The concept of reinforcement learning (RL) has become one of the powerful means to make optimal choices in the setting of software-as-a-service (SaaS). The aim of the suggested work is as follows: to conclude about the best time of customer involved in business-to-business (B2B) SaaS platforms based on an adaptive Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The framework measures the predictiveness of engagement timing based on an examination of interaction patterns within the history, churn and customer lifetime value (CLV) in a reinforcement-driven feedback loop. The simulation process was carried out using an instrument like an episodic environment simulator, a reward-shaping module, and a synthetic interaction generator. This model is trained using 10,000 episodic experiences over 30 features and is reached convergence after 58 episodes and the average gain of rewards is 0.84. The comparative analysis showed the proposed model, PPO, performed better than Q-Learning and DQN with a boost of 13.2% and 9.4 in engagement efficiency and customer retention respectively as compared to the baseline models. As benchmark metrics, there were the values of mean reward, accuracy of engagement timing, and cumulative retention scores used. An engagement frequency normalization and CLV-maximization hybrid re-ward strategy produced a much higher stability in decision-making than did either of the two original strategies. The efficiency of the proposed method was proven by the fact that latency was decreased by 26.5% in comparison with the Q-Learning baseline. The given paper builds the dominance of reinforcement learning structures in time-sensitive interactions in B2B SaaS realities and provides a repeatable framework of simulation and validation in relevant cases. The indicators of performance show high scalability levels of different engagement conditions.