Human-machine collaboration in online customer service – a long-term feedback-based approach

The rising expectations of customers have considerably contributed to the need for automated approaches supporting employees in online customer service. Since automated approaches still struggle to meet the challenge to fully grasp the semantics of texts, hybrid approaches combining the complementary strengths of human and artificial intelligence show great potential for assisting employees. While research in Case-Based Reasoning (CBR) already provides well-established approaches, they do not fully exploit the potential of CBR as hybrid intelligence. Against this background, we follow a design-oriented approach and develop an adapted textual CBR cycle that integrates employees’ feedback on semantic similarity, which is collected during the Reuse phase, into the Retrieve phase by means of long-term feedback methods from information retrieval. Using a real-world data set, we demonstrate the practical applicability and evaluate our approach regarding performance in online customer service. Our novel approach surpasses human-based, machine-based, and hybrid approaches in terms of effectiveness due to a refined retrieval of semantically similar customer problems. It is further favorable regarding efficiency, reducing the average time required to solve a customer problem.