Persado Intellectual Property Limited

Guy Stephane Krief

Assaf Baciu

Alexios Vratskides

Ori Einstein

Avishalom Shalit

Rui Miguel Forte

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.

System and method for recommending a grammar for a message campaign used by a message optimization system

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.

Message optimization

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.

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Papagou, Greece

Guy Stephane Krief