REAL-TIME MARKETING CAMPAIGN STIMULI SELECTION BASED ON USER RESPONSE PREDICTIONS
First Claim
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.
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Accused Products
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.
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Citations
20 Claims
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1. A computer implemented method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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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:
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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. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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20. The system of claim 19 wherein the user interaction data record comprises cookie information associated with a particular subject user.
Specification