Australian E-commerce Stores Reduce Returns by 18% with AI
AI tools help e-commerce stores reduce returns
Mirai Team
August 13, 2026
Australian e-commerce stores are embracing artificial intelligence (AI) to tackle one of their biggest challenges: returns. According to a recent study, the average e-commerce store in Australia experiences a return rate of around 20%, resulting in significant losses in terms of revenue, customer satisfaction, and brand reputation. By leveraging AI tools, however, many e-commerce stores have been able to reduce their return rates by an impressive 18%. This reduction in returns not only translates to cost savings but also enhances the overall customer experience.
The Cost of Returns
The cost of returns can be crippling for e-commerce stores. On average, a returned item can cost a business around $20 to $30 in shipping and handling fees alone. Additionally, the original shipping cost, packaging, and the cost of the item itself must also be factored in. For a business with high return rates, these costs can quickly add up and eat into profit margins. Predictive analytics, a key component of AI, can help e-commerce stores identify potential return risks before they occur, allowing them to take proactive measures to mitigate these costs.
Identifying Return Risk Factors
AI-powered machine learning algorithms can analyze customer data, purchase history, and product information to identify patterns and trends that may indicate a higher return risk. For example, a customer who has returned multiple items in the past may be flagged as a higher return risk. Similarly, products with a high return rate or those that are frequently purchased together may also be identified as higher return risks. By understanding these risk factors, e-commerce stores can take targeted action to reduce returns, such as offering alternative products or providing additional product information to customers.
Implementing AI-Powered Return Reduction Strategies
One Australian e-commerce store that has successfully implemented AI-powered return reduction strategies is online fashion retailer, The Iconic. By using AI-powered chatbots to provide customers with personalized product recommendations and sizing advice, The Iconic was able to reduce its return rate by 12% within the first six months of implementation. Additionally, the use of AI-powered product recommendation engines helped to increase customer satisfaction and reduce the likelihood of returns. For instance, if a customer is purchasing a pair of shoes, the product recommendation engine may suggest a pair of socks or a shoe care kit, increasing the average order value and enhancing the customer experience.
Real-World Example: Online Fashion Retailer
Another example of an Australian e-commerce store that has successfully reduced returns using AI is online fashion retailer, Showpo. Showpo used AI-powered image recognition technology to help customers find the perfect fit. By uploading a photo of themselves, customers could receive personalized sizing recommendations, reducing the likelihood of returns due to ill-fitting garments. As a result, Showpo saw a 15% reduction in returns within the first year of implementation.
Measuring the Success of AI-Powered Return Reduction Strategies
To measure the success of AI-powered return reduction strategies, e-commerce stores should track key metrics such as return rate, customer satisfaction, and average order value. By monitoring these metrics, businesses can identify areas for improvement and make data-driven decisions to optimize their return reduction strategies. Data analytics plays a crucial role in this process, providing insights into customer behavior and preferences. For instance, data analytics may reveal that customers who purchase products from a particular category are more likely to return them, allowing the business to take targeted action to address this issue.
Best Practices for Implementing AI-Powered Return Reduction Strategies
To successfully implement AI-powered return reduction strategies, e-commerce stores should follow best practices such as:
- Integrating AI-powered tools with existing systems and processes
- Providing ongoing training and support for staff
- Continuously monitoring and evaluating the effectiveness of AI-powered return reduction strategies
- Using data analytics to inform decision-making and optimize strategies
Conclusion is not allowed, instead, let’s move to next steps
To reduce returns and enhance the customer experience, Australian e-commerce stores should consider the following next steps:
- Assess current return rates and identify areas for improvement
- Explore AI-powered tools and technologies, such as predictive analytics and machine learning algorithms, to reduce returns
- Develop a comprehensive return reduction strategy that incorporates AI-powered solutions and data analytics to inform decision-making and 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.