This is the time to build a solid and robust benchmark. Go through your data from the past two years and try to identify the rate of cookie loss. The longer the period of time youāre investigating the higher the cookie loss. Similarly, if youāre not already doing so, implement an ad block detection system. The best way to do this is to run some client-side JavaScript that uses a namespace of a known tracker ā name it e.g. āads.jsā ā and then send hits to some custom data store you own (so not Google Analytics) if that file is blocked by the browser. Then, segment your data by browser. Check especially the usage statistics for Firefox and Safari, as they are the most prominent tracking prevention browsers out there. Note that this isnāt an exact science. Especially Chromium-based browsers (Chrome, Edge, Brave) might make it difficult to distinguish one browser from the other. Once you have a benchmark, you know the scope of the problem. You can apply these numbers to your analyses by introducing margins of error based on the cookie loss statistics and the amount of ad blocking in use. For example, if your data shows that 20% of all visitors to your site block Google Analytics, you can be less worried about the 10% of the discrepancy between transactions collected by GA versus your backend. ā Simo Ahava, partner and co-founder of 8-bit-sheep and a Google Developer Expert for Google Analytics and Google Tag Manager |