REAL-TIME MARKETING CAMPAIGN STIMULI SELECTION BASED ON USER RESPONSE PREDICTIONS

• US 20160210657A1
• Filed: 12/17/2015
• Published: 07/21/2016
• Est. Priority Date: 12/30/2014
• Status: Abandoned Application

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Chittilappilly, Anto, Sadegh, Payman, Jose, Darius
• Application Number:
  US14/972,801
• Publication Number:
  US 20160210657A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0246 Traffic

Abstract

A method, system, and computer program product for media spend management using real-time marketing campaign stimuli selection based on user response predictions. Embodiments commence upon identifying one or more users comprising an audience for one or more marketing campaigns. Observed touchpoint data records are collected based on audience responses to campaign stimuli. A collection of historical touchpoint data records are used to form a predictive model that captures relationships between the stimuli and the responses. At any moment in time, such as when a particular user is online, the predictive model is used to predict one or more next desired touchpoints based on a particular user'"'"'s then-current online interactions. Marketing campaign stimuli that has a known historical effectiveness with respect to the desired touchpoints is reported. A marketing manager can increase the prevalence of such effective stimuli so as to increase the likelihood of desired responses by the particular user.

Claims

1. A computer implemented method comprising:

storing in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users;

identifying an audience segment of users comprising a subset of the users;

sorting data for the touchpoint encounters in the computer to separate into converting user data, which comprises touchpoint encounters for the users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for the users that exhibited a negative response to the marketing message;

retrieving, from storage, the converting user data and the non-converting user data;

training, using machine-learning techniques in a computer, the converting user data and the non-converting user data as training data to generate a touchpoint response predictive model that defines a plurality of sets of touchpoint encounters that reflect a positive response to the marketing message;

receiving at least one user interaction data record corresponding to a detected online user touchpoint encounter associated with a user of the audience segment of users for presentation of one or more marketing campaigns;

predicting, using the touchpoint response predictive model, and responsive to the user interaction data record, at least one touchpoint encounter from a set of touchpoint encounters defined for the audience segment of users; and

determining one or more selected stimuli parameters for the user of the audience segment of users based on the predicted touchpoint to effectuate the marketing campaigns.

2. The computer implemented method of claim 1, further comprising generating a spending amount based at least in part on the selected user stimuli parameters.

3. The computer implemented method of claim 1, further comprising generating one or more user propensity scores that are based at least in part on the user interaction data record.

4. The computer implemented method of claim 3, wherein determining the user propensity scores is based at least in part on one or more predicted responses generated by applying a user interaction sequence to the touchpoint response predictive model.

5. The computer implemented method of claim 3, wherein determining the selected user stimuli parameters is further based on a difference between the user propensity scores and one or more thresholds.

6. The computer implemented method of claim 1, wherein the user interaction data record comprises cookie information associated with a particular subject user.

7. The computer implemented method of claim 6, wherein the selected user stimuli parameters characterize one or more touchpoints to be presented to the particular subject user.

8. The computer implemented method of claim 6, further comprising:

identifying one or more campaign execution providers to receive the selected user stimuli parameters for presenting a set of selected user stimuli to the particular subject user; and

delivering the selected user stimuli parameters to the campaign execution providers.

9. The computer implemented method of claim 1, further comprising:

providing a media planning application to at least one application user for operation on at least one management interface device; and

delivering the selected user stimuli parameters to the media planning application for presentation to the application user.

10. The computer implemented method of claim 1, further comprising:

emitting a recommendation to increase a frequency of occurrences of stimuli corresponding to the detected online user touchpoint encounter.

11. A computer readable medium, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts, the acts comprising:

storing in a computer, a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users;

identifying an audience segment of users comprising a subset of the users;

sorting data for the touchpoint encounters in the computer to separate into converting user data, which comprises touchpoint encounters for the users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for the users that exhibited a negative response to the marketing message;

retrieving, from storage, the converting user data and the non-converting user data;

training, using machine-learning techniques in a computer, the converting user data and the non-converting user data as training data to generate a touchpoint response predictive model that defines a plurality of sets of touchpoint encounters that reflect a positive response to the marketing message;

receiving at least one user interaction data record corresponding to a detected online user touchpoint encounter associated with a user of the audience segment of users for presentation of one or more marketing campaigns;

predicting, using the touchpoint response predictive model, and responsive to the user interaction data record, at least one touchpoint encounter from a set of touchpoint encounters defined for the audience segment of users; and

determining one or more selected stimuli parameters for the user of the audience segment of users based on the predicted touchpoint to effectuate the marketing campaigns.

12. The computer readable medium of claim 11, further comprising instructions which, when stored in memory and executed, causes the processor to perform generating a spending amount based at least in part on the selected user stimuli parameters.

13. The computer readable medium of claim 11, further comprising instructions which, when stored in memory and executed, causes the processor to perform generating one or more user propensity scores that are based at least in part on the user interaction data record.

14. The computer readable medium of claim 13, wherein determining the user propensity scores is based at least in part on one or more predicted responses generated by applying a user interaction sequence to the touchpoint response predictive model.

15. The computer readable medium of claim 13, wherein determining the selected user stimuli parameters is further based on a difference between the user propensity scores and one or more thresholds.

16. The computer readable medium of claim 11, wherein the user interaction data record comprises cookie information associated with a particular subject user.

17. The computer readable medium of claim 16, wherein the selected user stimuli parameters characterize one or more touchpoints to be presented to the particular subject user.

18. The computer readable medium of claim 11, further comprising instructions which, when stored in memory and executed, causes the processor to perform emitting a recommendation to increase a frequency of occurrences of stimuli corresponding to the detected online user touchpoint encounter. 19, A system comprising:

a storage device to store a plurality of touchpoint encounters that represent marketing messages exposed to a plurality of users; and

a processor for executing instructions which, when stored in a memory and executed by the processor causes the processor to perform,

identifying an audience segment of users comprising a subset of the users;

sorting data for the touchpoint encounters into separate into converting user data, which comprises touchpoint encounters for the users that exhibited a positive response to the marketing message, and non-converting user data that comprises touchpoint encounters for the users that exhibited a negative response to the marketing message;

retrieving, from the storage device, the converting user data and the non-converting user data;

training, using machine-learning techniques, the converting user data and the non-converting user data as training data to generate a touchpoint response predictive model that defines a plurality of sets of touchpoint encounters that reflect a positive response to the marketing message;

receiving at least one user interaction data record corresponding to a detected online user touchpoint encounter associated with a user of the audience segment of users for presentation of one or more marketing campaigns;

predicting, using the touchpoint response predictive model, and responsive to the user interaction data record, at least one touchpoint encounter from a set of touchpoint encounters defined for the audience segment of users; and

determining one or more selected stimuli parameters for the user of the audience segment of users based on the predicted touchpoint to effectuate the marketing campaigns.

20. The system of claim 19 wherein the user interaction data record comprises cookie information associated with a particular subject user.