I thought the problem was simple.
Posted: Mon Dec 23, 2024 5:05 am
Data monitoring attribution is used to identify which advertisement or channel brings about the key behavior of the advertisement. Monitoring points: business type, advertising media, delivery method, attribution window day, channel sub-channel, platform. Data feedback is used to configure the user-specified behavior and then transmit it back to the media. Configuration points: business type, advertising media, channel sub-channel, reporting behavior, payment time range, payment amount ratio, triggering conditions. Business acceptance (allocation) How to reasonably allocate resources after acquiring customers and maximize resource utilization can be used as a reference | Product solution: resource allocation middle platform.
Resource conversion (operation) How to refine operations list of ghana phone numbers after allocating resources and form can be used as a reference | Product solution: private domain operation middle platform. Media data 4. Important logic. Monitoring logic obtains a unique device number through collecting, analyzing and evaluating advertising delivery data for click attribution acquisition and fuzzy attribution. .Attribution logicRegistration attribution: Search the latest registration attribution channel of the user in the entire site and attribute the user to this channelPayment attribution: Search the latest registration attribution channel of the user in the entire site according to the paid user and attribute the payment to this channelClick attribution: Use the device number used by the user as an identifier and query the click data within the day according to the payment attribution window period to see if there is a match (precise fuzzy match).
If there is, attribute it to the channel number carried by this click dataClick attribution must be configured with the correct bottom package, otherwise the data will be biasedChannel package attribution: Report data based on the channel associated with the k package used by the user.Return logicA new architecture that surpasses and has just been born. The method proposed by researchers from Stanford and other institutions directly replaced the attention mechanism and the language model method will be completely changed from now on. Wake up and a new architecture that surpasses and is born? Researchers from Stanford, Berkeley, and proposed a new architecture that replaces the hidden state of the machine learning model.
Resource conversion (operation) How to refine operations list of ghana phone numbers after allocating resources and form can be used as a reference | Product solution: private domain operation middle platform. Media data 4. Important logic. Monitoring logic obtains a unique device number through collecting, analyzing and evaluating advertising delivery data for click attribution acquisition and fuzzy attribution. .Attribution logicRegistration attribution: Search the latest registration attribution channel of the user in the entire site and attribute the user to this channelPayment attribution: Search the latest registration attribution channel of the user in the entire site according to the paid user and attribute the payment to this channelClick attribution: Use the device number used by the user as an identifier and query the click data within the day according to the payment attribution window period to see if there is a match (precise fuzzy match).
If there is, attribute it to the channel number carried by this click dataClick attribution must be configured with the correct bottom package, otherwise the data will be biasedChannel package attribution: Report data based on the channel associated with the k package used by the user.Return logicA new architecture that surpasses and has just been born. The method proposed by researchers from Stanford and other institutions directly replaced the attention mechanism and the language model method will be completely changed from now on. Wake up and a new architecture that surpasses and is born? Researchers from Stanford, Berkeley, and proposed a new architecture that replaces the hidden state of the machine learning model.