The core index of network marketing effect measurement and what kind of logical thinking we use (2)

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Objective】

In the previous session we explained the two most important ways to measure the effectiveness of online marketing-measuring changes in people's minds and measuring human behavior. Changes in human behavior, in accordance with its impact from the shallow to deep logic, we are divided into four stages: traffic, indicators, conversion and retention, and detailed description of the traffic situation, the following preparation for two articles, For everyone to explain engagement--such a large length of indicators, it is because it is very important, involved in all aspects of a lot of people, if we can patiently read, will be able to have a lot of harvest.

What you will read in this article: 1 what is engagement,2) indicators what metrics are generally included, what they mean, and 3 how they need to be monitored to accurately obtain indicators related measures.

Body】

What's indicators?

A long time ago I wrote an article introducing indicators: The most basic measure of website Analysis (8)--engagement, please refer to. Indicators does not mean a specific metric, but a set of metrics that measure the extent to which users are involved in a marketing campaign. Since the site is part of the marketing campaign, indicators is often used to measure the degree to which users interact with the content and functionality of the site. But indicators is not only that, it can also measure the other ways in which users interact with marketing activities, such as reading, commenting and forwarding of users in Weibo marketing, or the interaction of audiences and rich media advertising (Richmedia). Indicators is a meaningful indicator that can be understood in such a way that it measures the user's behavior and processes before and after the flow is generated, and in particular reflects the user's interest in marketing activities/websites and the factors that influence the eventual transformation.

So I can't give indicators a specific definition, it's an indicator system, not a specific metric, and it's not a standardized metric like visit. For this reason, the Americans (Avinash Kaushik and Eric Peterson) themselves have different opinions, one that it is worth popularizing to become a standardized metric, one that it should not be a metric, it is difficult to standardize the application. No matter who is more reasonable, in solving specific problems, indicators has its very obvious value, so in our core index system, I always think indicators is one of the most important.

Indicators can be divided into two categories, one is to standardize the measurement of user behavior of the target family, the other is based on the different circumstances on the basis of the definition of the target family. The two kinds of indicators have different meanings and similar functions, and they are very important.

Standardized Indicators Index

Standardized indicators indicators are divided into macro-level and micro-level. Macro refers to a site-wide indicators situation, while micro refers to a specific page of the indicators situation.

Macro indicators indicators are mainly known as the kind--bounce Rate (bounce rate), pv/visit and time on Site, these three indicators describe three different types of user behavior.

Bounce Rate

Bounce rate explains whether users have been interested in the content of the site after they enter the site. If not, then the user leaves without clicking on any of the links on the page, so he actually sees the landing page (landing page) that the site presents to him. Bounce rate is an indicator of the technological advances that have not changed much. Some friends asked me, if a person entered the landing page, he looked at the landing page for several minutes, but did not click on any of the above links to see other pages, he was bounce off? This is clearly stated in the bounce rate definition of most web analytics tools, That is, bounce or not is actually related to the time the user has viewed on the landing page, only if he or she clicks into another page. If there are clicks into other pages, then it is not bounce, otherwise even if the bounce, so the above situation regardless of how long the visitor looked at the landing page, and did not click on any of its links into other pages, which is still a bounce. So perhaps the definition of bounce rate is too strict, and the length of time to browse the page does not seem to be reasonable (and later will be devoted to the question of time). But the definition is technical simplicity and the "best solution" of the efficiency principle that captures the big probability event (viewing the page for several minutes without clicking on any of the links on the page is indeed a small probability event) and has been used.

Interestingly, a long time ago, Avinash's explanation for bounce was that it was less than 10 seconds (or 30 seconds, I didn't remember very well) on the page/website. However, because the user page browsing time is not easy to accurately monitor (or accurately monitor the technical implementation simplicity of the Web Analytics tool), and the alternative (the definition of the current bounce rate) can still fairly accurately describe the bounce situation in reality, As a result, most tools do not use browsing time as the basis for the definition of bounce and bounce rate.

The detailed definition and interpretation of Bounce Rate has been many, if not too much to understand or want to review the system, please read these articles: "The most basic measure of website Analysis (5)--bounce Rate", "Bounce rate--how much is good?" Challenge the public wisdom in Web Analytics (1)--bounce Rate ".

pv/v

Unlike bounce rate, pv/visit (or often abbreviated as PV/V) describes the interaction of another type of user with the site, that is, the depth of the browsing site. The more pages viewed by the user during one visit (visit), the more interested the user is in the site. So in general, the higher the pv/v the better. Of course, interest has the active interest and the passive interest points. Passive interest refers to the process of constantly trying to find what you want in a Web site, and the pv/v will be bigger, but it's not a good thing-but it's a rare phenomenon.

Time on Site

Now that we've talked about the depth of browsing, it's natural to have a browse length corresponding to it, that is, time on site, which refers to the average stay of people visiting the site. For example, a website has 3 visits, one stays for 2 minutes, one stays for 10 minutes, one stops for 0 minutes, then time on site is 4 minutes. As with pv/visit, generally speaking, this value is bigger and better.

It is worth noting, however, that the time spent on the Web analytics tool is definitely different from the time the actual user stayed on the site. The length of time people visit the last page of the site will not be counted by the Web analytics tool. The reason is simple, because the average web analytics tool does not count the exact time people leave a site, but only the exact time of the second page of the site, so the last time the page he visited is actually completely ignored. You will ask why not count the last page to stay on the time?—— because the site Analysis tool defaults to users to close the page behavior, or from this page browser window to jump to other sites do not do statistics, unless you make a special setting.

If you do not make additional settings, this arrangement means two points, first, the Website analysis tool statistics to the site browsing time is always less than the site was opened in the browser time (although the browser open the page does not mean that you are really every minute to see it); second, all bounce visit ( That is, a visit that accesses only one page) stays on the Web site for a total of 0.

There are some web analytics tools that break this rut and try to keep track of when people leave the site. However, I personally feel that the meaning is not particularly large, unless the last page of each visit is a great chance for those who specifically need to do more to stay in the careful view of the page. As long as the method of tool statistics is consistent, even if the last page is less than the remaining time, still can achieve apple to Apple comparison, still can help us grasp the user macro indicators situation. It also reduces the difficulty of technology implementation and increases monitoring consistency, which improves monitoring accuracy (since the method of recording the exact moment away from the site is not actually completely reliable, and only a certain probability can be counted, this makes the actual usability of these methods less).

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Visit/uv and other user stickiness indicators index

One of the macro indicators indicators is not commonly used, which is the metric we mentioned earlier VISIT/UV. It is used to measure the stickiness of visitors visiting the site. If you like a site, you will often come, a UV will take a number of visits. The higher the number of VISIT/UV, the higher the user loyalty of the site.

Other indicators metrics that indicate user stickiness, such as access frequency distributions (do a graph), and access interval distributions (do a graph). I've never used these two metrics very much, and I think the best way to read them is to compare the different websites and compare them to their own psychological expectations.

Figure: Access Frequency

Figure: Access time interval

Micro-level Indicators index

Micro-level Indicators index I do not want to talk too much, in essence is to describe the user's behavior on specific pages, the more important one is exit rate. Exit Rate is a measure of the probability of a page being viewed as the last browsed page before the user exits the site (compared to its overall number of visits). For example, the exit rate for a page is 75%, which means that all PV that is generated by accessing this page, Three-fourths of PV is the last browsing page before these visits exit the site. See this article for a detailed explanation.

What I want to say here is that the exit rate is a more microscopic description of the page indicators metric (which is a measure of the overall station indicators), a measure of the performance of the page, a similar metric and average time on Page,next page Flow (as well as the heat generated by it), and so on, a lot of related articles, no longer with the detailed introduction, if interested, please click on the previous links.

Indicators metrics on Demand

Standardized indicators metrics describe multiple user behaviors but are not sufficient to cover more specific measurement and analysis requirements. For example, a site has some very important specific user behavior (action), such as registering or logging in, applying for a trial opportunity, downloading a product description, or adding a certain amount of merchandise to a shopping cart. For these specific action, standardized indicators metrics do not actually take any extra care of them.

This time we need custom indicators metrics to describe the specific user behavior that is valuable. There are two types of behavior:

1. Non-standardized behavior: These actions mentioned above, such as registration, login, trial, download, click on a particular location or function, add to the shopping cart and so on, belong to this category.

2. More targeted user behavior as required for standard indicators settings. For example, the visit length of more than 3 minutes is a more specific user behavior than a single visit, or the visit of more than 3 browsing pages in visit is also a more specific user behavior. In addition, you can set up access to a particular page of the visit, also belong to the defined conditions of the user behavior. Depending on the criteria you set, the value of the corresponding metric is different.

You will find that these indicators have a rather "arbitrary" nature. Yes, they are indeed freely defined according to your needs, which means that the indicators indicators used by others may be completely different from yours. But we do need these indicators, otherwise we can not fully describe the characteristics of user behavior and value, can not be targeted analysis and optimization.

The existence of the indicators index on demand makes the network marketing analysis can really match the business. Otherwise, simply using visit or bounce rate to measure traffic and user behavior is too sketchy.

Now that your problem may arise--since these metrics are custom--then there must be no uniform standard report on the Web Analytics tool that provides their data, how do we get that data?

Monitoring and realization of indicators index on demand

Don't worry, any indicator can be a necessary condition for an indicator that it can be monitored first. If it cannot be monitored, the value of its existence is lost, and that is what is called-immeasurable, non-existent.

Custom indicators metrics must be able to be monitored. Web analytics tools actually provide a very comprehensive approach. It is divided into the following categories:

1. The user's action is to click on the link to open a new page:

This situation does not actually require us to implement additional monitoring tools because clicking the link opens a new page that will record the new PV for the new open page. So we count the PV on the newly opened page to know the number of times the user clicks on the link. Of course, the number of clicks and pages Open is not 100% corresponding, but has been very close, completely does not affect our analysis.

Download the data in Excel and then do a filter to record the data you think belongs to the indicators page, bingo!

If you click the same link each time, the open page is not a static URL of the page, but each URL is not the same dynamic page. This is also OK, we can filter the settings (such as GA filter settings) to the URL is not the same dynamic page to unify the same URI, so that the GA in the record will not be considered as a lot of pages, but will be recorded as a page. However, this method must have a prerequisite, that is, the dynamic page URL is a certain format, that is, at least some common, completely random URL there is no way. The implementation of the method this article is limited to the length of the detailed, I am ready to write a quick little article introduced to you, please look forward to.

2. The user's action is to click on the link does not open a new page, but to open some specific features:

These features include: After clicking on the JavaScript or div floating layer, click on the Flash, click on the outside of the chain and so on. In these cases, we need to configure our GA monitoring code.

1 Click on the object is JavaScript or div floating layer:

Use the event tracking function (Official document, English) or Virtual page feature (official description, English). The principle of this method is to add event tracking or virtual page calls to the OnClick event of the click Action itself. For example:

The difference between event tracking and virtual page is that the former puts the click action record in the GA event report, which treats the activation of the action as a page record and displays it in the content report. These two methods are the methods that GA learns must master. If you need, I will write a special article to introduce these two methods, if necessary please leave a message below.

2 Click on the object is flash:

Thinking is similar to the above, you need to use event tracking or virtual page functionality, but write the appropriate method into flash. Some complex, need technical colleagues to help solve. See this article for a better solution.

3 Click on the object is outbound link:

The official approach is similar to the method of monitoring JavaScript or Div, which is to make an event tracking or virtual Page of the outbound link (outbound links) Click Behavior (onclick event). This requires each outbound link to do the OnClick event reference, and add event tracking and other methods. Please see here: http://support.google.com/analytics/bin/answer.py?hl=en&answer=1136920 (English). This method is quite cumbersome if there are many outbound links on the page.

A one-time solution is also present, as described in this article: http://wptheming.com/2012/01/tracking-outbound-links-with-google-analytics/. I didn't try it myself, but looking at the code should be achievable.

Summary of this chapter:

This chapter only does three things: explain what indicators is, what metrics are included, and how you need to monitor indicators related metrics. It is worth remembering that indicators contains standard metrics, as well as customized metrics, which may require reprocessing of the tool's code for customized metrics.

In the next chapter we continue to move around the indicators, just to get into the more "core" areas. It includes some computational methods of indicators, indicators interpretation and its application in analysis. Please look forward to it.

If you have any questions or thoughts, please leave a message below. Finally, I wish the Friends of Beijing to carry the fog, self-improvement do not suck! I wish our friends a Happy new week!

This article copyright belongs to "website analysis in China CWA" and its author sing

Original link: http://www.chinawebanalytics.cn/web-marketing-key-metrics-and-logic-2/

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The core index of network marketing effect measurement what kind of logical thinking do we use?