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Google Analytics

web_analytics digital_analytics marketing

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2020-Q1 2026-Q1

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Miroslav will present Analytics from a completely different perspective. Some things he will reveal about the life and job of an Analytics / data expert will be painful, honest, interesting and most of all, funny. After his Stand-up, you will look at attribution, data collection and Analytics dimensions / metrics, from a new point of view. Please, don't expect everything will be 100% accurate - after all, it's a stand-up.

talk
by Doug Hall (ConversionWorks, UK)

Correct and accurate measurement of a hybrid app with GA is hard. The Native app outer can be measured in GA using app measurement and the mobile web content can be measured using normal universal analytics GA. The problem is that you can't tell where hybrid app users came from in the mobile web GA - there is no useable traffic source. You can't measure the app and web in the same property but you have hybrid app mobile web and pure mobile web in the mobile web property. This sounds like a complex mess and it is but we've solved it and we'll explain how with a real live demo and technical walk through with Q & A.

Enhanced Ecommerce introduced some new metrics that explain user behavior – something Google Analytics should be used for. Robert, a corporate web analyst or the EEEO (Enhanced Ecommerce Executive Officer) will show us how he uses Cart-to-Detail Rate, Buy-to-Detail Rate and some calculated metrics for better campaign as well as warehouse planning in the EEEO (Enhanced Ecommerce Enabled Organization).

Somewhere along the spectrum of "logging into Google Analytics" and "the machines are in control" is the world of the power analyst who interacts with the data on the fly, applies statistics to large data sets, and develops interactive visualizations that go well beyond the capabilities of Excel. Those power analysts are operating on the fringes of the domain of the "data scientist" -- a role for which no one can really agree on a concrete definition! In this session, Tim -- who has never claimed to be and never will claim to be a data scientist -- will share what he has learned from trying to understand the scope and nature of that role. And, beyond that, how he has grown as a digital analyst, expanded his skills to "program with data" with R, and increased his value to the organizations with which he works as a result.