3results about How to "Precision marketing" patented technology

The application discloses a multimedia publicity page promotion method and system, and technical scheme points are as follows: a multimedia publicity page promotion method comprises the following steps: S1, performing promotion analysis: including analyzing publicity purposes and audience groups, determining a network public activity theme, and selecting a network promotion tool; S2, generating and manufacturing publicity pages: according to a result of the promotion analysis, drawing an electronic file and a text page of the publicity pages; S3, publishing the publicity on the network; S4, integrating information after acquisition; S5, feeding back the publicity pages; S6, opening a video conference: uploading an opinion book to the video conference to discuss and acquire a publicity scheme; and S7, secondary publicity. The multimedia publicity page promotion method and system have good publicity page promotion capability, can realize accurate marketing, have the capability of integrating promotion feedback, and the method steps are simple and easy to implement.

The application relates to the technical field of Internet, and provides a precise marketing method, device and equipment based on user consumption data, which comprises the following steps: dividing a data set into a majority class data set and a minority class data set after normalizing the consumption data; sorting the Mahalanobis distance of each sample in the minority class data set and then dividing the sorted samples into several subsets to calculate the minimum Mahalanobis distance; taking the samples in different subsets as parent samples by using the MAHAKIL algorithm with a random parameter, iteratively generating offspring samples, and training a user group classifier by taking the offspring samples with a Mahalanobis distance greater than or equal to the minimum Mahalanobis distance from the parent samples as a new minority class data set to perform precise marketing on different user groups; and balancing the sample quantity relationship between the minority class data set and the majority class data set can make the classifier accurately identify the minority class users, avoid the bias towards a certain class of users caused by the quantity difference of the training data set, and improve the precision of marketing.