Abstract: The present disclosure describes a system, method, and computer program for automatically predicting the emotion(s) to which a user is most likely to respond based on user characteristics and for tailoring a marketing message to a user based on the predicted emotion(s). A statistical model is created that predicts how a user with certain characteristics will respond to certain emotions. User characteristics are the input to the model, and, for each of a set of emotions, the output is the probability of a corresponding user responding to a message with the emotion. The statistical model is used to generate promotional messages that are tailored to each user based on the emotion to which the user is predicted to respond best, given the user's characteristics.

Abstract: The disclosure is directed to a system, method, and computer program product for detecting and responding to changes in the performance of an ad. In a first phase, an expected performance value for an ad is calculated. In a second phase, the performance of the ad is monitored in intervals, wherein for each interval, the response rate of the ad during the interval is calculated and compared to the expected performance value, an upper performance limit, and a lower performance limit. The ad is replaced or the expected performance value is recalculated, depending on how the response rate compares to the expected performance value, the upper performance limit, and the lower performance limit.

Abstract: A system and method is provided for recommending a grammar for a message campaign used by a message optimization system. A user specifies parameters for a new campaign, from which a set of statistical design budgets is calculated. The user selects a grammar structure, recommended based on the statistical design budgets, for the campaign. The n-most relevant past campaigns are identified. Semantic tags, associated with each previously used value from the n-most relevant past campaigns and each of a plurality of untested values, are identified and ranked based on past performance. The previously used values are ordered by ranked tag group and then within each tag group, while the untested values are ordered by ranked tag group and then randomly within the tag group. Recommended values are selected from the ranked list of previously used values and untested values depending on the degree of exploration/conservatism indicated by the user.

Abstract: The present invention provides a system and method for optimizing a message. Components of a starting message are identified, and at least one rule is applied for modifying at least one message component to create at least one variation of the starting message. Message variants are tested by sending each variant to a sample of people and measuring a response rate for each sent message variant. The measured response rates are used to create an optimal version of the message. In one embodiment, message variants may be created and tested in multiple rounds.

Abstract: The present invention provides a system and method for optimizing a message. Components of a starting message are identified, and at least one rule is applied for modifying at least one message component to create at least one variation of the starting message. Message variants are tested by sending each variant to a sample of people and measuring a response rate for each sent message variant. The measured response rates are used to create an optimal version of the message. In one embodiment, message variants may be created and tested in multiple rounds.

Abstract: A system, method, and computer program are provided for identifying message content to send to users based on the users' language characteristics. Language characteristics are extracted from user-generated content and language characteristic scores are assigned to each user. The users are clustered into groups using the language characteristic scores. The system sends test messages with different message content to at least a subset of each group's users and the response rates are measured. For each group, a message content to which the group is most responsive is identified and is associated with the group. Language characteristics from a new user's user-generated content are extracted and language characteristic scores are assigned to the new user. The group to which the new user belongs is identified using the new user's language characteristic scores. A message is sent to the new user with the message content previously associated with the identified group.