Retourenverhinderung durch gezielte Rabatte: Entwicklung eines KI-basierten Prototyps mit Low-Code-Technologie für den Kundendienst

This case study outlines the development of a prototype designed to minimise consumer returns by supporting customer service agents in offering tailored discounts that shall incentivise the customers to retain their purchases. The prototype is based on an AI model, namely Case-Based Reasoning, integrated within a user-friendly web application to enhance usability. Utilising the low-code technology myCBR had enabled a rapid development, while still maintaining a low-cost implementation. The effectiveness of the prototype was validated under authentic conditions by customer service agents from a cooperating German retailer for furniture and home accessories. The results confirm that the prototype systematically uses existing customer service data to create sufficient discount offers that are both efficient and cost-effective. By implementing data-driven discount strategies, the processing of customer enquiries is optimised and unnecessary returns are proactively avoided. As a result, the prototype’s approach can contribute significantly to sustainability, cost reduction, and conservation of resources.