Seasonal time series depicts a variety of demand patterns due to the trend and seasonality effects. Short lifecycles and peak demand are the complex characteristics of most seasonal products. This study presents a multiplicative exponential smoothing technique and introduces an additional seasonal parameter in the quest for better peak season forecast. Considering an actual time series of a seasonal product, the study analyzes four possible combinations of additive and multiplicative forms to feature the trend and strong seasonality of the data. The model uses the appropriate combination of additive and multiplicative forms and introduces an additional smoothing parameter to represent the time series pattern. The technique is relatively easy and computationally stable. The test result indicates the model performance is robust and encouraging for researchers and practitioners to exercise customized model in predicting strong seasonal demand.