… that can model the conversational dynamics of user and agent social behaviours. To train models in the role of customer service agents, we need to capture the various nuances of their speech patterns, which often combine emotional support and problem-solving skills. We design a multi-label approach, modelling the behaviours expressed in one speaker as a chronologically ordered sequence of socio-emotional labels, including emotions and conversational actions. We propose a two-module architecture. The planning module uses the dialogue’s history to generate the best sequence of social and emotional strategies for the response. The generative module conditions the response generation according to the predicted sequence of labels to improve the quality of the final answer while allowing for transparency and controllability. To efficiently adapt the system to our use case, we require adequate datasets; however, no publicly available corpus meets all our criteria, including multi-label annotations and French task-oriented conversations. To address this, we introduce DATA-SERGE, a new corpus composed of French customer service conversations, annotated following a comprehensive multi-label scheme. Additionally, we observe that existing automatic metrics are lacking to properly evaluate such a system. We present a human evaluation protocol and new scores to fill this gap. We evaluate this architecture on both DailyDialog, an English open-domain dataset, and DATA-SERGe. The findings support our primary research hypothesis and demonstrate that, even in highly domain-specific contexts like French customer service data, planning sequences of labels has a positive impact on response generation.