Zeta Global Corp.

Other

Bharat Goyal

Pavan Korada

Sunpreet Singh Khanuja

Weiwei Zhang

Yun Sam Chong

Edward Robert Rau, Jr

Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

Adaptive real time modeling and scoring

Various examples are directed to systems and methods for adaptively generating leads. A marketing system may determine that a first lead score for a first lead is greater than a first lead score threshold and determine that a second lead score for a second lead is less than the first lead score threshold. The marketing system may generate a set of filtered leads including the first lead information from the first lead. The marketing system may determine a scrub rate that describes a portion of first execution cycle data having lead scores greater than the first lead score threshold and determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold. The marketing system may select a second lead score threshold that is lower than the first lead score threshold.

Adaptive lead generation for marketing

A computer implemented method, comprising: selecting a plurality of dynamic models for evaluating a scoring request, wherein the dynamic models are stored on a scoring database; deploying each of the dynamic models to one of a plurality of evaluators; synchronizing the dynamic models, the synchronizing including at least determining a same version of the dynamic models is deployed and available to all evaluators; receiving, at a scoring node, a scoring request for a score of a lead from at least one requester; separating the scoring request into a plurality of scoring requests, wherein each of the scoring requests is assigned to one of the selected dynamic models wherein the request is separated by model and aggregate model results and sent to an evaluator queue; combining results from each of the dynamic models; evaluating the combined results wherein each of the evaluators sends a model evaluation response to the response queue; and providing a response to the scoring request based on the evaluation of the combined results.

Predictive modeling and analytics integration platform

Certain embodiments may generally relate to systems and methods for rapidly deploying predictive scoring models on a platform that allows for easy integration. A method may include selecting one or more dynamic models for evaluating a scoring request. The method may also include receiving, at a scoring node, a scoring request from at least one requester. The method may further include separating the scoring request into one or more scoring requests, in which each of the one or more score requests may assigned to each of the selected one or more dynamic models. The method may also include combining results from each of the one or more dynamic models. The method may further include evaluating the combined results. The method may also include providing a response to the scoring request based on the evaluation of the combined results.

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San Jose, CA, United States of America

Bharat Goyal