Who picks up the soap for your app?

2048 miracles are the dream of most individual developers. and to make Flappy Bird such a cross-era game, we can make a pot full of money. So how do we make our app unique and stand out from the crowd.

We the ordinary programmers, for our app only focus on two kinds of indicators, one is the amount of downloads, one is the amount of activation. caused the "re-promotion, light operation," or even "there is promotion, no operation" situation. I think it's impossible for the app to create the most loyal user groups, or really fire up.

Do you think your users, after downloading your app, will become your real customers after installing the app? Not too? Even when he opens up your app, he's holding a skeptical attitude. Real is your customer, should be your app for him to bring unexpected effects, so interested in opening your app again, is your real users. To make our dick-wire programmer, better to recognize this. Here we need to introduce a AARRR model that explains his pattern behind.

① What is Aarrr Model

AARRR is acquisition (acquisition user), Activation (increased user activity), Retention (increased retention), Revenue (increased revenue), Refer (virus spread), the five words written, corresponding to the 5 key segments of this mobile application life cycle. Let's take a brief look at the meaning of each item in the AARRR model.

Get Users (Acquisition )
Have the user to talk about the operation, access to users, the actual is to promote, have users, have everything.
Increased activity (Activation )

Brush machine, advertising can bring us users, but how to make these passive users into active, high-quality users, this is a problem.

Sometimes, we can see two apps such a gap, even if you start to get to the same user, an app to put the user down, another app to open the user once, and never want to open. What is the cause of such a gap? I think this is mainly due to two factors. One, which retention rate app, is to seize the majority of users just need, and skillfully use the precise promotion of attracting target audience's eyeball. In fact, the first impression of the app, it is not like the college entrance examination of the teacher, a few 10 seconds of life and death, so a good product design, voluntary and will be for you to retain more users.

increase retention rate (Retention )

With the depth of time, the app customer activity is improved, but, we found that the problem, the user is really "come also hurriedly", this is the app is not sticky.

In fact, the cost of developing a new user is far greater than the cost of retaining the old user, but many apps have a case of the application operation of the bear to break the corn (take one, lose one). How can we solve this problem? is to improve his lazy on this app, how to improve dependence, tool class app to do is to try to improve the quality of the app, and constantly improve the function, the game app is a need to have good stories, play, make users addicted to such methods. In general, the commercial building app retention rate is higher than the entertainment app, the entertainment app is higher than the game.

get Revenue (Revenue )
Earning revenue is actually the most central piece of application operations. Few people develop an application purely out of interest, and most developers are most concerned about revenue. Even for free applications, there should be a pattern of profitability.

People do not for their own hindmost, the app's core goal is to make money. There are three ways to make money in general app apps, a paid app that downloads on charges, a value-added service, and some application fees, an embedded ad. No matter the type of charge, I think it's the right thing to do.

self-propagating (Refer )

The above four aspects, is the traditional mode of operation, with the rise of SNS, viral transmission, is to obtain users of a new world, this way, there are other operations incomparable advantages, one effect is very good, two cost is very low. Millet, Apple is the leader in this way. Visible to achieve viral marketing the only premise, is to learn a big sentence "Iron also need their own hard", your product should be good enough.
With this aarrr model, we see the acquisition of the user (promotion) is only the first step in the entire application operation, the show is still behind. If only see the promotion, do not pay attention to the other layers in the pipeline, let the user to fend for themselves, then the application of the future must be bleak.

② How to use Aarrr Model
First, get the user (acquisition)

Download volume, Installed Capacity, is an extensive standard to judge the success of the app.

At this stage, the first data you care about is the amount of downloads. Today, some media reports also often use downloads to measure the size and success of an app's users. However, downloading the app does not necessarily mean that it will be installed, and installing an app does not necessarily mean using the app. So soon the amount of activation becomes the most concerned data in this level, even the only data that some of the extension people are concerned about. The usual amount of activations (that is, the number of new users) is defined as the number of new standalone devices that have launched the app. Literally, the amount of activation seems to be the second-level activation indicator, but because of the amount of downloads, installs the data is more virtual, can not really reflect whether the user has been acquired. So everyone has to look at the activation, which is really getting to the new user.

Another very important data is the amount of activations that are counted by the channel. For example, Android's Big App Store, Apple AppStore. Because in the channel promotion, many application developers choose the pay promotion. When settling, it's natural to know how many users are actually active in a channel. Even without a billing relationship, developers need to know which channel is most effective. But at a higher level, CAC (user acquisition costs customer acquisition cost) is the data that most needs attention. There is a rough line in the industry that the acquisition cost for each Android user is around $4, while iOS users are about $8 or more. Of course, the cost of obtaining a variety of different channels, such as app market downloads, phone presets, and advertising, is completely different. There is a cost-effective problem, some channels to obtain higher costs, but the user quality is also relatively high (what is called high quality, the following will be explained).

Ii. increased activity (Activation)
To see the activity, we will first think of the indicator is dau (daily active users), MAU (monthly active users). These two data basically illustrate the application of the current user base size, in the online game industry This is the two operators must see the indicator. Typically active users are users who have started within a specified period of time. But is activation really equal to active? If it is started only once during the specified period, and the time is short, such user activity is not very high (of course, for some special applications may be high, for example, to record the female physiological cycle of the application, starting in January is enough). So we actually have to look at another two indicators: average duration of use per start and average daily boot times per user. When these two indicators are in an uptrend, it is certain that the user activity of the application is increasing.
It is also important to use channel statistics for the duration and number of launches. We call them the quality data of the channel, and if the two indicators are poor in a channel, then it is meaningless to invest too much in this channel. The most typical is the user of parallel brush machine, many of the pre-set applications are activated when the brush is completed. For this passive activation of the user, you can look at another indicator, called a one-time start user number, that is, the number of users who have only been launched so far.
In addition to the channel, another and active degree-related analysis dimension is version. There are also differences in the length of use and number of launches for each version. For product managers, analyzing different versions of the activity difference helps to improve the application continuously.
In addition to the activity, there are daily activity rates, weekly activity rates, and monthly activity rate indicators. Of course, the activity rate and the category of application is very related, such as desktop, power-saving applications of the active rate is higher than the application of the dictionary class.

Iii. Increased retention rate (Retention)
Download and install--use--uninstall or forget, which is the user's life cycle in each app. Successful applications are those that maximize the user's life cycle, maximizing the user's value during this life cycle (the topic of the next session to life cycle value).
For most applications, the 1-day Retention and 7-day Retention should be of concern. Here I use English, because its Chinese translation is not uniform, easy to cause ambiguity. 1-day retention is usually translated as the first day retention rate, in fact, this "debut" does not mean that the application is installed to be used on the initial days (assuming the date is D), but D+1 day, that is, the second day of installation use. Because the first day of installation is not the concept of retention (only 100%, if any). By the next day, the number of users who installed the user in the previous days was still starting to use the app, which is 1-day Retention. Because it is the next day, some articles are also called "the next day retention rate". Similarly, 7-day retention is the percentage of the total number of users who started using the app on the D+7 day for the first time that the application was installed using this app. Usually the first few days after the user's new installation is the most draining period (for details on user retention, please refer to our colleague's other blog, "read your user retention"). Therefore, these two indicators in the retention rate analysis is the most important. There have been experts in the game industry pointed out that if you want to become a successful game, 1-day retention to reach 40%, 7-day retention to reach 20%.
Some applications do not need to be launched daily, so the weekly retention rate, the monthly retention rate and other indicators will be more meaningful. The retention rate is also an important indicator of the quality of the users of the channel, and if the first-day retention rate of one channel in the same application is much lower than other channels, the quality of the channel is relatively poor.

Iv. Income Acquisition (Revenue)
The most familiar indicator of income is the value of ARPU (average per user income). The corresponding comparison of the less mentioned is also called Arppu (average per paid user income). Is Arppu high, ARPU will certainly be high? The answer is not necessarily. Because there is another indicator is the proportion of paid users, that is, the proportion of the total users of paid users. If the percentage of paid users is low, the average of those revenues will be lower for all users. In general, if a game increases the price of a virtual prop in order to improve Arppu, the percentage of paid users will be reduced accordingly. Find a balance between a arppu and a paid user ratio to maximize revenue.
But income is not the most important, profit is. How to maximize profits? The most streamlined formula for profit is: Profit = income-cost. First we look at the cost, we mentioned in the previous article CAC (user acquisition cost). In addition, there are development costs for the application itself, server hardware and bandwidth costs, and operational costs. However, in the case of large user size, CAC will be the most important cost, and other costs are not an order of magnitude, so we in the subsequent discussion only consider CAC. So how is the income calculated? ARPU is a time-related indicator (usually up to a monthly ARPU value) and does not correspond fully to CAC because CAC is not directly associated with time periods. So we have to look at one more indicator: LTV (life cycle value). The user's life cycle is the period between a user starting the application for the first time, and the last time the app is started. LTV is the total amount of revenue that a user creates for the application over the life cycle, which can be seen as a long-term cumulative ARPU value. Average LTV per user = monthly ARPU * User average life cycle per month.
The difference in LTV–CAC can be regarded as the profit that the application obtains from each user. Therefore, maximizing the profit becomes how to increase the LTV while reducing the CAC, so that the difference between the two is maximized. Further, the different channel source users do the dating analysis, according to their different CAC and LTV, can deduce the different sources of profit margin difference.

V. Self-Propagation (Refer)

Self-propagating, or viral marketing, is a marketing approach that has been extensively researched in the last 10 years. Although we have heard of some classic viral marketing cases, but how to quantify the effectiveness of the evaluation, but few people know the K-factor (K-factor) this measure. In fact, the term K factor is not originated in the market or software industry, but from the science of infectious diseases-the right, is to study the real virus transmission of the sciences. K-Factor quantifies the probability of infection, that is, a host infected with the virus can be exposed to all the host, how many hosts are infected with the virus.
The formula for K-factor is not complicated, k = (the number of invitations each user sends to his friends) * (the person receiving the invitation translates into the conversion rate of the new user). Suppose the average user sends an invitation to 20 friends, and the average conversion rate is 10%, K =20*10%=2. The result is a good one--when k>1, the user base grows like a snowball. If k<1, then the user group to a certain size will stop through self-propagating growth.
Unfortunately, even in mobile applications with social classes, the current K-factor is more than 1. So the vast majority of mobile applications are not entirely dependent on self-propagation, but must also be combined with other marketing methods. However, from the product design stage to join in favor of self-propagating function, or is necessary, after all, this free promotion method can be partially reduced CAC.

Above we have listed inApplication PromotionSome of the metrics that you need to focus on at each level of your operations (at each stage). In the whole AARRR model, these quantitative indicators have a very important position, and many indicators of influence is across multiple levels. Timely and accurate access to specific data on these indicators is essential to the successful operation of the application.

With these theoretical guidelines, I hope that the app no longer soy sauce, pick up a few pieces of soap and get rich together.