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Mar 8 2021
As the traffic dividend is fading away, where can APP go to ask for user growth? Some APP choose to convert from super users; some APP choose to sink the market to get more new traffic; some APP increase the incentive chip of user sharing in exchange for user fission ...... but the anxiety of APP operation never stops, how long can these methods maintain growth? Where is the next growth point of user operation?
Nowadays, the cost of the process of acquiring target users and then converting them into users of their own products has become very expensive. And in such a brutal competitive environment, it is a recognized trend in the industry to do well in new user operation with the help of data intelligence. However, how to use data intelligence technology to maximize the growth effect is a learning curve.
In order to understand the anxiety of app operators, Personal Push conducted a big data analysis on the active users of popular apps on the market, such as information, video and tools. The analysis results show that except for some apps that use cash rewards for user fission, the average monthly active users of the three main categories of apps is 27.8% of the stock. In other words, app still has more than 70% of user resources still to be developed.
Why app does not go to tap the value of this 70% of users? On the basis of data insight, personal push also did research on some app operations. The research found that most apps have insufficient ability of user portrait, which restricts the development of refined user operation.
For example, about 60% of the old users in these apps have incomplete or invalid portrait preferences, while the preferences of newly registered inactive users are completely unknown. That is to say, apps do not want to exploit these 70% of user resources, but they lack understanding of these inactive users, and there is no good way and good way to understand.
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If app wants to revitalize users, it must first really understand them. Therefore, user portrait is an important way for app to understand users, and it is also a necessary tool for refining operation. app can discover different characteristics of users through user portrait, put different tags on users, and then group users according to the combination of tags, and then carry out group operation.
At present, many apps are beginning to pay attention to the application of user portraits, and some large companies will use third-party data tools to assist on the basis of their own user portrait system, with the aim of refining operations through accurate user portraits. However, there are still many problems that need to be solved if app wants user portrait to be accurate enough.
The optimization of app user portrait is a gradual process, which requires multiple necessary conditions such as time precipitation, data accumulation and algorithm model refinement. For example, app can not accurately understand the preferences of newly registered users, the essence of which lies in the lack of time to interact with users, relying on some information filled out by users during registration and not accurate enough.
The problem of incomplete app portrait of silent users lies in the lack of continuity and stability of app data accumulation for inactive users, unable to understand the migration changes in user needs. The app in the start-up period is constrained by funds and manpower, and the development of user portraits is not strong enough, resulting in poor differentiation of user portraits to be useful. These are not things that can be done overnight for app, and they are also the bottlenecks at present.
App wants to solve the problem of accurate user portrait, on the one hand, it needs to have patience and be able to iterate and update the user portrait continuously; on the other hand, it can make up the short board of its own user portrait and improve the accuracy of user portrait with the advantage of third-party data service providers with full dimension, good continuity and strong stability; finally, it also needs to combine with the use scenario of app operation and make innovation to its own user portrait. Finally, it is also necessary to make innovation to the application of its own user profile in combination with the use scenario of app operation.
App operations often use third-party data services to do a good job in cold-starting new users, but when using them, they will find that the tags provided by third-party data services have a low match with their own user tags and cannot cover the full amount. Take consumption level label as an example, different apps have different definitions for user consumption level label. Group buying app spending more than 300 is considered high consumption people, while 100,000 models in the car app belong to the low-end models.
Therefore, app for third-party data can not be used directly, it is best to generate customized labels that match their own labeling system through data modeling of both sides.
Personal Push User Portrait has cooperated with an information app, and both sides have output a complete and customized new portrait label through joint data modeling, and the accuracy of label prediction has reached 70% after testing. In the process of cold start, the app recommended interested contents for new users through customized tags, and the next day retention rate of new users increased by 18%.
In today's depleted traffic dividend, it is more valuable for an app to activate a silent user than to pull a new user. Doing so not only saves growth costs, but also facilitates the continuity and stability of app data accumulation, and provides valuable data for app data development and application.
App to wake up a silent user, not simply brutally get the user back, but need to distinguish the internal and external causes of user loss, corresponding to different users and different situations, to make different operation methods and solutions. The internal causes can be found from the internal data of app, but for the external causes of user silence, we need to use the ability of three-party data to understand the changes of users' online behavioral preferences during the silent period and circle the users who need to be awakened. For these users who need to be awakened, it is not enough to use the data they left on the app a few days ago, but also need to combine with external data, insight into the migration of user needs and interests, through the selection of channels and customized content, so that users can come back to life.
In today's world where user interest preferences constantly challenge human imagination, it is difficult to find a balance between the fineness of user portraits and the cost of building app. The label is too coarse, the degree of differentiation is not enough to accurately target users. With finer tags, more data and longer time are needed to accumulate, and the cost cannot be controlled. In such a dilemma, app operation can create and update distinguishing features in real time through the data modeling ability of third-party data service providers, combined with their own in-depth research on a certain field, to help optimize user portraits.
For example, for the label of basketball fans, the traditional distinguishing features are based on the user's basketball application preference degree. app if you want the label to be more accurate, you can combine the user's specific behavior of watching the game, specific offline scenes to customize the features and generate more accurate user labels.
In short, the mobile Internet is about to enter the era of inward growth, app's requirements for refined operation will become higher and higher, and the role of user portraits in refined operation will become more and more prominent.
App only do fine and good user portraits, in order to truly understand and grasp the needs of users, to do a good job of products and services, to achieve a good conversion of 30% of super users, while revitalizing the remaining 70% of user resources. The app can only do a good job in understanding and grasping the needs of users, and doing a good job in products and services, so as to achieve the conversion of 30% of super users and revitalize the remaining 70% of user resources.
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