Abstract: A system and method are presented for predicting workload demand in a customer journey application. Using historical information from journey analytics, journey moments can be aggregated through various stages. Probability-distribution-vectors can be approximated for various paths connected the stages. Stability of such probability distribution can be determined through statistical methods. Predictions for future volumes progressing through the stages can be determined through recursive algorithms after applying a time-series forecasting algorithm at the originating stage(s). Once future volumes have been forecasted at every stage, future workload can be estimated to better capacity planning and scheduling of resources to handle such demand to achieve performance metrics along the cost function.