![\"Six](\"https:\/\/www.lukaswojcik.com\/blog\/wp-content\/uploads\/diagrams\/modellschwellen-en.png\")
The two conditions are not symmetrical<\/h2>\n
The difference in wording is easy to skim past and decides the case: the first condition counts events<\/em>, the second counts users<\/em>. A site with many page views per visit reaches the first mark considerably sooner than the second, because one person generates a dozen events in a day and still remains one user.<\/p>\n Then there is the windowing. The first condition asks for seven days, the second for seven out of 28. Both are minimums on rolling windows, and neither appears anywhere in the interface as a progress indicator.<\/p>\n Modelling needs examples. It transfers the behaviour of consenting people onto refusing ones, and for that it needs enough consenting people – not enough refusing ones. This puts a site with a high refusal rate in an awkward position: plenty of gaps to fill and too little material to fill them with.<\/p>\n That exact case is the fifth panel in the picture. The upper row sits far above the line, the lower one just below, and the verdict is nonetheless: not modelled. Anyone looking only at the refusal rate reads that as a fault.<\/p>\n Once the thresholds are met, estimated values appear in the standard reports, for instance under users, sessions and new users. The list of where they do not<\/em> appear is longer and matters more in practice.<\/p>\n The report in the interface therefore holds a figure that cannot be reproduced from the export – not through a fault, but because one figure contains an estimate and the other does not. Using both sources side by side and hunting for the discrepancy means hunting for something that has no cause in the data.<\/p>\n Google’s page states explicitly that a property which once met the prerequisites and later does not loses the estimated data – and gets it back if it meets them again. Neither transition is announced.<\/p>\n For a time series that means the counting method can change mid-course without a note beside it. A site with seasonal traffic may pass through this twice a year. The fourth panel is therefore the uncomfortable case rather than the third: missing the line clearly at least yields a consistent time series.<\/p>\n The page likewise states that the external prerequisites are not a guarantee. The model applies criteria of its own on top, and the ones named are the ratio of new to returning users and the ratio of users to sessions.<\/p>\n The thresholds themselves can be checked, and that is the first step. Two figures suffice: events per day with storage denied, and daily consenting users. Both are in the property, and the answer is a yes or a no rather than a feeling.<\/p>\n If the answer is no, the consequence is not helplessness but clarity: the reported figures are then counted values from consenting people only, and the refusing portion is missing entirely. That is an honester basis than a figure whose estimated share nobody knows. Estimating the missing portion anyway means doing the arithmetic oneself – and that arithmetic starts with the refusal rate, which is measurable in any case.<\/p>\nWhy the second condition is the harder one<\/h2>\n
Where modelled figures appear and where they do not<\/h2>\n
Modelled values appear in\n\n standard reports users, sessions, new users\n\nModelled values do not appear in\n\n BigQuery export not at all\n audiences no\n user explorer no\n cohort exploration no\n segments with sequences no\n retention reports no\n predictive metrics no\n path and funnel analysis handled differently\n\n Consequence: the interface and the export count differently,\n and both are right. The difference is the modelling.<\/code><\/pre>\nEligibility is not permanent<\/h2>\n
What follows from this<\/h2>\n