By determining these spots and delivery time? they made an The media advertising delivery plan for the client. Based on these plans? the advertising materials cut according to the size of the advertising spots were manually uploaded to the media in advance. according to the advertising positions and times indicated in the grids? and the delivery work was basically completed.
This was the original 1.0 era of The media
Digital advertising? which was indeed very primitive – manually uncomplicated inside sales to scale your business selecting spots (advertising spots)? manually making materials? manually adjusting the size of materials? and manually uploading…
As a result? a large number of middlemen (new agencies) who provide technical solutions for advertising delivery have emerged in the digital advertising industry chain. The Excel (scheduling table) required for “climbing the grid” and this part of 3d layers in arcgis: using integrated mesh? 3d object? and point cloud the job positions have been greatly reduced. Instead? the demand for “advertising delivery optimizer” positions has grown rapidly.
Then? media platforms that provide
Advertising resources discovered a more attractive feature adb directory of digital advertising 2.0: the ability to use machine learning iterations to automatically optimize the delivery effect? and the higher the delivery cost and the longer the delivery time? the more likely the client will enjoy this benefit. The logic is not complicated: media platforms will count: which groups of people with certain characteristics are more likely to respond after seeing an advertisement? such as being more willing to click on an advertisement or even to perform a specific conversion behavior? which means that an advertisement is effective for this group of people. Then? the next step is to find more people with similar characteristics based on the characteristics of these groups of people? and deliver the same or similar advertisements to these people.
