Web site new and old user analysis to determine the basis for measuring development

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The analysis of new and old users in the Web site has become a common method of user segmentation in Web analytics and an important component of user analysis in Web Analytics. The naming of new and old users in Google analytics, respectively, is visitors and returning visitors, and provides a breakdown of many of the analysis metrics based on new and old users.

Simply put, a new user is the first user to visit a website or to use a Web service for the first time, while an older user is a user who has previously visited a Web site or used a Web service. Both new and old users can bring value to the site, which is the meaning of analysis.

Analysis of the significance of new and old users

The site's old users are generally loyal users of the site, there is a relatively high viscosity, but also for the site to bring value to the main user groups, and new users means the development of the website business, is the premise of increasing the value of the site. It can be said that the old user is the basis for the survival of the site, new users are the driving force for the development of the site, so the development strategy of the site is often based on the retention of old users on the basis of constantly improve the number of new users.

So the meaning of the analysis of new and old users is: through the analysis of old users, to determine whether the foundation of the site is solid, whether there is a crisis of elimination, through the analysis of new users, to measure the development of the site is smooth, whether there is greater space for expansion. One looking now, one looking to the future.

Identification of new and old users

To the website user's recognition, before has written a related article--the website user's recognition, inside mainly is in based on clicks the Flow Log Foundation to provide 4 class to identify the user the method, may serve as the reference. However, the identification of new and old users may be based on the site's own specific and have different definition methods.

The most common way to identify new and old users is to see whether the user has visited the site before, that is, the user is the first access to distinguish, GA is the use of cookies to define new and old users, that is, the cookie before the visitor is the old user, otherwise for new users. This definition applies to all Web sites, but where it is inaccurate, the deletion of the cookie, the user changing the PC, and so on will cause the deviation of the data.

Another type of discrimination is relatively accurate, but generally only applicable to registered login Web site, that is, to define the first registered user for the new user, the user to log in again for the old users, rather than using the first visit to distinguish. This distinction is generally based on the user ID or user name to identify, relatively accurate, but the scope of application is limited.

Analysis of new and old users

The goal of the site is to maintain the old users, to expand new users, so the performance of the site data analysis, is to maintain the steady growth of the number of old users under the premise of the increase in the proportion of new users.

For most of the normal development of the site, the site's number of old users should be relatively stable, and there will be a sustained small increase, you can see the GA on my blog on the number of old users of the weekly trend of changes:

The returning visitors can be selected through the advanced segments of GA's dashboard, and the appropriate time interval and the total granularity (day, week, month) are selected to display the trend change curve. This smooth upward curve indicates that the development of the website is tending to normal.

But not all the old users of the site trend will be so smooth, such as tourism sites, tourism will obviously be affected by the season to show relatively large fluctuations, so here to introduce the concept of year-on-year and chain analysis.

The year was meant to eliminate the effects of seasonal changes, comparing the current period with data from the same period last year, such as the February data compared to last February;

The chain refers to the current period of data and earlier data comparison, can be the day chain, the month chain, the week chain, etc., such as February this year and January this year compared with the data.

And the chain is heavily applied to the trend analysis based on time series, for the site, the number of visits, sales, profits and other site key indicators can also be cited year-on-year and chain analysis, for analysis of these indicators of the trend of change, to eliminate the impact of the season are positive effects. Below is a tour based on the Year-on-year and the chain of the old user data Simulation Trend Analysis chart:

It can be seen from the graph that the influence of the seasons the old user number fluctuations are relatively large, so the corresponding chain growth fluctuation is also very large, but the trend of growth is relatively smooth, has been maintained at about 10% of the growth rate above, this can be seen that the site to maintain the old user is effective, the site's operating situation is more stable.

One might ask, why use an absolute quantity, rather than a relative amount, such as the ratio of old users to total visitors, for trend analysis? The main consideration here is that the website will actively promote marketing, or due to the impact of certain events or media dissemination of the effect of passive promotion, This time may attract a large number of new users into the site and lead to a sharp decline in the proportion of old users, while the absolute data for the old users of the site is relatively stable, more reference value.

The absolute number of new users is not as stable as the old users, and will not necessarily maintain the trend of growth, and the analysis of new users is mainly to measure the effectiveness of web site promotion, assessment of the above active marketing or passive events brought about the impact, so do not recommend absolute value, since the old user relatively stable Then can be based on the new user proportion of the trend of change to analyze the site to promote the effect of a certain period of time. The benchmarking of GA also provides a trend comparison between the proportion of new visits and other site baselines:

Often a sharp rise or fall in the curve of a certain point in time means that the impact of a marketing event, and when the curve continues to decline means that the effect of the site to promote the adverse, need to increase the intensity of the promotion.

If you have a better view of the new and old users of the site, please comment.

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Original address: http://webdataanalysis.net/web-quantitative-analysis/new-returning-visitors-analysis/