Australian E-commerce Businesses Reduce Returns by 18% with AI
AI workflows for e-commerce return reduction
Mirai Team
July 6, 2026
Australian e-commerce businesses face a significant challenge in managing returns, with the average return rate ranging from 15% to 20%. This can be costly, not only in terms of the direct expense of processing returns but also in terms of the indirect costs, such as damage to brand reputation and decreased customer loyalty. Artificial intelligence (AI) can help reduce returns by improving the order fulfillment process and enhancing customer experience. By leveraging AI, Australian e-commerce businesses can minimize returns and maximize revenue.
Understanding the Return Problem
The return process is complex and time-consuming, involving multiple stakeholders, including customers, customer service teams, and logistics providers. A single return can involve up to 10 different steps, from initiating the return request to processing the refund. According to a study, the average cost of processing a return is around $20, which can add up quickly, especially for businesses with high return rates. E-commerce platforms can help streamline the return process, but they often lack the intelligence and automation needed to prevent returns from happening in the first place.
The Root Causes of Returns
Returns are often caused by inaccurate product descriptions, poor sizing, and damaged or defective products. These issues can be addressed by implementing AI-powered product information management (PIM) systems, which can help ensure that product descriptions are accurate and up-to-date. Additionally, **AI-driven size recommendation engines can help reduce sizing issues by providing customers with personalized size recommendations. By addressing these root causes, e-commerce businesses can reduce returns and improve customer satisfaction.
Implementing AI Workflows for Return Reduction
To reduce returns, e-commerce businesses can implement AI workflows that automate and optimize the order fulfillment process. For example, AI-powered chatbots can be used to provide customers with real-time support and guidance, helping to prevent returns by addressing customer inquiries and concerns proactively. Another example is the use of machine learning algorithms to analyze customer behavior and preferences, enabling businesses to personalize the shopping experience and reduce the likelihood of returns. A case study by an Australian fashion retailer found that implementing an AI-powered chatbot reduced returns by 12% within the first six months.
Practical Example: Personalized Size Recommendations
A great example of an AI workflow for return reduction is the use of personalized size recommendation engines. These engines use machine learning algorithms to analyze customer data, such as purchase history and body measurements, to provide personalized size recommendations. For instance, a customer shopping for a dress on an e-commerce website can be prompted to enter their measurements, and the AI engine can recommend the best size based on their input. This not only improves the customer experience but also reduces the likelihood of returns due to sizing issues. According to a study, personalized size recommendations can reduce returns by up to 25%.
Measuring the Impact of AI on Return Reduction
To measure the impact of AI on return reduction, e-commerce businesses can track key metrics, such as return rate, customer satisfaction, and revenue growth. By monitoring these metrics, businesses can evaluate the effectiveness of their AI workflows and make data-driven decisions to optimize their return reduction strategies. For example, a business can use AI-powered analytics to identify trends and patterns in customer behavior and adjust their marketing and sales strategies accordingly.
Real-World Results
Australian e-commerce businesses that have implemented AI workflows for return reduction have seen significant results. For example, a study found that businesses that used AI-powered chatbots to provide customer support saw a 15% reduction in returns within the first year. Another study found that businesses that implemented AI-powered size recommendation engines saw a 20% reduction in returns due to sizing issues. These results demonstrate the potential of AI to reduce returns and improve customer satisfaction in the e-commerce industry.
Next Steps
To start reducing returns with AI, Australian e-commerce businesses can take the following steps:
- Assess their current return process to identify areas for improvement and opportunities to implement AI workflows
- Explore AI-powered solutions, such as chatbots and size recommendation engines, to automate and optimize the order fulfillment process
- Monitor and evaluate the impact of AI on return reduction, using key metrics such as return rate and customer satisfaction to inform future strategies
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.
Explore related services:
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.