Abstract: A firm’s ability to compete in new product markets is vital its profitability and long-term survival. Therefore, it is important to forecast accurately the Marketing diffusion of new products in designing Marketing strategies for new product planning and management. The paper introduce a new estimation procedure, augmented kalman Filter with continuous state and Discrete observations(AKF(C-D), for estimating the state space Models of the diffusion process of a new product. This method is directly applicable to differential diffusion models without imposing constraints on the model structure or the nature of the unknown parameters. The authors compare AKF(C-D) empirically with three other estimation procedures, demonstrating AKF(C-D)’s superior prediction performance.