveraging Artificial Intelligence (AI) and Machine Learning (ML), many businesses are now able to predict and prevent returns, resulting in significant cost savings. For example, a recent study found that Australian e-commerce businesses that implemented AI-powered return prediction systems were able to reduce their return rates by an average of 18%.
The Cost of Returns
The cost of returns can be substantial, with some estimates suggesting that it can be as high as 30% of the original purchase price. This includes not only the cost of shipping and handling the returned item, but also the cost of processing the return and restocking the item. Furthermore, returns can also have a negative impact on customer satisfaction and loyalty, as customers who experience issues with their purchases are less likely to return to the same retailer in the future. By reducing return rates, Australian e-commerce businesses can not only save money, but also improve customer satisfaction and loyalty.
Predicting Returns with AI
So, how can AI help predict and prevent returns? One key way is by analyzing customer behavior and purchase history to identify patterns and trends that may indicate a higher likelihood of return. For example, an AI system may analyze data on customer purchases, including the types of products purchased, the frequency of purchases, and the customer’s returns history. By identifying these patterns, AI systems can flag potential returns and alert retailers to take proactive steps to prevent them. This may include sending targeted communications to the customer, such as product care instructions or troubleshooting tips, or offering a replacement or refund before the customer even requests one.
In addition to analyzing customer behavior, AI systems can also be used to improve product descriptions and product imagery, which are two of the most common reasons for returns. By analyzing data on customer returns and feedback, AI systems can identify areas where product descriptions or imagery may be inaccurate or misleading, and provide recommendations for improvement. For example, an AI system may analyze customer reviews and feedback to identify common complaints about product sizing or color, and recommend changes to the product description or imagery to better reflect the actual product. By improving product descriptions and imagery, retailers can reduce the likelihood of returns due to customer dissatisfaction with the product.
Real-World Example
One Australian e-commerce business that has seen success with AI-powered return prediction is online fashion retailer, The Iconic. The Iconic implemented an AI-powered return prediction system that analyzed customer behavior and purchase history to identify potential returns. The system also analyzed data on product descriptions and imagery to identify areas for improvement. As a result, The Iconic was able to reduce its return rate by 15%, resulting in significant cost savings. The company also saw an improvement in customer satisfaction and loyalty, as customers were less likely to experience issues with their purchases.
The Role of Machine Learning
Machine Learning (ML) plays a critical role in AI-powered return prediction systems, as it enables the system to learn and improve over time. By analyzing large datasets and identifying patterns and trends, ML algorithms can develop predictive models that accurately identify potential returns. These models can then be used to inform business decisions, such as which customers to target with proactive communications or which products to improve. For example, an ML algorithm may analyze data on customer returns and feedback to identify common characteristics of customers who are likely to return items, such as purchase history or browsing behavior. By targeting these customers with proactive communications or personalized recommendations, retailers can reduce the likelihood of returns and improve customer satisfaction.
Implementing AI-Powered Return Prediction
So, how can Australian e-commerce businesses implement AI-powered return prediction systems? The first step is to identify the key areas where returns are occurring, such as product descriptions or customer behavior. Next, retailers should gather and analyze data on these areas, using tools such as data analytics and customer feedback. This data can then be used to develop predictive models that identify potential returns, using ML algorithms and other AI technologies. Finally, retailers should implement proactive strategies to prevent returns, such as targeted communications or product improvements.
Key Benefits
The key benefits of AI-powered return prediction include:
- Reduced return rates, resulting in significant cost savings
- Improved customer satisfaction and loyalty, as customers experience fewer issues with their purchases
- Increased efficiency and productivity, as retailers can automate many of the tasks associated with return prediction and prevention
- Improved decision-making, as retailers have access to data-driven insights on customer behavior and purchase history
Next Steps
To get started with AI-powered return prediction, Australian e-commerce businesses should take the following next steps:
- Identify the key areas where returns are occurring, and gather data on these areas
- Develop predictive models using ML algorithms and other AI technologies
- Implement proactive strategies to prevent returns, such as targeted communications or product improvements
- Continuously monitor and evaluate the effectiveness of AI-powered return prediction systems, making adjustments as needed to optimize results.
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Written by Mirai Team
The Mirai team builds AI automation systems for Western SMBs. We write about what we're building, what we're learning, and what's actually working.