Part 1 Website Analysis Definition and Tool Overview
1. Overview of website information
The goal of the website determines the data and focus required for analysis. The value of data analysis is reflected in improving the user experience on the website, and the improvement of user experience can attract more users to visit frequently (traffic ), and finally get the desired result. This is an iterative, continuous improvement process.
2. Interpretation of key information
(1) Results (goals)
The goals of each website are also different. Websites in the early stages of growth do not pay attention to ROI, they pay more attention to the growth of new customers and the value they bring. While mature websites pay more attention to the frequency of old customers' visits and the value brought by each visit.
(2) Data
- Data from the website itself: to measure the performance of the website;
- Competitor data: used to understand competitors and industry changes;
- Quantitative data: The website automatically obtained through the tool is quantitative data, which tells us the story of what happens in the website every day, such as: Who has visited my website? where are they from? What did you do on the website? What information were viewed? From what pages?
- Qualitative data: tell us why these stories happened, eg: did the visitor complete the visit goal in the website? If not completed, why? A simple way to obtain qualitative data is to communicate directly with website visitors.
(3) User experience
There are only 3 user experience measurement criteria:
- Whether the purpose of the visit is completed is not limited to whether the user completes the conversion, but also includes the user's visit behavior that is not for the purpose of conversion.
- time taken to achieve goal
- The workload required to achieve goals. Together with the second criterion, it is called the efficiency of users completing tasks, which includes both the time it takes to complete the task and the number of times the visitor interacts with the website during the process.
Data does not directly drive or affect results, but is used to measure and improve user experience, which requires that the data must be able to completely and accurately reflect user behavior.