Australian E-commerce Sites Saving 15% on Returns with AI
15% fewer returns — Australian e-commerce businesses using AI to flag high-risk orders, automate return workflows, and reduce refund processing time.
Mirai Team
May 18, 2026
Australian e-commerce sites are facing a major challenge: return rates. On average, 10% of products sold online are returned, resulting in significant losses for businesses. However, some companies are now using AI workflows to reduce returns and save thousands of dollars. For example, a Sydney-based online retailer implemented an AI-powered return prediction system, which helped them reduce their return rate by 15% and save over $200,000 per year.
Reducing Returns with AI
To understand how AI can help reduce returns, it’s essential to look at the return process. Most e-commerce sites have a manual return process, which involves customers contacting the company, explaining the reason for the return, and waiting for a response. This process can be time-consuming and often leads to customer frustration. AI-powered workflows can automate this process, making it faster and more efficient. For instance, AI-powered chatbots can help customers initiate the return process, provide them with return shipping labels, and update them on the status of their return.
Identifying Return Patterns
One of the key ways AI can help reduce returns is by identifying return patterns. By analyzing customer data and purchase history, AI algorithms can identify products that are more likely to be returned. For example, a company may find that a particular product is being returned frequently due to size issues. The company can then take steps to address this issue, such as providing more detailed size charts or offering free returns for size-related issues. This can help reduce the number of returns and save the company money.
Implementing AI-Powered Return Workflows
Implementing AI-powered return workflows can be a complex process, but it can also be highly beneficial. For example, an online retailer can use machine learning algorithms to analyze customer data and predict which customers are likely to return a product. The company can then take proactive steps to prevent the return, such as offering a discount or a free gift. Additionally, AI-powered workflows can help automate the return process, making it faster and more efficient. This can help improve customer satisfaction and reduce the number of returns.
A great example of a company that has successfully implemented AI-powered return workflows is an Australian fashion retailer. The company used AI-powered chatbots to help customers initiate the return process and provide them with return shipping labels. The company also used machine learning algorithms to analyze customer data and predict which customers were likely to return a product. As a result, the company was able to reduce its return rate by 12% and save over $150,000 per year.
Measuring the Success of AI-Powered Return Workflows
To measure the success of AI-powered return workflows, companies need to track key performance indicators (KPIs) such as return rate, customer satisfaction, and cost savings. For example, a company may track the number of returns per month and compare it to the number of returns per month before implementing AI-powered return workflows. The company can also track customer satisfaction by monitoring customer feedback and social media reviews. By tracking these KPIs, companies can determine whether their AI-powered return workflows are effective and make adjustments as needed.
Some of the benefits of using AI-powered return workflows include:
- Reduced return rates
- Improved customer satisfaction
- Increased efficiency
- Cost savings
- Improved customer insights
To achieve these benefits, companies need to have a clear understanding of their return process and identify areas where AI-powered workflows can be implemented. They also need to have access to high-quality customer data and the technical expertise to implement AI-powered workflows.
Best Practices for Implementing AI-Powered Return Workflows
To implement AI-powered return workflows effectively, companies need to follow best practices such as:
- Starting with a clear understanding of the return process and identifying areas where AI-powered workflows can be implemented
- Having access to high-quality customer data
- Using machine learning algorithms to analyze customer data and predict return patterns
- Implementing AI-powered chatbots to help customers initiate the return process
- Continuously monitoring and evaluating the effectiveness of AI-powered return workflows
By following these best practices, companies can ensure that their AI-powered return workflows are effective and help reduce return rates. For example, a company can start by analyzing its return data to identify patterns and trends. The company can then use this information to implement AI-powered workflows that address these patterns and trends.
Next steps to save 15% on returns with AI:
- Conduct an audit of your current return process to identify areas where AI-powered workflows can be implemented
- Invest in AI-powered return prediction software to analyze customer data and predict return patterns
- Implement AI-powered chatbots to help customers initiate the return process and provide them with return shipping labels
- Continuously monitor and evaluate the effectiveness of your AI-powered return workflows to ensure they are reducing return rates and improving customer satisfaction.
Ready to implement this in your business? Mirai deploys AI automation for SMBs across the US, UK, Canada, and Australia — typically in under a week.
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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.