To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity. The importance values associated with the key individuals informs how to efficiently allocate resources to each individual interaction.