In the case of the previous customer，Xiaohong uses data analysis to discover areas that require usability testing。But so far，Xiao Hong only found individual pages and page groups in the website that were worth testing。She feels she needs to know more about the most common user journeys。She also wants to understand 188bet best bookmaker more deeply，See how different types of 188bet best bookmaker access the website。In order to conduct the best usability testing，Xiaohong really wants to know how people actually use the website。

In simple terms，Data analysis is an excellent way to discover the best test pages for usability testing。In the first part of 188bet best bookmaker series，I explain how to use data analysis to find problems with your website。Doing 188bet best bookmaker allows us to better understand current user behavior，And help us focus on the tasks to be tested。

Summary of this series of articles on how to use data analysis to guide usability testing，I will explore more closely how to identify user journeys、Categorize 188bet best bookmaker to compare the behavior of different user groups。

Identify churn points

Knowing how 188bet best bookmaker move throughout the site can add context to the status of individual pages。For example，Analyzing data from previous pages in the user journey can help us identify why a particular page has a particularly high exit rate。Also，Finding out the most common user journeys is good for planning usability testing。Usability testing can be designed based on these common user journeys，To ensure that the user behavior in the test is consistent with the existing user behavior。

Google Analytics attempts to show user journeys through user flow and behavior flow reports。They may be harder to read，And it often becomes troublesome to combine multiple pages together。This means that Google Analytics can usually only display the most popular pages individually，And group other pages together，Shows ">100 pages" - 188bet best bookmaker doesn't help us at all。The screenshot below shows how 188bet best bookmaker very limited information makes analysis difficult: only a few pages are displayed individually for each stage of the user journey，The remaining pages are grouped together。

Although the pages were combined causing a lot of problems，Spending some time analyzing these reports can still help us identify problem areas，Based on churn or unexpected user journeys (such as，This is what we expected，But the user went in another direction？）。Once we find the problem area，We can plan usability testing，Let’s see how 188bet best bookmaker think during the entire trip，Understand why they have these troubles。

In Google Analytics User Flow and Behavioral Flow reports，All pages are represented by green rectangles，Gray connecting lines are used to represent user journeys between pages。Each rectangle also represents the percentage of churn (meaning 188bet best bookmaker are leaving the site) in red。They illustrate common user journeys，And where 188bet best bookmaker left the site – another sign of problem areas。

The example below is from a travel website I used to work on。It has a particularly obvious 188bet best bookmaker box on the homepage。

In this simplified diagram with added notes，We can see a possible problem。188bet best bookmaker use the search box to find a travel destination，But then returned to the homepage from the search results page (aka，pogo sticking)，Indicates that the search results are not satisfactory enough for the user。This may be due to many reasons: maybe the search function often fails to find results，Too many results found，Or too few results。Maybe this problem has nothing to do with the search results itself，But other reasons，For example, the price of the hotel in the search results is too high。

Data shows initial searches dissatisfy 188bet best bookmaker，This made me decide to do some usability testing on the search box。The results of the usability test are displayed，The problem is that the search results are too many and too general，User is overwhelmed by results。According to this test result，I suggest introducing a faceted search system: allowing 188bet best bookmaker to filter search results based on some criteria on the search results page，No need to return to the homepage to search again。This new search system allows 188bet best bookmaker to filter their search results based on hotel amenities；For example, whether there is a swimming pool、Gym and other facilities，This means 188bet best bookmaker can discover results that are useful to themselves。This design solution has greatly reduced the number of 188bet best bookmaker who return to the homepage after searching，Let more 188bet best bookmaker move to the next step in their journey。

The above results show the analysis data one month after the multi-faceted search system was introduced。shown in the picture，Reduced “spring” between home page and search results page。Although there is still room for improvement，But the positive effects of 188bet best bookmaker change are very encouraging。

188bet best bookmaker segmentation, more details

Data segmentation provides an excellent way to observe different behaviors of different 188bet best bookmaker。A simple example is to compare new 188bet best bookmaker and returning 188bet best bookmaker。The picture below is from an online job search website，It shows that the number of new 188bet best bookmaker was almost flat for the month，However, the number of returning 188bet best bookmaker follows a different pattern: a significant drop in numbers over the weekend。

This makes me want to know more details，About the differences between new 188bet best bookmaker and returning 188bet best bookmaker。Other data about these two different 188bet best bookmaker is shown，Returning 188bet best bookmaker tend to spend more time on the website，More pages will be viewed every time，And more inclined to apply for jobs。

Based on this data, I can make a hypothesis: returning 188bet best bookmaker are more likely to be people who are actually looking for a job，But new 188bet best bookmaker are more casual when visiting the website。So I recommend the website to do some personalized design - to treat new 188bet best bookmaker，Show more warranty information，Indicates that the job search website is legal、Trusted，And guide them to take action simply and quickly，For example, registering for work reminders。Treat returning 188bet best bookmaker，More accurate display、Detailed search options，And provide information to encourage them to apply for jobs。

The different behaviors of new 188bet best bookmaker and returning 188bet best bookmaker can reveal many things，Depends on the type of website。For example，For an e-commerce website，It shows that people who come back to this website are more likely to place an order。If this is true，Then we can focus on helping 188bet best bookmaker who visit the website for the first time place orders。

This data segmentation analysis can also help with recruitment for usability testing。If there is a clear behavioral difference between new 188bet best bookmaker and returning 188bet best bookmaker，Then it might be best to recruit both existing 188bet best bookmaker and 188bet best bookmaker who have not yet visited the site to test。Testing different user types can help explain why they behave very differently on the site。

In addition to the new 188bet best bookmaker and returning 188bet best bookmaker in the above example，There are also some ready-made data segmentation methods on Google Analytics to help us separate the data，Includes:

Different traffic sources – can be used to identify differences in 188bet best bookmaker who come to your site through searches and links。

188bet best bookmaker using different device types – can be used to compare mobile usage、Parameters for tablet and desktop 188bet best bookmaker。

It is also a good way to modify the segmentation method according to your own needs，This allows the segmentation to better match important 188bet best bookmaker and roles across the site。By this method，We can analyze the different user journeys taken by these different user groups，For example，Compare the journeys of existing 188bet best bookmaker and first-time purchasers。

Data segmentation can be used to observe user journeys using different devices。Segmenting according to mobile tablets and desktop computers can provide three different behavioral processes for research。This method is particularly helpful for identifying issues that may exist for 188bet best bookmaker using different devices。Mobile user behavior flow chart may show a significant churn point in the user flow，But not a problem on tablets and desktops。This should lead to corresponding mobile usability testing，Focus on identifying why mobile 188bet best bookmaker are churn at this point in the process。

What to do now?

After using data analysis to identify problem areas，The next step is to find out why 188bet best bookmaker have these problems。Data analysis can provide some key points，Needs our special attention in usability testing，Or split out a special test。As a user experience professional，We naturally want to be with our 188bet best bookmaker，Learn from them during usability testing。Data analysis just helps us test better。

Give it a try - extract some of the methods mentioned here，Apply them to a project。You will be surprised to find，We can discover so many things from 188bet best bookmaker。

For those readers who still feel unsure，There are a lot of help materials here。If you want to keep up with the latest developments in Google Analytics，I recommend Google Analytics official blog，And Occam’s Razor – the blog of 188bet best bookmaker guru Avinash Kaushik。If you want to learn and improve 188bet best bookmaker skills in practice，You can refer to Google Analytics Training Center。These tutorials can help you better prepare to obtain a Google Analytics Personal Qualification Certificate。