Twelve methods to identify false traffic

Twelve methods to identify false traffic 1. Use advanced groups to separate traffic

Before checking, you must separate this part of advertising traffic from other traffic on the website. Advanced groups are the best choice. Because we have previously marked the traffic source, we only need to create an advanced group with the same source as bluewhale to split the traffic. 1.

Figure 1 filter traffic from bluewhale. CC

After creation, select to use this advanced group in the report. This part of traffic runs through the entire report. This is preparations before traffic check. Avoid traffic interference from other sources.

2. Traffic Generation Time

Google Analytics reports used: visitor-Visitor trend-number of visits. 2.

Figure 2 traffic change trend chart

The time here must be accurate to the access data per hour. Generally, normal Website access traffic is distributed in various periods of the day. Even if there is a peak, the curve is also relatively smooth (except when an advertisement is launched ). False traffic is artificially controlled. In order to save costs, we will not care about the time distribution of traffic, so we will find a sudden increase in traffic on the time curve. Therefore, if the traffic is too concentrated in a certain period of time, or an abnormal increase occurs in a certain period of time. This part of traffic is suspicious.

Of course, it is not ruled out that some programmers calculate the date and time end, and simulate click on the curve on time. If you encounter this "smart traffic" situation, you must continue to use the third method.

3. Geographic sources of traffic

Google Analytics reports used: visitor-map overwrite, as shown in 3.

Figure 3 geographic coverage of access volume

 

Visitors to a website usually come from different geographic locations (googleanalytics uses the visitor's IP address to determine the geographic location information of the traffic source ). Therefore, we can see many traffic sources in different regions in the map overlay chart report. However, false traffic is usually difficult to use different IP addresses in multiple regions to generate traffic. Therefore, the region coverage chart shows that if the traffic sources are concentrated in one region, this traffic is suspicious.

Your advertisement may only target visitors in a certain area, so the geographical location range of visitors is not applicable to you. Or you have another "more intelligent" traffic, such as human traffic! You can simulate access from multiple geographic locations through a proxy or a part-time employee distributed in different regions. Next, let's look at it.

4. Network Properties of traffic

Google Analytics reports used: visitor-service provider, as shown in.

Figure 4 visitor Network Access Report

The service provider report shows the network access methods used by website visitors. Under normal circumstances, the access methods of Website Visitors may vary widely. However, the access method for fake traffic is very simple. Therefore, if only one or two service provider names are displayed in this report, your traffic is suspicious. However, I still did not answer the above question, that is, the super intelligent human traffic. There are also many access methods for human traffic, which cannot be identified in the service provider report. How can we identify human traffic? Don't worry. This question will soon be answered.

5. Traffic bounce rate

Google Analytics reports used: visitor-Visitor trend-bounce rate.

Figure 5 24-hour bounce rate trend report

Bounce rate is a measure of the page quality. In turn, it is also a good tool to identify false traffic. If the bounce rate of a website increases suddenly in a certain period of time, find the traffic at that period and compare it with the previous access period, geographical location information and access methods. If any of the preceding conditions are met, the traffic during this period is very suspicious.

6. Website stay time of traffic

Google Analytics reports used: visitor-Visitor trend-website stay time.

Figure 6 website stay time 24-hour Trend Report

The website stay time is not a very accurate indicator, and will be affected by the cookie30-minute survival. However, the preceding reports can be used together to further verify suspicious traffic.

7. Go to the path & click the distribution chart

Google Analytics reports used: Content-popular content-go to the path.

Figure 7 visitor navigation Summary Report

We usually create a login page landingpage for advertising activities, so there is only one entry page for advertising. However, visitors may have different behaviors when they come to the website. They will click different links to access different pages and end their access to the website on different pages. These operations are hard to accomplish. Although some "smart traffic" can be completed 2 ~ Three clicks. However, they are all pre-defined. Therefore, their access paths are basically the same as the end page.

8. matching with the target report

Google Analytics reports used: Traffic Source-target.

 

Figure 8 traffic target conversion rate report

This is what you should do before every advertising activity. Is to set a target for the traffic. Google Analytics has now upgraded its target feature. You can set multiple targets for traffic. Check the traffic in multiple dimensions. Target fulfillment is the best way to identify false traffic. Many intelligent traffic indicators such as bounce rate, stay time, and access time distribution can be bypassed, but few of them can achieve the goal. Of course, this depends on the complexity of your goals. If the CPA is set to complete shopping, This is a killer target for fake traffic. If the target is only a registered user or information, manual traffic can be fulfilled.

9. single page refresh Analysis

Single-page refresh refers to the refreshing of traffic on the landingpage of the website to reduce the bounce rate. This type of Traffic bill shows good performance in terms of bounce rate indicators, but it has not completed conversion and purchase. In this case, it is difficult to determine whether the traffic is fraudulent. You need to perform in-depth analysis by accessing the path or clicking the heat map. However, in the case of multiple landingpages, even path or hot zone graph analysis becomes a very large project. This is because we may need to check the access of traffic on hundreds of landingpages one by one. We have a good solution to this problem, that is, using custom metrics pageviews/unique pageviews.

Figure 9 check a single page refresh by using the combined page views and unique page views

Pageviews indicates the page views, while uniquepageviews indicates the unique page views of each page, which is equivalent to the number of visits to each page. During a single access, browsing a page multiple times will only increase pageviews, while uniquepageviews will not increase. Therefore, we use different pages as dimensions, and use pageviews and uniquepageviews to see the number of times visitors browse the same page at a time. Generally, a visitor does not browse the same page multiple times during one access. Therefore, if the value of pageviews/unique pageviews is high, this part of traffic is worth noting. Of course, this is not an absolute standard. To ensure that nothing is lost, the best way is to set the pageviews/unique of this part of traffic
The pageviews value is compared with the value of these pages on the entire site.

 

10. Visitor Loyalty Analysis

Visitor Loyalty is an analysis of the frequency of visitor return visits within a period of time. Generally, when a certain number of visitors come to your website, some of them will access your website again. Even if there are very few visitors. Even if there is only one or two. This is like in a page, even if some links are placed in a very hidden location, there will always be people clicking, even if the proportion is very small. I remember a real lesson. When we analyzed a WAP website for the customer, we found that the number of clicks on a link on the page was 0. At that time, I took it for granted that this link was normal because of the high traffic and cost of providing online movies. But the actual situation is totally different from what we think.

Therefore, when analyzing the traffic of a channel, appropriately increasing the time dimension to analyze visitor return visits is also a way to identify false traffic. Real visitors will return to the website again, but fake traffic will not close after the cooperation. Most of the traffic that has not been returned after the cooperation period ends is abnormal.

11. Visitor coincidence analysis

Visitor coincidence refers to the ratio of the number of unique visitors to the number of unique visitors in a period of time. For example, suppose that I find 10 people each day to click on your advertisement for 10 consecutive days. In this case, Google Analytics records 10 unique visitors every day. The sum of ten days is 100. However, when we extend the time dimension to 10 days, there will be only 10 unique visitors. This is because googleanalytics performs deduplication on the visitor, so every visitor in the 10-day data is unique. Based on this logic, we can calculate the visitor coincidence between different channels. The formula is as follows: 1-heavy visitor/non-heavy visitor * 100%. In the preceding example, the visitor coincidence is equal to 1-10/100 * 100% = 90%.

Figure 10 unique identity visitor report

For different traffic channels, we can also use visitor coincidence indicators to identify false traffic. When the traffic of a channel has a high visitor coincidence in a short period of time, we need to further check the traffic quality of this channel.

12. Analyze the long tail of page access

Long Tail Analysis of page access refers to the extensive page browsing of visitors. Based on the characteristics of actual traffic, each visitor's characteristics, interests, and habits are unique. They will browse the website content in various ways according to their respective objectives. Visitors can see these natural and diverse features through the popular content on the website and the exit page. As shown in, these are all false traffic that cannot be simulated.

Figure 11 visitor page access Distribution Report

Popular content is the most popular page throughout the access process. It is the page view trend chart of popular content on the website. Because the purpose of each visitor is different, in addition to the most popular pages, many pages will also be browsed, and most pages have very few views, only 1-2 times. These are the long tails of page access. They fully demonstrate the nature and diversity of real visitor websites. Similarly, such a long tail will inevitably exist for exiting the page, because visitors will end the access on different pages.

Several methods to identify false traffic have been introduced, and it seems that there is still no way to completely identify false traffic. Yes, fake traffic is constantly imitating real traffic. And the human traffic is so cheap. We cannot prevent them. Google Analytics reports alone can identify a portion of false traffic. More fake traffic can only be detected by time. For example, after an ad activity period, this part of the traffic's return rate, retention rate, and so on.

 

 

Author Profile

Wang Yanping (blue whale), a web analytics enthusiast and author of The Blue Whale website Analysis notes blog, has been engaged in website analysis for many years and is good at using googleanalytics. We hope to explore the true meaning of website analysis and drive data-driven decision-making.

 

Joegh, author of the website data analysis blog, is engaged in Internet Data Analysis. He has been involved in web site data analysis and mobile Internet application data analysis, I also have some knowledge about Data Warehouse and Bi (Business Intelligence. He is good at quantitative analysis of website data, including content quality of the website, user behavior of the website, and user experience optimization of the website. He hopes to apply the conclusion of website data analysis to practice, and create value for the website through data analysis.

 

This article is excerpted from "website analysis practices-how to use data-driven decision making to enhance website value"

Edited by Wang Yanping Wu Shengfeng

Published by Electronic Industry Publishing House