Shulou(Shulou.com)06/02 Report--

This article mainly introduces how to obtain and analyze data in the operation of the website, which has a certain reference value. Interested friends can refer to it. I hope you will gain a lot after reading this article. Let's take a look at it.

Refinement of operations and becoming particularly important, data-driven decision-making is a challenge that we operators have to face and a skill we have to subconsciously learn.

Data-driven operation is the trend of future operation, and it is also a watershed for our operators. when the slash-and-burn era of operation has declined, refined operation and become particularly important. Data-driven decision-making is not only a challenge that we operators have to face, but also a skill that we have to learn subconsciously.

But it is also a headache for many newcomers who have just entered the field of operation, because it involves data analysis methods, methodology, logical analysis capabilities and the use of some tools, and a pile of data is something that many operators do not want to face. In this section, we focus on how to obtain data, how to analyze data, and which data dimensions a product focuses on.

Where to get the data

Before we analyze the data, we have to have data for us to analyze, so we have to get the data, how do we get it?

There are two main sources of data:

Own data analysis system-the company's own data is the most source of data, but also the most reliable and comprehensive. Generally speaking, if there are conditions, the internal data will prevail.

Third-party data analysis tools, this is with the help of external tools to obtain data.

Here are five main data analysis tools:

1. Friendly alliance

Support statistical analysis of iOS and Android application data

2.growingio

The power of growingio is that it can obtain and analyze comprehensive and real-time user behavior data without burying points, so as to optimize the product experience and achieve lean operation.

3. Applied radar

For iOS only, view the App Store general list and classified rankings. Check the search score of the product in App Store, one of the criteria to judge the effectiveness of ASO.

4. Baidu Mobile Statistics

Ios and android platforms are supported. In addition, after embedding Statistical SDK, developers can comprehensively monitor their own products, including user behavior, user attributes, regional distribution, terminal analysis and so on.

5. Cool biography

Only android platform application monitoring is supported. Developers can view data such as downloads, rankings, rating comments, keyword rankings and other data in the mainstream market, and systematically compare the data with similar competitors.

Of course, there are more than five data analysis tools, and if you are using other tools, you can also. Using analysis tools, we can get the following:

Record those clicks, including those that do not interact with the site; the percentage of links that can be generated directly, click on the distribution map and thermal map; count the user's hover and visualize the user's potential behavior

In fact, there are a variety of ways to obtain data, the key is that as operators to understand what kind of data is important, for the relevance of these data, this is a linkage process, not a single behavior.

With these data, how should we analyze the data? What is the amount that can be used by us, and which can be eliminated.

Second, how to analyze the existing data

After getting the data from a third-party data analysis tool or your own analysis background, how to analyze it? I believe that many operators do not have many ideas when they get the data. Either the eyebrows are scratched, or there is no way to start. All these are the manifestations of the lack of analytical ideas, which need to be guided by macro methodology and micro methods.

Methodology is often used in data analysis, and these methodologies play a macro-guiding role in data analysis. Therefore, when we carry out data analysis, we should first find a suitable methodology for guidance. The main methodology to be used:

1.PEST analysis: used to analyze the macro environment, including political, economic, social and technological.

2.5W2H analysis: Why, What, Who, When, Where, How, How much.

3. Logical tree analysis: a hierarchical list of all the sub-problems of the problem.

4.4P marketing theory: analyze the overall operation of the company, including product (product), price (price), channel (place), promotion (promotion) four elements.

5. User behavior theory: mainly used for website traffic analysis, such as visitors, newcomers, turnover rate, etc., in many indicators to choose some applicable.

6.AARRR (pirate rule of growing hackers): an important framework for lean entrepreneurship, which grows from five aspects: Acquisition, Activition, Retention, Revenue and Referral.

There are many methodologies for data analysis, which cannot be enumerated here; there is no best methodology, only the most suitable one. Next, I will introduce the AARRR methodology in detail, which fits very well with the problems of lean operation and business growth.

For Internet products, users have obvious life cycle characteristics. Let me take an APP as an example.

First of all, through a variety of online and offline channels to get new users, download and install APP. After installing APP, activate users through operational means, such as free first order, vouchers, red packets and so on. Through a series of operations to retain some users, and bring revenue to the enterprise. In this process, if users think the product is good, they may recommend it to the people around them, or encourage them to share it to their moments through incentives such as red envelopes. It should be noted that these five links are not exactly in the above order; operators can apply flexibly according to business needs. The five links of AARRR can be measured and analyzed by data indicators, so as to achieve the purpose of lean operation; the promotion of each link can effectively grow the business.

Use these data analysis methodologies to clarify their role:

● straightens out the analysis ideas to ensure the systematization of data analysis structure.

● breaks down the problem into related parts and shows the relationship between them.

● guides the development of follow-up data analysis.

● ensures the validity and correctness of the analysis results.

For example, we use trend analysis when analyzing the data dimensions of APP, because trend analysis is the simplest, most basic and most common method of data monitoring and data analysis. Usually we set up a chart or bar chart of the data indicators in the data analysis product, and then continue to observe, focusing on outliers. In this process, we should choose the first key indicator and not be confused by the vanity index.

If we take the number of APP downloads we analyzed as the first key indicator, we may go astray; because a user downloading APP does not mean that he is using your product. In this case, it is recommended to take the number of daily active users as the first key indicator, and only those users who have initiated and performed an operation can be counted; only such indicators have practical significance, and operators should focus on such indicators.

Third, which data dimensions does a product focus on?

We all know that operators deal with all kinds of data every day. Which product has those data dimensions that we often analyze?

The data index system of a product (especially APP) can be divided into: user scale and quality, channel analysis, participation analysis, function analysis and user attribute analysis.

1. The analysis of user scale and quality includes total number of users, number of new users, retained users, conversion rate. User size and quality are the most important dimensions of APP analysis, and its indicators are also the most relative to other dimensions. Product owners should focus on the indicators of this dimension.

two。 The main purpose of channel analysis is to analyze the changes and trends of channel quality in related channels, in order to scientifically evaluate channel quality and optimize channel promotion strategy. Special attention should be paid to channel analysis, because cheating in the mobile application market is an open secret in the industry. Channel analysis can compare the effects of different channels from multi-dimensional data, such as new users, active users, retention rate of the next day, duration of a single use, and so on. in this way, we can find the most suitable channel according to the data, so as to get the best promotion effect.

3. The main purpose of participation analysis is to analyze the activity of users, and the dimensions of the analysis mainly include startup times analysis, usage duration analysis, visit page analysis and time interval analysis.

4. Functional analysis mainly includes:

Functional activity indicators: active users of a function, usage; functional verification; data analysis of product functions to ensure the rationality of functional trade-offs.

Page access path: the page access and jump of the user at every step of the process from opening to leaving the application. The page access path is full statistics. Through path analysis, we can get the diversity of user types and the diversity of user purpose of using the product, restore the user purpose, subdivide the user through path analysis, and then return to the iteration of the product through user segmentation.

The funnel model is used to analyze the conversion rate of critical paths in the product to determine whether the design of the product process is reasonable and to analyze user experience problems. The analysis of user conversion rate, the core of the analysis of the reasons for the loss of each layer of funnel. Focus on the conversion rate of each step in the application and the impact of the conversion rate on the income level by setting custom events and funnels. Through the analysis of events and funnel data, we can optimize the steps with low conversion rate and improve the overall conversion level.

5. User attribute analysis is of great significance no matter in the initial stage of our product launch or in the adjustment of strategy. For example, before product design, we need to build user portraits to guide design, development and operation; product iteration process needs to collect user data, facilitate user behavior analysis, link with business model, and so on.

User attributes generally include gender, age, occupation, location, mobile phone model, and network usage. If you are interested in other user attributes, you can go to your Wechat official account or other backend, such as Toutiao or uc, to see which dimensions the user attributes contain.

The era of traffic-centric and barbaric operation is over, and the following era is based on scientific data, focusing on the refined operation era of users.

Thank you for reading this article carefully. I hope the article "how to obtain and analyze data in website operation" shared by the editor will be helpful to everyone. At the same time, I also hope that you will support and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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