User operation: AARRR user model and traffic pool model explained in detail!

1. What is the AARRR user analysis model?

AARRR is the abbreviation of Acquisition, Activation, Retention, Revenue, and Refer, which correspond to the five important links in the user life cycle. Acquire users - increase activity - improve retention - obtain revenue - self-propagation.

Acquisition

The first step in operating a mobile application is undoubtedly to acquire users, which is what we usually call promotion. Without users, there is no operation.

Q: What data are you concerned about at this stage?

1. Activation amount. Downloading an app (download volume) does not necessarily mean that it will be installed (install volume), and installing an app does not necessarily mean that it will be used. Therefore, activation volume has become the data that everyone is most concerned about at this level. Literally speaking, activation volume seems to be an indicator of the second-level activation, but because data such as download volume and installation volume are relatively virtual, they cannot truly reflect whether the user has been acquired. So everyone needs to look at activation, which is the real way to acquire new users.

2. Activation volume by channel statistics. Because when promoting through channels, CAC (Customer Acquisition Cost) is the data that needs the most attention. Of course, the acquisition costs of various channels such as app market downloads, mobile phone pre-installation, and advertising are completely different. There is a question of cost-effectiveness here. The acquisition cost of some channels is relatively high, but the user quality is also relatively high.

Q: What is the activation amount?

Usually, activation volume (i.e. the number of new users) is defined as the number of new independent devices that launch the application.

Increase activity

Users enter the application through different channels such as terminal pre-installation (flashing) and advertising. These users enter the application passively. How to convert them into active users is the first problem operators face.

Q: What are the factors that convert into active users?

1. The quality of promotion channels.

2. Whether the product itself can capture the user within the first few tens of seconds of use. No matter how meaningful the application is, if the first impression given is not good, blind dates will be the same.

3. Provide good tutorials to attract new users, which is especially prominent in the gaming industry.

4. The version used. The usage time and number of launches of each version will also vary. For product managers, analyzing the differences in activity between different versions helps to continuously improve the application

Q: What are the key indicators of activity?

1. The indicator is DAU (daily active users).

2. MAU (monthly active users). These two data basically illustrate the current size of the application's user base. Usually, active users refer to users who have activated the application within a specified period, but does activation really mean activity? So there are actually two other indicators to look at:

3. Average usage time for each startup.

4. Average number of daily launches per user.

5. For these passively activated users, we can look at another indicator, called the number of one-time activated users, which is the number of users who have only activated once so far.

Q: What are good and bad channels?

1. Poor promotion channels result in a large number of one-time users, those who will never use the service again and cannot be considered real users.

2. Good promotion channels specifically define the target population, and the users they bring in are highly consistent with the target population set when the application is designed. Such users are usually more likely to become active users. In addition, when choosing a promotion channel, you must first analyze the characteristics of your application (for example, whether it is a niche application) and the target population. What is a good promotion channel for others may not be suitable for you.

Q: What are the quality assessment indicators of the channel?

1. Length of use.

2. Number of starts.

3. Retention rate. It is also an important indicator to test the user quality of the channel. If the first-day retention rate of a certain channel of the same application is much lower than that of other channels, then the quality of this channel is relatively poor.

Improve retention

After solving the problem of activity, the next step is to solve the problem of user stickiness. Because the cost of retaining an old customer is much lower than the cost of acquiring a new customer, the situation of a bear picking corn (taking one and throwing one away) is a taboo in application operations.

Many applications do not really know when users churn, so on the one hand they continue to attract new users, while on the other hand they continue to lose a large number of users.

Download and install - use - uninstall or forget, this is the user life cycle in every application. Successful applications are those that can extend the user's life cycle as much as possible and maximize the user's value during this life cycle.

Q: What are the retention rate monitoring indicators?

1. Daily retention rate, weekly retention rate, and monthly retention rate.

2. Retention rate is also closely related to the type of application. Generally speaking, the first-month retention rate of tool apps may be generally higher than that of game apps.

Q: What do the 1-Day Retention and 7-Day Retention that everyone is concerned about mean?

1. 1-Day Retention is usually translated as the first-day retention rate. In fact, this "first day" does not refer to the first day the app is installed and used (assuming the date is D), but D+1, which is the second day of installation and use. On the second day, what percentage of users who installed and used the app the day before are still using it? This is 1-Day Retention. Because it is the second day, some articles also call it “next-day retention rate”. 2. 7-Day Retention refers to the percentage of users who started using the app on D+7 to the total number of users who first installed and used the app on D.

Q：Do all applications need to consider 1-Day Retention and 7-Day Retention?

uncertain. Some applications do not need to be launched daily. In that case, it would be more meaningful to look at indicators such as weekly retention rate and monthly retention rate.

Revenue

It is the core part of application operation. The essence of business is to make money.

Q: What is the main source of income?

1. Paid application.

(Paid apps are not widely accepted in China, including the Google Play Store, which only promotes free apps in China. In China, advertising is the source of income for most developers, and in-app payments are currently more widely used in the gaming industry.)

2. In-app payment.

3. Advertising.

Revenue comes from users - the user base needs to be increased - so increasing activity and retention rates are essential for generating revenue.

Q: What are the evaluation indicators regarding income?

ARPU (Average Revenue Per User) value.

ARPPU (Average Revenue Per Paying User).

Q: If ARPPU is high, does that mean ARPU will definitely be high?

uncertain. Because one of the indicators is the proportion of paying users, which is the proportion of paying users among all users. If the proportion of paying users is low, then the average value of those revenues spread over all users will be low. Generally speaking, if a game increases the price of virtual props in order to increase ARPPU, the proportion of paying users will decrease accordingly. Only by finding a balance between ARPPU and the proportion of paying users can revenue be maximized.

Q: But revenue is not the most important thing, profit is. -How to maximize profits?

The simplest formula for calculating profit is: Profit = Revenue - Cost.

Q: Cost?

Cost = CAC (user acquisition cost) + development cost of the application itself + server hardware + bandwidth cost + operating cost, etc.

(However, when the number of users is large, CAC will become the most important cost, and other costs are not of the same order of magnitude, so we will only consider CAC in the subsequent discussion.)

Q: Revenue and profit?

ARPU is an indicator related to the time period (usually the most common value is the monthly ARPU value), and it cannot be completely corresponded to CAC because CAC has no direct relationship with the time period. So we need to look at one more indicator: LTV (lifetime value). The user life cycle refers to the period from the first time a user launches an app to the last time the user launches the app. LTV is the total revenue generated by a user for the app during his or her lifetime, which can be regarded as a long-term accumulated ARPU value. Average LTV per user = Monthly ARPU * Average life cycle of the user in months.

The difference between LTV and CAC can be considered the profit that the app makes from each user.

Therefore, maximizing profits becomes about how to reduce CAC while increasing LTV to maximize the difference between the two. Furthermore, by conducting a generational analysis of users from different channel sources, the difference in profit margins of different channel sources can be deduced based on their different CAC and LTV.

Self-propagation (Refer)

The previous operating model ended at the fourth level, but the rise of social networks has added an aspect to operations, which is viral spread based on social networks, which has become a new way to acquire users.

Q: What are the advantages and disadvantages of this approach?

Advantages: The cost is very low and the effect is potentially very good; from self-propagation to acquiring new users again, application operations form a spiral upward track, and by using this track, they can continuously expand their user base.

Disadvantages: The only prerequisite is that the product itself is good enough.

Currently, most mobile applications cannot rely solely on self-propagation, but must be combined with other marketing methods. However, functions that facilitate self-propagation should be added from the product design stage.

Q: How to quantify and evaluate the effectiveness of viral marketing?

K-factor is a measurement indicator.

In fact, the term K-factor did not originate from marketing or the software industry, but from epidemiology - yes, the science of studying the spread of real viruses. The K factor quantifies the probability of infection, that is, how many hosts will be infected by a host that is already infected with the virus among all the hosts it can come into contact with.

Q: What is the calculation formula for K factor?

K = (the number of invitations each user sends to his friends) * (the conversion rate of people receiving invitations into new users).

Assuming that on average each user invites 20 friends and the average conversion rate is 10%, K = 20*10%=2.

When K>1, the user base will grow like a snowball. If K<1, then the user base will stop growing through self-propagation when it reaches a certain size. Unfortunately, even for social mobile applications, there are very few with a K factor greater than 1.

2. 4 types of traffic pools

In fact, in the Internet industry, "users are traffic". Based on the traditional AARRR model, I share 4 traffic pool models. My understanding is that this is also an operational idea for traffic (users). The key point lies in the fission link.

Funnel Model 1.0: Funnel

Funnel Model 2.0: Tea Bowl

Private Domain Traffic Pool Model 1.0: Big Fish Tank

Private Domain Traffic Pool Model 2.0: Small Fish Tank x N

• The above is a detailed explanation of the four traffic pool models. If you have any questions, you can leave me a message and we can communicate with each other.

Related reading:

1. How to use the AARRR model to attract 150,000 fans in 10 days at 0 cost?

2.0 cost 150,000 fans in 10 days. It turns out that the AARRR model can be used in this way!

3. Discuss product operation strategy through AARRR model

4. AARRR Model: Gamification User Growth Strategy

5.AARRRR model: the underlying logical model of the marketing process!

6. Case analysis: How to use the AARRR model to increase user growth?

Author: Miss Tuesday

Source: Tuesday Qianjin