text: E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We propose a machine learning system which learns the user behaviour from multiple previous sessions and predicts useful metrics for the current session. In turn, these metrics can be used by the applications to customize and better target the customer, which can mean anything from offering better offers of specific products, targeted notifications or placing smart ads. The data used for the learning algorithm is extracted from Google Analytics Enhanced E-commerce, which is enabled by most e-commerce websites and thus the system can be used by any such merchant. In order to learn the user patterns, only its behaviour features were used, which don't include names, gender or any other personal information that could identify the user. The learning model that was used is a double recurrent neural network which learns both intra-session and inter-session features. The model predicts for each session a probability score for each of the defined target classes.
  @cite_0: Performance measurement of tourism websites is becoming a critical issue for effective online marketing. The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of. This method could be interesting for any tourism website optimizer.
  @cite_5: The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h=1, 2months ahead) a univariate benchmark performs best, while for longer horizons (h=3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two.
  @cite_1: 
  @cite_10: ABSTRACTThe primary purpose of an academic library Web site is to serve as a portal to library-acquired content. Navigational design of a library Web site affects the user's ability to find and access content. At Albertsons Library, the goal of the navigational design of the Web site is to mimic user behavior on the Web site to help them access information and articles from over 300 different library vendors. Coordinating with different vendors makes tracking the navigational flow of user behavior difficult with the tool Google Analytics. Using the events feature in Google Analytics, the team responsible for Web design was able to track user flow, and was able to quantify how many users were actual “drop-offs” versus those that were clicks into library resources. Decisions made after acquiring these data resulted in a Web site with a 10 percent or less bounce rate, and decreased the number of clicks required for users accessing the library's content.
summary: Using Google Analytics data for Machine Learning purposes is a relatively new and rarely used method, perhaps because the analytics platform's original focus is on aggregated data. The User Explorer https: support.google.com analytics answer 6339208?hl=en report, which provides insights at the user level, is relatively new. In @cite_5 , they predict the amount of people visiting a city based on website traffic of various touristic websites. In @cite_1 , they try to identify the demography of the users on a website, studying the evolution of location as well as device used for some time frames. In @cite_10 , they analyzed the website traffic to sort the most visited pages for some online library. Then, based on the traffic analysis, they put more emphasis on the most visited ones, promoting them to the main page more often which resulted in a decrease of the bounce rate by a large margin. In @cite_0 , they analyze the performance of various e-commerce sites using computed statistics of the features offered by Google Analytics.