Telemarketing towards merchants is considerably more complex than traditional dialogue systems. Given a user utterance, the response must not only follow the context but also strategically and naturally guide the conversation toward marketing objectives. A common approach is to fine-tune LLMs using high-quality dialogue data from top sales. However, we find that even after careful data cleaning, these data cannot be used directly due to two issues: (1) Poor strategy-following: Real-world conversations are highly random with much chit-chat topics, easily leading deviation from intended strategy. (2) Insufficient expert knowledge learning: Expert knowledge appears infrequently or not at all in limited collected corpus. To this end, we introduce a hybrid data synthesis framework with two main innovations. First, we unify the input schema with profile and strategy designed by top sales, and extract them via a Multi-task paradigm. In addition, we propose Role-playing Simulation and Session Prefix Completion to complementarily improve the strategy-following and inject long-tail expert knowledge into our fine-tuned model -- TeleBot. Comprehensive online and offline evaluations demonstrate its effectiveness. In particular, in terms of the final marketing results -- High Intention Rate, TeleBot reaches the performance level of the top 25% of human sales.