  Recent research in dialogue systems and corpora has focused on two main
categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD
systems help users accomplish specific tasks, while open-domain systems aim to
create engaging conversations. However, in real-world scenarios, user intents
are often revealed during interactions. A recent study introduced SalesBot,
which simulates dialogues transitioning from chit-chat to task-oriented
scenarios to train sales agents. Unfortunately, the initial data lacked smooth
transitions and coherent long-turn dialogues, resulting in poor naturalness in
sales-customer interactions. To address these issues, this paper presents
SalesBot 2.0, an improved dataset. It leverages commonsense knowledge from
large language models (LLMs) through strategic prompting. Additionally, we
introduce a novel model called SalesAgent, trained on salesperson's
interactions, using chain-of-thought (CoT) reasoning. This model excels in
transitioning topics, understanding user intents, and selecting appropriate
strategies. Experiments using diverse user simulations validate the
effectiveness of our method in controlling dialogue strategies in LLMs.
Furthermore, SalesBot 2.0 enhances coherence and reduces aggression,
facilitating better model learning for sales-customer interactions.
