Uncover the secrets behind application promotion operations

In the process of contacting a large number of mobile application developers, we noticed a phenomenon: many developers only pay attention to the application of downloads and activation, they see these indicators as a successful application of the logo. So many applications have been heavy promotion, light operation, or even promotion, no operation of the situation. But at what point does a person really become a user of an app? Did he decide to download the app? Or did he install the app? In fact, none. Even when he started and entered the app, he didn't really become a user of the app--usually with a skeptical attitude. Only when he felt that the application met (or could have exceeded) his expectations, or at least was interested in entering the application experience again, did he really become the user of the app. To help those mobile app developers understand this, we often explain the operating model behind a mobile application using the AARRR model below. What is the AARRR model AARRR is the acquisition, activation, Retention, Revenue, refer, the five words written, respectively, corresponding to the mobile application lifecycle of the 5 important links. Let's briefly explain the meaning of each item in the AARRR model. Getting users (acquisition) to operate a mobile application is the first step, there is no doubt that access to users, that is, generally speaking of the promotion. If there is no user, there is no operation. Increased activity (activation) Many users may be through the terminal preset (brush machine), advertising and other channels into the application, these users are passively into the application. How to turn them into active users is the first problem the operator faces. Of course, this is an important factor in the promotion of the quality of the channel. Poor promotion channels bring a large number of one-time users, that is, the kind of startup, but no longer use the kind of users. Strictly speaking, this is not really a user. Good promotion channels are often targeted to target groups of people, they bring users and the application design of the target audience has a great degree of coincidence, such users are often more likely to become active users. In addition, the selection of promotional channels must first analyze the characteristics of their application (for example, whether small audience applications) and the target population. It is a good promotion channel for others, but not necessarily suitable for you. Another important factor is whether the product itself can capture users within the first 10 seconds of use. Again have the connotation of the application, if give a person's first impression is not good, also will blind date failure, become married not to go out of the long-standing. In addition, some applications will attract new users by experiencing good novice tutorials, especially in the gaming industry. Increase retention rate (Retention) Some applications have found another problem after solving the problem of active degree: The user comes fast and walks quickly. Sometimes we say this app has no users.Viscous。 As we all know, the cost of keeping an old customer is usually much lower than the cost of acquiring a new customer. So bear break corn (take one, lose one) is the application of the operation of the taboo. But many applications do not know what time the user is lost, so on the one hand they constantly open up new users, on the other hand, and constantly have a large number of users lost. To solve this problem, we first need to monitor the user's loss by daily retention rate, weekly retention rate and monthly retention rate, and take corresponding means to motivate these users to continue using the application before the user is lost. The retention rate is also related to the type of application. Generally, the first-month retention rate for utility applications may be generally higher than the first month of the game class. Earning income (Revenue) income is actually the core of the application operation. Very few people develop an application just out of interest, most developers are most concerned about income. Even for free applications, there should be a profit model. There are a number of sources of income, mainly three: paid applications, application fees, and advertising. The acceptance of paid applications at home is low, including the Google Play Store in China, which only pushes free apps. At home, advertising is the source of revenue for most developers, and application fees are currently used in the gaming industry more. Either way, revenue comes directly or indirectly from the user. As a result, the increased activity and retention rates mentioned above are essential to income generation. The user base is big, the income is possible on the quantity. Self-propagating (refer) the previous operations model ended at the fourth level, but the rise of social networking has added one aspect of operations, the viral spread based on social networks, which has become a new way to get users. The cost of this approach is very low, and the effect is likely to be very good; the only prerequisite is that the product itself should be good enough to have a good reputation. From self-propagating to acquiring new users again, the application operation forms a spiral trajectory. And those excellent applications make good use of this track, and constantly expand their own user groups. With this aarrr model, we see that getting users is just the first step in the entire application operation, and the fun is still behind us. If only to see the promotion, do not attach importance to the other levels in the pipeline, allowing users to fend for themselves, then the application of the future must be bleak. How to use the AARRR model usually in the application, the headache is the background statistics of the activation volume than the channel to provide a lot less download. But a few days ago, a friend of mine consulted me about a sudden surge in the activation of one of their apps from one channel. But he checked the amount of downloads on that channel (the home Application market), and there was no noticeable change. So he was very confused and asked me if there was any way to help him find out why. A lot less can make a person headache--because the data is abnormal, it usually indicates that there is a problem. But just looking at an activation volume and a download amount doesn't reveal the root cause of the problem.。 Especially when we have learned about the mobile application operations model, we need to understand what kind of data we should focus on in every aspect of aarrr, what kind of data performance is normal--simply, only know that aarrr is not enough, but also use it. First, obtain the user (acquisition) This stage, originally everybody most concerned data is the download quantity. Today, some media reports often use downloads to measure the size and success of an application. However, the download of the application does not necessarily mean that the installation, installation of the application does not mean that the application must be used. So very soon the activation volume becomes the most concerned data of this level, even is the data that some promotion personnel only concern. The usual amount of activation, that is, the number of new users, is the new number of standalone devices that started the application. The activation volume seems to be more of a second layer of activation, but because of the amount of downloads and the amount of installation, the data are relatively virtual, and it does not really reflect whether the user has been acquired. So everyone has to look at activation, which is really getting the new user. Another very important data is the amount of activation that is divided into channel statistics. Because in the channel promotion, many application developers chose to pay the promotion. When settling, it is natural to understand how many users are actually active in a channel. Even without a paid relationship, developers need to know which channel is the most effective. But at a higher altitude, CAC (customer acquisition cost) is the most needed to focus on the data. There is a rough line in the industry that the cost per Android user is about 4 yuan, and iOS users are about 8 yuan. Of course, the application of market downloads, mobile phone presets, advertising and other different channels of access costs are completely different. There is a cost-effective problem, some channels to obtain a higher cost, but the user quality is also relatively high (what kind of call high quality, the following will be explained). Second, enhance the activity degree (activation) to see the active degree, everyone will think of the index is DAU (day active user), MAU (month active user). These two data basically illustrate the application of the current user base scale, in the online gaming industry This is two operators must see indicators. Usually active users are users who have started in a specified period of time. But is startup really equal to active? If only once in a specified cycle, and for a short period of time, such user activity is not very high (of course, for some special applications may be high, for example, used to record the female physiological cycle of the application, January start once is enough). So it's going to look at another two metrics: Average time to start each time and average daily boot times per user. When these two indicators are in the upward trend, it is certain that the application of user activity is increasing. It is also important to channel statistics on the use of time and startup times. We call them channel quality data ifA channel to the user, the two indicators are poor, so it is meaningless to invest too much in this channel. The most typical is the user of parallel machine brush, many preset applications are activated when the brush is finished. For this passive-active user, one can look at another metric, called the number of user-initiated users, that is, the number of users who have only started up to date. In addition to the channel, another and active degree-related analysis dimension is the version. There are also differences in the length of use and number of startup times for each version. For product managers, analyzing different versions of the difference in activity can help keep the application improving. In addition, related to the activity, there are daily active rate, weekly active rate, monthly active rate of these indicators. Of course, the active rate and the application of the category is very related, such as the desktop, the use of power-saving applications are higher than the application of the dictionary. Third, improve retention rate (Retention) download and install-use-uninstall or forget, this is the user's life cycle in each application. Successful applications are those that extend the life cycle of the user as much as possible, maximizing the value of the user's life cycle (the topic of the next session on life cycle value). For most applications, care should be 1-day Retention and 7-day Retention. 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 is not meant to be used for installation of the initial date of the application (the assumption is D), but effected day, that is, the second day of installation. Because the concept of a retention rate is not available on the first day of installation (only 100%). By the second day, the number of users who were installed on 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 this application in the first installation of D-Day on the d+7 day. Typically, the first few days after a new installation is used is the period of greatest wastage (for details on the retention of the user, please refer to our colleague's other blog, "read your user retention"). So these two indicators are most important in the retention rate analysis. There used to be a game industry experts pointed out that if you want to become a successful game, 1-day retention to reach the costs, 7-day retention to reach 20%. Some applications do not need to start daily, so you can see the weekly retention rate, the monthly retention rate and other indicators, will be more meaningful. Retention rate is also an important indicator of the quality of the users of the channel, if the same application of a channel's first day retention rate is much lower than other channels, then the quality of this channel is relatively poor. Iv. Income (Revenue) The most familiar indicator of income is the ARPU (average per user income) value. RightThere is also an indicator called ARPPU (average per paid user income). A few days ago, @ Wu gang in micro-blog map compared to the World War II ARPU value when it is noted that the weekly pay user ARPU (so actually is Arppu). But many people mistakenly read the 60-dollar week ARPU value, which makes them overly optimistic about the Android game. Is Arppu high, ARPU will certainly be high? The answer is not necessarily. Because there is also an indicator of the proportion of paid users, that is, the proportion of the total users of paid users. If the proportion of paid users is low, then the average income is lower for all users. In general, if a game increases the price of a virtual prop in order to improve Arppu, the proportion of paid users will be reduced accordingly. Find a balance between Arppu and paid users to maximize revenue. But income is not the most important, profit is. How to maximize profits? Profit is the simplest formula: Profit = income-cost. First we look at the cost, we mentioned in the last article CAC (user access costs). In addition, the development costs of the application itself, server hardware and bandwidth costs, and operating costs, and so on. However, in the case of a large number of users, CAC will become the most important cost, and other costs are not in order of magnitude, so we only consider CAC in subsequent discussions. So how is income calculated? ARPU is a time period-related indicator (usually the maximum monthly ARPU value) and cannot be fully matched to the CAC because the CAC and time periods are not directly related. So we have to look at one more indicator: LTV (life cycle value). A user's lifecycle is the cycle between a user starting the application for the first time, and the last time the application is started. LTV is the sum of revenue that a user creates for the application during the lifecycle, and can be viewed as a long-term cumulative ARPU value. Average LTV per user = monthly ARPU * User average lifecycle per month. LTV–CAC, it can be considered the profit that the application obtains from each user. So maximizing the profit becomes how to reduce the CAC while increasing the LTV, so that the difference between the two is maximized. Further, the different channel source users do the analysis, according to their different CAC and LTV, can deduce different channel source profit margin difference. Self-propagation (refer), or viral marketing, is a marketing method that has been widely studied in the last 10 years. Although we have all heard of some classic viral marketing cases, but to say how to quantify the effectiveness of the results, few people know the K factor (k-factor) this measure. In fact, the term K-factor does not originate in the market or software industry, but in the science of infectious diseases--yes, that is, the scientific study of real viral transmission. K-Factor quantifies the probability of infection, a host that has already contracted the virusHow many hosts in all the hosts that are exposed to it can infect the virus. The K-factor formula is not complicated, K = (number of invitations each user sends to his friends) * (The conversion rate of the person who receives the invitation into 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 pretty good--when K1, the user base grows like a snowball. If the k<1, then the user group to a certain size will stop the growth through self propagation. Unfortunately, even in the mobile applications of social classes, there are currently fewer k factors than 1. So the vast majority of mobile applications can not rely entirely on the self-communication, but also must be combined with other marketing methods. But from the product design stage to join in favor of the function of self-propagation, or is necessary, after all, this free promotion method can be partially reduced CAC. We have listed some of the indicators that need to be paid attention to at all levels of application extension operations. In the whole AARRR model, these quantitative indicators have a very important position, and the influence of many indicators is across multiple levels. Timely and accurate access to the specific data of these indicators is essential to the successful operation of the application.