Returns - a real headache for the e-commerce industry. Did you know that 12 to 15% of all shipped packages are returned? This costs significant time and money; a whopping €12,50 per return. Fortunately, AI offers advanced solutions to drastically reduce this flow of returns. Starting today. By bridging the gap between customer expectations and reality, retailers can improve their operational efficiency and support sustainable practices. In this article, we’ll give you five innovative AI strategies that can help solve return issues.
1. AI and Augmented Reality (AR) for reducing returns
Online shopping comes with its challenges, particularly the inability to physically experience products. This can lead to uncertainty and more returns. AI and augmented reality (AR) provide a smart solution to this problem.
With AR, customers can see how a product fits into their environment. The augmented reality market was valued at $40.12 billion in 2022 and is expected to grow to a massive $1.19 trillion by 2032. This explosive growth shows why it’s worth investing in this technology for marketers and platforms alike. For example, IKEA already uses AR successfully, allowing customers to virtually place furniture in their homes to see how it fits with their interior design.
This visual support helps prevent disappointments and significantly reduces the number of returns. When customers can see exactly what they’re getting, they feel more confident about their purchase. This leads to fewer surprises upon delivery and less need to return products. The result? Higher customer satisfaction and lower costs for the retailer.
2. AI-Powered Product Descriptions and Recommendations
One of the most powerful applications of generative AI in e-commerce is creating product descriptions tailored to each customer’s unique preferences. By understanding browsing history, purchasing behaviour, and personal preferences, AI can generate dynamic product descriptions that perfectly match what a customer is looking for. And it doesn’t stop there. This collected data can also be used to make targeted product recommendations, making the online shopping experience even more personalised and relevant.
Consider a customer who regularly buys decorative cushions and has a clear preference for grey tones. AI recognises this preference and automatically adjusts the description of a new cushion to perfectly suit the customer’s style. This personalised approach makes customers feel understood and confident in their purchase. The result? More satisfied customers and a higher conversion rate.
3. AI-Driven Customer Reviews and Feedback Analysis
AI goes beyond just personalising product descriptions and recommendations; it’s also a powerful tool for analysing customer reviews. AI can give retailers the ability to analyse reviews and uncover hidden patterns and trends. These insights can then be used to resolve common issues before they lead to returns.
For example, if customers regularly mention that a wardrobe looks lighter on the website than in reality, AI can spot this pattern. The solution? Adjusting the product photos so they better reflect the true colour. This small detail can make a big difference. By quickly responding to customer feedback, retailers can prevent disappointments and improve the shopping experience.
4. Personalisation with AI and Customer Profiles
AI is transforming the way retailers understand and respond to customer behaviour. Rather than simply reacting to purchases, AI can uncover deep patterns in purchase history, browsing behaviour, and customer service interactions. These insights enable retailers not only to anticipate what a customer might want but also to prevent problems before they arise.
For example, if a customer frequently returns shoes due to a poor fit, AI can recognise this recurring frustration. Instead of waiting for the next customer to be disappointed again, the system offers alternative recommendations that better suit their preferences. Additionally, AI can provide warnings about specific product features, such as fit or material. For instance: “This product runs large; we recommend ordering one size down.”
5. AI in Customer Service and Chatbots
AI is changing the way customers are supported during the purchasing process. When someone has questions about a product, they must get quick and clear answers. This prevents uncertainties and ensures that expectations are clear. AI-driven chatbots offer a powerful solution here.
When a customer asks about the delivery time of their package, they can expect an answer within seconds from an AI-driven chatbot. A chatbot can provide customers with the exact information they need, whether it’s product comparisons or specific details. This leads to fewer returns and a smoother, more efficient returns process.
This approach also works excellently in the retail sector. With AI support, customers can be helped directly. This results in fewer returns and a more efficient returns process. Ultimately, AI helps retailers improve their shopping experience and build a strong relationship with their customers.
Conclusion
An efficient returns process is vital for profitability and customer satisfaction in e-commerce. Want to know how Emixa can help you with this? Get in touch with us today!
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