The common behavior patterns. And the algorithm needs to divide users according to their propensity to make a purchase i.e. classified. From the machines point of view this is a standard task. Classification requires labeled data with features and classes that the algorithm. Llearns to identify In the case of data the more the better . At the same time they should be distinguished by quality analytical systems data Google Analytics AppsFlyer internal data known about users. Based on the information features for the learning model are built. From the signs we add everything that is known about the user user experience micro conversions. After collection we select the algorithm to use.
Try different algorithms include new
It is impossible to say with certainty which one is suitable in Netherlands Phone Number List a particular case. features and see what gives the best result. How does the model continue to work Imagine that there are user groups in front of you. In group the conversion is . and in group it is zero . That is in group they do not make purchases.he largest portion of all paid users. Whats left to do Theoretically focus on two groups and connect sales only for them. But the remaining including group then drop outjpg To do this we enable optimization finetuning online advertising to attract target groups of users.
Groups and during this time collected
Optimization reduces click costs and increases traffic to the site. Optimization of Netherlands Telegram Number List advertising campaigns There are two options for introducing optimization Create new campaigns Replace the event that generates conversions in current campaigns Lets look at the second option. In the example above all users received a rating and when the event is replaced the scenario for the algorithm will be the same the same conversions will come. Dont be afraid to turn off groups.
