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This article will explain in detail the example analysis of digital user operation. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

The necessity of establishing a digital user operation is to quantitatively measure the value of your work, and the second is to achieve the basis of fine operation.

Flow and refinement are the basic thinking that every operator should have. If flow thinking is the operator's qualitative thinking on the operation goal, then data is a quantitative description of the path and effect of achieving this goal. it implements your work ideas on specific data indicators to measure your work effectiveness and goal achievement.

The necessity of establishing a digital user operation is to quantitatively measure the value of your work, and the other is to achieve the basis of fine operation. For example, the hierarchical classification and profile of users based on data mentioned later is the premise of fine operation.

Digital user operation is to make use of the idea of user operation, combined with the idea of data analysis, business guidance data, data-driven business, to achieve fine operation of users, which is the core idea of digital user operation. The cycle process of user operation digitization is as follows: user data collection-construction of user data operation index system-data-driven operation.

I. user data collection

The collection of user data mainly includes user basic data, user behavior data and user traffic data.

The basic user data refers to the static data of the user, including gender, age, region, work, etc., this kind of data describes who the user is, mainly by filling in the basic information. User behavior data is a collection of a series of user actions on the product, which user completed what kind of operations in which time, where and in which way, including user browsing, purchase, content contribution, invitation dissemination, social networking and so on. This kind of data describes what the user has done, which is mainly realized by data hiding. User traffic data is the source of the user, which is generated based on the web page that the user visits, including device, operator, port, time and so on. This kind of data describes where the user comes from. However, the current traffic data statistics mainly come from third-party tools such as GA and Baidu statistics, which cannot be recorded in the database, that is, they cannot correspond to the above-mentioned user basic data and behavior data one by one.

The above data are the original data obtained from products or third-party tools. In order to achieve the operational goals, we also need to do data mining and data analysis on the basis of the original data, combined with operational goals and paths to build a data operation index system.

Second, construct the index system of user data operation.

If you can't describe the business in terms of indicators, then you can't grow it effectively. So what you need to do in this section is to index your business. The data index is not constant, it depends on the business process or functional process of your product, and is closely related to the goal and the path to achieve it.

The purpose of user operation is to maximize user value, if you are an e-commerce product, then your goal is to get users to pay for goods, if you are a community product, then your goal is to get users to contribute content. However, the realization of product goal and user value is not only a gradual process, but also a dynamic evolution process, some from potential users to active users, some from active to loss, and some from loss back to active.

In the image above, orange is the dynamic evolution of user status, and red is the operational goal. Along the operation idea of goal-path-effect, data analysis is to split your goal on specific data indicators as the core inspection index, and use the data to monitor the way to achieve the goal to evaluate the effect. Compare the original core inspection indicators to judge, verify, revise and optimize the work path, so as to achieve better and faster results. According to this idea, we build the following data operation index system, each system contains a series of related indicators. The construction of the index system is realized through data processing and processing on the basis of the user data collected in the first part.

1. In the customer acquisition link from potential users to registered users, what we need to do is to analyze the customer access channels and the promotion strategies adopted in each channel, evaluate the channel quality through data indicators, and optimize the channel promotion strategy. The data indicators mainly include the number of new users, the cost of user acquisition, and the retention rate of new users.

Number of new users: new users refer to the users who start the application for the first time after installing the application. According to the statistical time span, it is divided into daily, weekly and monthly new users. The index of new users is mainly the most basic index to measure the effect of marketing promotion channel.

User acquisition cost: the conversion rate of the response channel for the payment channel.

New user retention rate: reflects the quality of new users and the degree of fit with the target users. In addition, for the mature version of the product, if there is a significant change in the user retention rate, it means that the user quality has a significant change, which is probably caused by the change in the quality of the promotion channel.

Channel A: SEM

Channel B: Weibo

two。 The promotion and retention of registered users and active users is one of the most important tasks of operators. Our daily user hierarchical classification and user growth incentive system are all done in this link. Reflected in the data, the index system that we can set up includes a system for understanding the size and quality of users, a system for understanding user participation (depth of use), and a user profile system for understanding user attributes.

(1) scale and quality of users

Active user indicator: active users refer to the number of devices that have launched applications (APP) in a certain statistical period. Active users are the index to measure the size of application users. Usually, whether a product is successful or not, if you look at only one indicator, this indicator must be the number of active users. The number of active users can be divided into daily active users (DAU), weekly active users (WAU) and monthly active users (MAU) according to different statistical periods.

New user indicators: the new user volume index mentioned earlier is the main index to measure the effectiveness of the promotion channel; in addition, the proportion of new users to active users can also be used to measure the health of the product. When the ratio is too high, special attention should be paid to the retention rate.

User retention rate indicator: the user retention rate refers to the percentage of users who still start the application after a period of time among the new users in a certain statistical period. The user retention rate can focus on the retention rate of the next day, 7th, 14th and 30th. Retention rate on the one hand reflects the quality of users, on the other hand also reflects the attractiveness of products. When the retention rate is abnormal, you can find the reason in these two aspects.

User composition indicators: user composition is an analysis of the composition of active users in the statistical cycle. Taking weekly active users as an example, weekly active users include return users this week, continuous active n-week users, loyal users, and so on. It is helpful to understand the health of active users through the structure of new and old users.

Index of active days per user: the average number of active days per user in the application during the statistical period. If the statistical period is relatively long, such as a statistical period of more than one year, the total number of active days per user can basically reflect the number of days spent on APP before the loss, which is an important indicator of user quality, especially user activity.

(2) user participation

User participation system is an important index system to measure user activity. The definition of being active in different products is different, for example, activity in e-commerce products can be defined as purchase, and activity in community products can be defined as content contribution. So the following three indicators can evolve differently in different products.

Number of starts = number of purchases = number of content contributions

Last use = recent consumption = recent content contribution

Duration of use = consumption = content contribution

Use interval = purchase frequency = content contribution frequency.

Startup times: refers to the number of times the user started the application in a certain statistical period. In data analysis, on the one hand, we should pay attention to the trend of the total number of starts, on the other hand, we need to pay attention to the number of starts per capita, that is, the ratio of the number of starts in the same statistical cycle to the number of active users, such as the number of daily starts per person, it is the ratio of daily starts to the number of daily active users, which reflects the average number of starts per user per day.

The most recent use: the last time the user used the time from now, through the analysis of dimension and distribution, it can also reflect the activity to a certain extent.

Duration of use: refers to the total time spent from the start of APP to the end of use in a statistical cycle. The duration of use can also be analyzed from the point of view of per capita use time (the ratio of total use time to the number of active users), single use time (total use time and start-up times), etc., which is an important index to measure product activity and product quality.

Usage interval: the usage interval refers to the time interval between two launches adjacent to the user. We usually analyze the distribution of time intervals, and generally count the number of active users who use time intervals within a month. The problem of user experience can also be found through the differences in the distribution of usage intervals in different statistical periods (different time points but the same span).

Visit pages: the number of pages visited refers to the number of pages that the user starts to visit at a time. We usually analyze the distribution of the number of pages visited, that is, the distribution of active users of the number of pages visited by applications within a certain period (such as 1-2 pages, 3-5 pages, 6-9 pages, 10-29 pages, 30-50 pages, and more than 50 pages. At the same time, we can find the problems of user experience through the differences in the distribution of visit pages with different statistical periods (but with the same statistical span, such as 7 days).

Among the above user participation indicators, we can select an indicator that can reflect the main operation objectives, such as consumption, build a user level model (user layering), or we can select multiple related indicators, such as the last consumption time R, consumption frequency F, consumption M to build a commonly used RFM user model.

The function is to make targeted operation strategy or user incentive system according to the characteristics of users in different levels (user layering) or different regions (RFM model) in the model.

Take the question and answer community as an example, the main KPI is the quantity and quality of content, which is reflected in the recognition number of content contributed by users. Through data collection and collation, it is concluded that the distribution of user approval number is as follows, and we use the user recognition number as an index to establish user layering.

It can be seen that the distribution is similar to the logarithmic normal distribution. The first, second and third quartiles are defined as critical values by similar distribution histograms, and users are divided into four levels: ordinary users, content producers, content contributors and big V users.

When the number of users is large enough, the user characteristics in each user level also show great differences. For example, in the content contributor layer, some people mainly publish articles with low frequency and high approval number per article; some people mainly ask and answer questions with high frequency and low approval number per article; this is combined with the RFM model to subdivide the users in each layer.

For example, some people are less than 3 years, some people are more than 5 years, some people like social content, some people like e-commerce content, this can be combined with the user profile described below to do a more detailed attribute description of users, to achieve a more refined operation effect.

RFM model

This is the end of the article on "sample Analysis of Digital user Operation". I hope the above content can be helpful to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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