Purpose – This paper aims to evaluate the performance of the consolidation process of a product recently included commercially in a portfolio through a sales forecasting model that, focused on several segments of the customer portfolio, supports the commercial decision-making. Design/methodology/approach – This approach uses the ABC Curve methodology to define the analysis in segments of relevance and then integrates two methods: (i) the Markovian models in discrete time and annual step to predict the transition behavior between the new replacement product and the consolidated ones; (ii) first-order and second-order exponential smoothing time series forecasting method to predict products in aggregate demand. This model was applied to a seed distributor company based on its customer portfolio and historical data for sales between 2011 and 2019. Findings – The sales forecasting benchmark scenario, designed for stable conditions in the behavioral adherence of new products and the commercial strategy, resulted in a quantitative support for targeting and monitoring commercial efforts to maximize the global performance for this process. Originality/value – Besides presenting a new Markov Chains commercial management approach, the model developed introduces a quantitative tool into the literature for targeting the customer portfolio management processes in the context of replacement in a product portfolio. Keywords - Curve ABC. Markov Chains. Sales Forecasting. Exponential Smoothing. Customer Portfolio Management.