Web analytics - according to my defintions - is a set of tools and techniques for collecting, processing and reporting data concerning events with an user-client actions. The website and its users are often the core of any analysis however, the need of extending the present fields to external factors (such as brick and mortar stores) is getting more and more desired. Not only Analytics collects data from the website but also it considers other sources as a valuable source of information e.g. cash register in a brick and mortar shop, self-service kiosk, mobile app, game etc.
Data gathered from different sources of a client vs. shop relation are our first major task.
Second task is to process, connect and filter all the data in such a way they'll become meaningful and valuable to analyse. Reports as the final product of such a development are to help business owner make better decisions.
A very important issue is the purpose of this analysis. Personally, I think that using Google Analytics makes a lot of sense only if we know exactly what data should be collected and how to use it in decision making process. Otherwise it is a waste of time.
Analytics needs implementation of its tracking code to particular website. Tracking code follows the user across the website to send us essential data about: where did the user enter your site, what was visited, how much time did he spent on a page, was the form filled, where did he finish the visit, etc. Metaphorically speaking, the tracking code is like a sentry who reports every activity of the user and sends his reports to Google Analytics. All the collected information are anonymous so that it cannot be linked to any specific user.
In the same way as in website analysis. The mobile app is structured with a special code responsible for data collection and periodical exporting it to Google Analytics.
This kind of operation is a bit different process and requires slightly more effort. Google Analytics enables you to transfer data via specific protocol called Measurement Protocol. To use this feature you must equip your cash register with an Analytics collabotation feature. Alternatively, you can aggregate information from the cash register in a different way and upload it with another tool. This issue will be discussed in one of the last chapters. At this stage the most important thing is the ability to verify both, the customers behavior of the online shop and a brick and mortar shop.
Generally speaking, number of data and its type are no longer a limit. Any business data can be transfered and connected to standard data of Google Analytics. Transfer containing advertising campaign costs data may be a great example of analyzing the results of the campaign and its influence on our store's income.
Every user who visits your website generates a stack of data called "hit". Number of hits depends on the number of pages visited by the user. A visit is a certain number of hits is called session. Number of hits defines session length. One user may perform multiple sessions.
Tom has visited your website via smartphone browser. He moved to the contact tab from the home page to find your phone number. The next day he visited all 20 products via his laptop. Analytics will show you 2 sessions: one consisting of 2 hits, second one with 20 hits.
Information about campaigns can result from Google Adwords and get automatically connected with Analytics. Campaign data may be marked independently e.g. mailing campaigns or content marketing data.
Analytics can be linked with Google Webmaster Tools, whereby reports are extended with technical information about Google search results.
Also, one needs to mention data related to demographics and interests of users. This kind of data come from DoubleClick cookie (https://support.google.com/adsense/answer/2839090?hl=en) - of Google Display Network integrated with a large number of This technology gives us insight into granular data (age, sex, interests) of a significant part of our users.
Remember that you can compare your own data with these collected by Google Analytics. You can do this by using Data Import and Cost Data Import functions which are going to be covered in next chapters.
All mentioned data is "magically" processed by Google Analytics and shared in form of numerous reports ready to be freely configured.
Analytics begins the processing right after receiving all the necessary data from the above mentioned sources. Varoius data that is received is being linked in the reports related to sessions, users and activites.
Previously configured filters are applied on collected data to reduce their amount and modify them. The most common filter is the one deleting your own computer activity from the reports. It maintains only real users activities.
New data is being generated by basing on your data. A good example might by the input about previously configured objectives and their conversion rate.
Collected data can be grouped in various ways.
Finally, all the collected info end up in relevant reports.
Each report consists of dimensions and data (metrics), normally presented as a table. Dimension is simply a description or text. Metric is a number or relation of two numbers. For example, dimension "Sex" may refer to metric called "Number of women" with a value of 10. For more information visit: https://support.google.com/analytics/answer/1033861?hl=en
Tables can be freely sorted and filtered. Information can be presented in many other ways e.g. as graphics charts.
Google Analytics is a tool to collect data from various sources allowing their thorough analysis and draw a constructive conclusions. Enormous capabilities and variety of configuration to attain a personalized data feed are making Analytics an outstandingly powerful tool. Nowadays world highly values any authentic information. Therefore, it is fundamental to know how to draw proper conclusions relying on the data. Now when you are acquainted with Analytics foundations you may move on to the next chapter.
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