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UX Research Catalogue - Coggle Diagram
UX Research Catalogue
Automatically Gathered Data
Usage data
Site metrics
observe the behaviour of all users on the website.
Web analytics
Log files / log analysis
: information stored on the server that contains all pertinent information about a user's computer. Country, OS, device, browser, language, new vs. old visitor etc.
WA vendors
: services that build and offer reading of log files, plus additional cookies that store a user's information on a given page. In the case of Google Analytics, behavioural information of users collected from other Google sites and services is also stored.
Tracking codes / page tagging:
Whenever a user requests a page or performs an action tracked by one of these codes, a message is sent to the hosting service, which logs that action and saves the information on its servers.
How to analyse
Defining a date range:
To make the best use of analytics data, it is necessary to compare the current data to what previously happened and to what you want to have happened.
Deciding on context
: The metrics of the greatest interest are the ones that tell you how many users do these activities, how often, and how much revenue (or awareness, or some other payoff) you gain as a result.
Issues
: User analytics do not tell you the cause of issues, but it finds correlations. In order to identify causes for frictions on journeys, you have to talk to customers directly via surveys or usability interviews. Usage metrics are supposed to identify problems.
Types of WA
Site-Wide Measurements
Definition
Examples
Session-Based Statistics
Definition
Examples
User-based Statistics
Definition
Examples
Clickstream Analysis
Definition
Examples
Metrics for Internet Advertising
Definition
Examples
Web analytics and A/B testing
: Site metrics are important not only to detect problems linked to journeys on the site, but also to evaluate the performance of A/B tests. These tests, also called split tests, aim to distribute users into journeys and evaluate variations of tested designs on the site. Multivariate tests include more than two variations.
Inconsistent, piecemeal changes:
one risk of continuous A/B testing is that focusing and adding piecemeal changes that cause small conversion rates contains a problem. Interfaces can get overloaded and then the website loses not only consistency but also compromises the experience. This is a problem that can’t be diagnosed through measurement alone.