Adaptive real time modeling and scoring
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.
1. An adaptive real time modeling and scoring system for generating scoring models, the system comprising, at least:
a memory that stores instructions; and
one or more processors configured by the instructions to perform operations comprising:
determining, based on a trigger, whether a predictive scoring model is ready for a refresh;
receiving and transforming user-selectable system input data, the user-selectable system input data comprising elements of a plurality of consumer profiles including a plurality of email traffic, display traffic and social media traffic;
monitoring and identifying a change in the input data and, based on an identified change in the input data, automatically refresh the scoring model for calculating new lead scores, the lead scores for leads are values indicating a probability that the leads will make a purchase; and
outputting a refreshed predictive scoring model;
wherein the refreshed predictive model output by the system is used to personalize conversation for in-bound calls at a call center, or optimize the purchase of a marketing media mix on web channels based on evolving quality trends in historical data, or recommend new pricing and strategies for display media bidding based on a difference between original pricing assumptions and a most recent quality and bid performance.
2. The system of claim 1 , wherein the operations further comprise receiving an input defining a consumer profile of an existing target consumer and, based on the received consumer profile, generating a look-alike audience comprising potential target consumers replicating at least in part aspects of the consumer profile.
3. The system of claim 2 , wherein the operations further comprise receiving user selections relating to at least some aspects of the consumer profile with an interactive user interface.
4. The system of claim 3 , wherein the operations further comprise-receiving a selection of a degree of replication accuracy or population size of the look-alike audience with a consumer element of the interactive user interface.
5. The system of claim 3 , wherein the received consumer profile is based at least in part on the user-selectable input data.
6. A method for performing adaptive real time modeling and scoring, the method comprising, at least:
determining, based on a trigger, whether a predictive scoring model is ready for a refresh;
receiving and transforming user-selectable system input data, the user-selectable system input data comprising elements of a plurality of consumer profiles including a plurality of email traffic, display traffic and social media traffic;
monitoring and identifying a change in the input data and, based on an identified change in the input data, automatically refreshing the scoring model for calculating new lead scores;
outputting a refreshed predictive scoring model; and
using the refreshed predictive model to personalize conversation for in-bound calls at a call center, or optimize the purchase of a marketing media mix on web channels based on evolving quality trends in historical data, or recommend new pricing and strategies for display media bidding based on a difference between original pricing assumptions and a most recent quality and bid performance.
7. The method of claim 6 , further comprising receiving an input defining a consumer profile of an existing target consumer and, based on the received consumer profile, generating a look-alike audience comprising potential target consumers replicating at least in part aspects of the consumer profile.
8. The method of claim 7 , further comprising providing a look-alike audience creator, the look-alike audience creator including an interactive user interface for receiving user selections relating to at least some aspects of the consumer profile.
9. The method of claim 8 , further comprising using the interactive user interface to receive a selection of a degree of replication accuracy or population size of the look-alike audience.
10. The method of claim 7 , wherein the received consumer profile is based at least in part on the user-selectable input data.
11. A machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations comprising, at least:
determining, based on a trigger, whether a predictive scoring model is ready for a refresh;
receiving and transforming user-selectable system input data, the user-selectable system input data comprising elements of a plurality of consumer profiles including a plurality of email traffic, display traffic and social media traffic;
monitoring and identifying a change in the input data and, based on an identified change in the input data, automatically refreshing the scoring model for calculating new lead scores, the lead scores for leads are values indicating a probability that the leads will make a purchase;
outputting a refreshed predictive scoring model; and
using the refreshed predictive model to personalize conversation for in-bound calls at a call center, or optimize the purchase of a marketing media mix on web channels based on evolving quality trends in historical data, or recommend new pricing and strategies for display media bidding based on a difference between original pricing assumptions and a most recent quality and bid performance.
12. The medium of claim 11 , wherein the operations further comprise receiving an input defining a consumer profile of an existing target consumer and, based on the received consumer profile, generating a look-alike audience comprising potential target consumer replicating at least in part aspects of the consumer profile.
13. The medium of claim 12 , wherein the operations further comprise providing a look-alike audience creator, the look-alike audience creator including an interactive user interface for receiving user selections relating to at least some aspects of the consumer profile.
14. The medium of claim 13 , wherein the operations further comprise using the interactive user interface to receive a selection of a degree of replication accuracy or population size of the look-alike audience.
15. The medium of claim 12 , wherein the received consumer profile is based at least in part on the user-selectable input data.
16. The method of claim 6 , wherein the trigger is a forced trigger.
17. The method of claim 6 , wherein the trigger is an automated trigger that is based on analysis of historical data and new data that reveals a new relationship between different variables that did not exist previously.