Abstract: The diversity of job titles prevents the extraction of information from job titles to be automated and scalable. Accordingly, disclosed embodiments utilize a machine-learning named-entity recognition model to tokenize job titles, and tag those tokens according to one or more named entities, such as job responsibility and job function. The named entities may be extracted from the tagged tokens to be used as an input to a persona model that predicts a persona score, indicating the relative importance of a person to a sales opportunity. This enables granular information to be automatically extracted from job titles, regardless of structure, style, or format, without continual retraining and in a scalable manner, for use in one or more downstream functions.

Abstract: The determination of an account outcome, such as a renewal, upsell, or cross-sell, for a product subscription is not presently capable of automation or scaling. Accordingly, disclosed embodiments automate this determination, in a scalable manner, using a trained predictive machine-learning model. In particular, values for a set of features of a customer account are extracted and input to a predictive machine-learning model to identify the probability of an account outcome. This probability may then be used in one or more downstream functions, such as for reports, alerts, account segmentation, marketing orchestration, sales intelligence, determining a next best action, and/or the like.

Abstract: AI-based orchestration. In an embodiment, a recommendation engine is applied to a data pipeline representing company accounts. Engagement metric(s) are calculated based on activity data associated with the company accounts, and predictive model(s) are applied to the activity data and/or firmographic data associated with the company accounts to generate predictive output. A tactic recommendation model is applied to orchestration features, comprising the engagement metric(s) and predictive output, to generate recommended tactic(s). In addition, a contact recommendation model is applied to contact data to generate recommended contact(s). The recommended tactic(s) are combined with the recommend contact(s) to generate an orchestration, comprising recommended action(s), to be executed.