CN113626705A - User retention analysis method and device, electronic equipment and storage medium - Google Patents
User retention analysis method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113626705A CN113626705A CN202110915583.0A CN202110915583A CN113626705A CN 113626705 A CN113626705 A CN 113626705A CN 202110915583 A CN202110915583 A CN 202110915583A CN 113626705 A CN113626705 A CN 113626705A
- Authority
- CN
- China
- Prior art keywords
- user
- retention rate
- user group
- users
- obtaining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of data analysis, and provides a user retention analysis method and device, electronic equipment and a storage medium. Obtaining a first prediction value set of the first user group with specific behavior characteristics according to the behavior characteristic set of the first user group; then, acquiring a second user group based on the first prediction value set, wherein the second user group has a preset range of difference between a second prediction value set with specific behavior characteristics and the first prediction value set; and finally, obtaining the relation between the specific behavior characteristics and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group. Thereby, the first user group and the second user have an association relationship by setting conditions of the first prediction value set and the second prediction value set. The method and the device realize the analysis of the retention rate of the user, avoid the influence caused by the selective deviation, obtain accurate results and are beneficial to analyzing out key influence factors.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a user retention analysis method and device, electronic equipment and a storage medium.
Background
With the development of the technology, big data processing and analysis can be applied to service scenes such as social service, game service and live broadcast service.
In these business scenarios, a retention rate for the user is necessary. In the related art, due to the update of the service scene or the deviation of the selected test user, the prediction is inaccurate or the precision is low.
Disclosure of Invention
In view of the above, the present invention provides a user retention analysis method, an apparatus, an electronic device and a storage medium.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, the present invention provides a user retention analysis method, comprising:
obtaining a first prediction value set of a first user group with specific behavior characteristics according to the behavior characteristic set of the first user group;
acquiring a second user group according to the first prediction value set; the second set of predicted values for the second group of users having the particular behavioral characteristic differs from the first set of predicted values by a preset range;
and obtaining the relation between the specific behavior characteristic and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group.
In an alternative embodiment, the first user group includes a plurality of first users, and the behavior feature set of the first user group includes the behavior feature of each of the first users;
the step of obtaining a first prediction value set with specific behavior characteristics of the first user group according to the behavior characteristic set of the first user group includes:
obtaining a first estimation value of each first user with the specific behavior characteristic according to the behavior characteristic of each first user and a prediction model;
and obtaining the first prediction value set according to all the first estimation values.
In an alternative embodiment, the first set of predicted values comprises a plurality of first predicted values;
the step of acquiring a second user group according to the first prediction value set includes:
acquiring behavior characteristics of a plurality of undetermined users;
obtaining a second estimated value of each undetermined user with the specific behavior characteristics according to the behavior characteristics of each undetermined user and the prediction model;
determining a plurality of target users from the plurality of pending users; the second estimation value of each target user is different from one first prediction value by a preset range; the second group of users includes all target users.
In an alternative embodiment, the step of obtaining the relationship between the specific behavior feature and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group includes:
obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group;
and obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value.
In a second aspect, the present invention provides a user retention analysis apparatus, the apparatus comprising:
the device comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring a first prediction value set of a first user group with a specific behavior characteristic according to the behavior characteristic set of the first user group;
a determining module, configured to obtain a second user group according to the first prediction value set; the first set of predicted values differs from a second set of predicted values for the second group of users having the particular behavioral characteristic by a preset range;
and the analysis module is used for obtaining the relation between the specific behavior characteristic and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group.
In an alternative embodiment, the first user group includes a plurality of first users, and the behavior feature set of the first user group includes the behavior feature of each of the first users; the acquisition module has a processor configured to:
obtaining a first estimation value of each first user with the specific behavior characteristic according to the behavior characteristic of each first user and a prediction model;
and obtaining the first prediction value set according to all the first estimation values.
In an alternative embodiment, the first set of predicted values comprises a plurality of first predicted values; the determining module is specifically configured to:
acquiring behavior characteristics of a plurality of undetermined users;
obtaining a second estimated value of each undetermined user with the specific behavior characteristics according to the behavior characteristics of each undetermined user and the prediction model;
determining a plurality of target users from the plurality of pending users; the second estimation value of each target user is different from one first prediction value by a preset range; the second group of users includes all target users.
In an alternative embodiment, the analysis module is specifically configured to:
obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group;
and obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value.
In a third aspect, the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor implements the method of any one of the preceding embodiments when executing the computer program.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of the preceding embodiments.
The embodiment of the invention provides a user retention analysis method and device, electronic equipment and a storage medium. Obtaining a first prediction value set of the first user group with specific behavior characteristics according to the behavior characteristic set of the first user group; then, acquiring a second user group based on the first prediction value set, wherein the second user group has a preset range of difference between a second prediction value set with specific behavior characteristics and the first prediction value set; and finally, obtaining the relation between the specific behavior characteristics and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group. Thereby, the first user group and the second user have an association relationship by setting conditions of the first prediction value set and the second prediction value set. Compared with the prior art, the method can avoid generating selective deviation in the process of selecting and testing the user, realize the analysis of the retention rate of the user, obtain accurate results and be beneficial to analyzing out key influence factors.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a schematic diagram of a scenario provided by an embodiment of the present invention;
FIG. 2 is a block diagram of an electronic device provided by an embodiment of the invention;
FIG. 3 is a flow chart of a user retention analysis method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a user retention analysis method according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a user retention analysis method according to an embodiment of the present invention;
FIG. 6 is a schematic flow chart of a user retention analysis method according to an embodiment of the present invention;
fig. 7 shows a functional block diagram of a user retention analysis apparatus according to an embodiment of the present invention.
Icon: 100-a server; 102-a terminal device; 120-a processor; 130-a memory; 170 — a communication interface; 300-user retention analysis means; 310-an acquisition module; 330-a determination module; 350-analysis module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the prior art, for the retention rate analysis of a user, an AB test mode can be adopted to implement a strategy on an experimental group for comparison with a control group, but the feasibility and the accuracy of the mode have great difficulty. Or a mode of selecting two test user groups is adopted, and key factors influencing the retention rate are analyzed based on the retention rates of the two test user groups. However, in this way, the selection of the test user group is strict, which may cause the result of the analysis to be inaccurate due to the selection bias (selection bias) of the test user group. Further, an embodiment of the present invention provides a user retention analysis method to solve the technical problems in the related art, and the following describes the user retention analysis method provided in the embodiment of the present invention.
Fig. 1 is a schematic view of a scene according to an embodiment of the present invention. The server 100 and the plurality of terminal devices 102 are included, and the server 100 is in communication connection with the plurality of terminal devices 102 to realize data interaction.
The server 100 may be a stand-alone server or a server cluster composed of a plurality of servers.
The terminal device 102 may be a smart phone, a personal computer, a tablet computer, a wearable device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a Personal Digital Assistant (PDA), or the like. The embodiments of the present invention do not limit this.
Optionally, the scenario diagram may be used to provide a variety of possible services, including but not limited to: multimedia streaming services, cloud gaming, distributed storage, and the like. Taking live video as an example, the server 100 may be a server providing live video stream, and the terminal device 102 may install a live video related Application (APP).
The server 100 may collect and analyze data related to the live video application in the terminal device 102 for different analysis purposes. The terminal device 102 may obtain relevant data of the user when using the live video application, and report the data to the server 100.
Fig. 2 is a block diagram of an electronic device according to an embodiment of the present invention. The electronic device includes a processor 120, a memory 130, and a communication interface 170.
The processor 120, memory 130, and communication interface 170 are in direct or indirect electrical communication with each other to facilitate the transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The user retention analysis device 300 includes at least one software function module that may be stored in the memory 130 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the server 100. The processor 120 is used to execute executable modules stored in the memory 130, such as software functional modules or computer programs comprised by the user retention analysis apparatus 300.
The processor 120 may be an integrated circuit chip having signal processing capability. The Processor 120 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 130 may be, but is not limited to, a Random Access Memory 130 (RAM), a Read Only Memory 130 (ROM), a Programmable Read Only Memory 130 (PROM), an Erasable Read Only Memory 130 (EPROM), an electrically Erasable Read Only Memory 130 (EEPROM), and the like. The memory 130 is used for storing a program, and the processor 120 executes the program after receiving an execution instruction, and the method executed by the server 100 defined by the flow process disclosed in any embodiment of the present invention may be applied to the processor 120, or implemented by the processor 120.
The communication interface 170 may be used for communicating signaling or data with other node devices.
It should be noted that the structure shown in fig. 2 is only a schematic structural diagram of the electronic device, and the electronic device may further include more or less components than those shown in fig. 2, or have a different configuration from that shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
It is understood that the electronic device shown in fig. 2 may be configured to implement the server 100 or the terminal device 102 in fig. 1; in order to implement the corresponding functions of the terminal device, the electronic device may further include other modules, such as: radio frequency circuits, I/O interfaces, batteries, touch screens, microphones/speakers, etc. And are not limiting herein.
The server 100 is used as an execution subject to execute each step in each method provided by the embodiment of the present invention, and achieve the corresponding technical effect.
Referring to fig. 3, fig. 3 is a flowchart illustrating a user retention analysis method according to an embodiment of the present invention.
Step S202, according to the behavior feature set of the first user group, obtaining a first prediction value set of the first user group with specific behavior features;
it can be understood that, in the process of using the application program by the user, the terminal device may obtain that the use condition of the user is the behavior characteristic.
The specific behavior feature refers to a behavior feature with a preset value and used for analyzing the influence of the behavior feature on the retention rate of the user. For example, when analyzing the effect of the behavior feature on the retention rate of the user, the subscription behavior is a specific behavior feature.
Alternatively, a plurality of users may be preset as the first user group, for example, in a live service scenario, during the process that a user uses a live video application, a terminal device of the user may collect behavior characteristics of the user, such as viewing duration, viewing days, viewing times, barrage, registration duration, active days, viewing anchor number, level, and then send the behavior characteristics to the server.
After receiving the behavior feature of each user, the server may obtain a behavior feature set of the first user group, and according to the behavior feature set, may obtain a first prediction value set of the first user group having a specific behavior feature. This first set of prediction values may be understood as a tendency score representing the tendency of the first group of users to produce the particular behavioral characteristic.
Step S204, acquiring a second user group according to the first prediction value set;
optionally, after obtaining the first prediction value set of the first user group, the server may obtain the second user group according to the first prediction value set.
Optionally, the second set of predicted values of the second user group having the characteristic behavior characteristic and the first set of predicted values satisfy a predetermined condition, the predetermined condition may be that the second set of predicted values and the first set of predicted values differ by a predetermined range, for example, the difference between the second set of predicted values and the first set of predicted values may be a predetermined multiple of the first set of predicted values. The second set of predictive values may be understood as a tendency score representing a characteristic behavior feature of the second group of users.
It is understood that, if the first set of predicted values of the first user group and the second set of predicted values of the second user group satisfy a preset condition, there is a certain association relationship between the first user group and the second user group.
Step S206, obtaining the relation between the specific behavior characteristics and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group;
optionally, after obtaining the first user group and the second user group, the retention rates of the two users, that is, the first retention rate of the first user group and the second retention rate of the second user group, may be respectively calculated, and based on the retention rates of the two user groups, the relationship between the specific behavior feature and the retention rate, that is, the influence of the specific behavior feature on the user retention rate, may be obtained.
For ease of understanding, the above steps will be described below by taking a subscription behavior as a specific behavior feature and by way of example with reference to the scenario diagram shown in fig. 1.
As shown in fig. 1, the number of the first user group may be preset, and the behavior feature set of the first user group is acquired through the plurality of terminal devices 102, where the behavior feature set includes behavior features such as viewing duration, viewing days, viewing times, barrage, registration duration, active days, viewing anchor number, level, and the like; each terminal device 102 then reports the collected behavior characteristics to a server 100.
After obtaining the behavior feature set of the first user group, the server 100 obtains a first prediction value set of the first user group having a specific behavior feature according to the behavior feature set of the first user group, that is, obtains a tendency score of the first user group having a subscription behavior.
And then acquiring a second user group according to the first prediction value set, wherein the difference between the second prediction value set with the specific behavior characteristics of the second user group and the first prediction value set is within a preset range. Optionally, the average value of the first prediction value set is a first average value, and the average value of the second prediction value set is a second average value. The second prediction value set is different from the first prediction value set by a preset range, and the difference between the first average value and the second average value may be a preset multiple of the first average value, such as 0.05 times.
After the server 100 acquires the second user group, the first retention rate of the first user group and the second retention rate of the second user group are obtained, and based on the retention rates of the two user groups, the relationship between the specific behavior characteristics and the retention rate, that is, the influence of the subscription behavior and the user retention rate is obtained.
Based on the steps, obtaining a first prediction value set with specific behavior characteristics of the first user group according to the behavior characteristic set of the first user group; then, acquiring a second user group based on the first prediction value set, wherein the second user group has a preset range of difference between a second prediction value set with specific behavior characteristics and the first prediction value set; and finally, obtaining the relation between the specific behavior characteristics and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group. Thereby, the first user group and the second user have an association relationship by setting conditions of the first prediction value set and the second prediction value set. Compared with the prior art, the method can avoid generating selective deviation in the process of selecting and testing the user, realize the analysis of the retention rate of the user, obtain accurate results and be beneficial to analyzing out key influence factors.
Optionally, it is known from the above steps that the first predicted value set of the first user may affect the result analysis, and further to improve the accuracy, an embodiment of the present invention provides a possible implementation manner, please refer to fig. 4, where the step S202 may further include the following steps:
step S202-1, obtaining a first estimation value of each first user with specific behavior characteristics according to the behavior characteristics of each first user and a prediction model;
the first user group comprises a plurality of first users, and the behavior feature set of the first user group comprises the behavior feature of each first user;
the prediction model is a model which is constructed in advance according to a large amount of test data, and can predict the tendency score of the user for generating a specific positioning feature according to the behavior feature of the user. If the prediction model is a logistic regression model: g (z) ═ 1/(1+ e-z), z denotes a behavioral characteristic, g (z) denotes a predicted user's tendency score to produce a particular behavioral characteristic, which may also be denoted by s.
Alternatively, the behavior feature of each first user may be used as an input of a prediction model, and an output of the prediction model is a first estimation value of the specific behavior feature of each first user. The first estimate may be understood as a tendency score indicative of the first user's tendency to produce the particular behavioral characteristic.
Step S202-3, a first prediction value set is obtained according to all first estimation values;
it is understood that there may be abnormal values in all the obtained first estimated values, and in order to avoid that these abnormal values affect the accuracy of the analysis result, it is necessary to perform screening on all the first estimated values.
Alternatively, a mean value and a standard deviation may be calculated based on all the first estimated values, the mean value may be represented by u, and the standard deviation may be represented by δ, and then a number set formed by the first estimated values within a preset interval, such as within a [ u-3 δ, u +3 δ ] interval, may be used as the first predicted value set. It should be noted that the preset interval may be designed according to actual requirements, and the embodiment of the present invention is not limited.
Through the steps, according to the behavior characteristics and the prediction model of each first user in the first user group, a first estimation value of each first user with specific behavior characteristics can be obtained, and then all the first estimation values are screened to obtain a first prediction value set. By screening the abnormal values, the inaccurate analysis caused by the abnormal values is avoided, and the accuracy is improved.
Optionally, in the above steps, it is mentioned that the second user may be obtained based on the first prediction value set, so that the first user group and the second user group have a certain association relationship. Furthermore, an embodiment of the present invention provides a possible implementation manner, please refer to fig. 5, wherein step S204 may further include the following steps:
step S204-1, acquiring behavior characteristics of a plurality of undetermined users;
optionally, the behavior characteristics of a plurality of users to be determined may be collected by the terminal device, and the behavior characteristics of each user to be determined are sent to the server, so that the server obtains the behavior characteristics of the plurality of users to be determined.
Step S204-3, obtaining a second estimated value of each undetermined user with specific behavior characteristics according to the behavior characteristics of each undetermined user and the prediction model;
optionally, the behavior feature of each user to be determined may be used as an input of a prediction model, and an output of the prediction model is a second estimated value of each user to be determined having a specific behavior feature. The second estimate may be understood as a tendency score indicative of the pending user's propensity to produce the particular behavioral characteristic.
Step S204-5, determining a plurality of target users from a plurality of pending users;
it is to be understood that the first prediction value set includes a plurality of first prediction values, and the first prediction values are to be understood as first estimation values in the preset interval.
Alternatively, a plurality of target users meeting a preset standard may be determined from the plurality of pending users according to all the second estimated values, where the preset standard is that the second estimated value of the target user differs from one first estimated value by a preset range, for example, a difference between the second estimated value of the target user and one first predicted value is a preset multiple of the first predicted value, and the first predicted value is greater than the second estimated value.
Optionally, all target users meeting the preset criterion in the plurality of pending users are used as the second user group.
In order to better understand the present invention, the above steps will be further illustrated below with subscription behavior as a specific behavior feature.
For example, when the influence of the subscription behavior on the user retention analysis is analyzed, the user retention analysis method provided by the embodiment of the invention can enable the obtained subscription behaviors of the two user groups, such as the subscription times, to have relatively close tendencies, and meanwhile, the subscription behaviors have certain differences, so that the selective deviation brought by selecting the users can be eliminated, and the influence of the subscription behavior on the user retention rate can be accurately analyzed.
Assuming that whether the user is subjected to policy intervention or not is in a relationship with the subscription behavior, a binary intervention T can be used to represent whether the user is subjected to policy intervention or not, namely Ti0 indicates that the user is not intervened, Ti1 indicates that the user is intervened.
For each user i, it produces two results based on whether it is subject to policy intervention, namely Yi0And Yi1. Wherein, Yi0Representing that the user retains the probability improvement value when not being intervened; y isi1Indicating that the user withholds the probability increase value when the user is intervened. The user-generated results may be represented by Y, e.g. T when the user is not subject to interventioniWhen equal to 0, Y is equal to Yi0。
To avoid selective deviation, the user can be simulated to compare with the user itself, and then can be formulated: ATT ═ E [ Y ═ Yi1-Yi0|T＝1]And T-1 represents the user subjected to the intervention, and ATT represents the average influence effect of the strategy on the user subjected to the intervention.
However, it cannot be interfered with nor interfered with for a user, i.e. Y in the above formulai1-Yi0It cannot be calculated. Furthermore, the target user close to the user in the subscription behavior can be obtained through the user retention analysis method provided by the embodiment of the invention, so that the two user groups, namely the first user group and the second user group, have homogeneity. The user' S subscription behavior propensity score S may be expressed as: s ═ Pr [ T ═ 1| X ═ X]X represents the behavior characteristic of the user, and T ═ 1 represents the user who is subjected to the intervention.
Furthermore, based on the behavior characteristics and the prediction model of each first user in the first user group, a first estimation value, which is a tendency score of the subscription behavior of each first user, can be obtained, and after all the first estimation values are screened, a plurality of first prediction values, which are first prediction value sets, are obtained.
According to the first predicted value, a target user corresponding to the first predicted value is obtained, the difference between the second estimated value, which is the tendency score of the subscription behavior of the target user, and the first predicted value is within a preset range, for example, the difference between the second estimated value and the first predicted value is 0.05 times of the first predicted value, and the first predicted value is larger than the second estimated value.
And obtaining target users meeting the preset standard based on each first predicted value in the first predicted value set, and taking all the target users as a second user group. The method and the device can avoid the selective difference between the first user group and the second user group, further eliminate the influence caused by the selective difference and improve the accuracy of analysis.
With respect to the step S206, the embodiment of the present invention provides a possible implementation manner. Referring to fig. 6, step S206 may further include the following steps:
step S206-1, obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group;
optionally, after the first user group and the second user group are obtained, an average retention rate of the first user group, that is, a first retention rate, may be calculated, and an average retention rate of the second user group, that is, a second retention rate, may also be calculated. The first retention rate can be represented by u1, the second retention rate can be represented by u2, and the difference u3 is equal to u1-u 2.
Step S206-3, obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value;
optionally, after obtaining the retention rate difference, the relationship between the specific behavior feature and the retention rate may be obtained according to the preset range and the retention rate difference.
For example, taking the subscription behavior as an example of the specific behavior feature, when the preset range is 5%, that is, the difference between the first predicted value of the first user and the second estimated value of the corresponding target user is 0.05 times of the first predicted value. It is understood that when the behavioral propensity score of a user changes by 5%, the retention rate of the user changes by u 3. The impact of the subscription behavior on the retention rate of the user, i.e. the relation of the specific behavior feature to the retention rate, can be derived.
In order to perform the corresponding steps in the above embodiments and various possible manners, an implementation manner of the user retention analysis apparatus is given below. Referring to fig. 7, fig. 7 is a functional block diagram of a user retention analysis apparatus 300 according to an embodiment of the present invention. It should be noted that the basic principle and the generated technical effect of the user retention analysis apparatus 300 provided in the present embodiment are the same as those of the above embodiments, and for the sake of brief description, no part of the present embodiment is mentioned, and reference may be made to the corresponding contents in the above embodiments. The user retention analysis apparatus 300 includes:
an obtaining module 310, configured to obtain a first prediction value set of a first user group having a specific behavior feature according to the behavior feature set of the first user group;
a determining module 330, configured to obtain a second user group according to the first prediction value set; the first prediction value set and a second prediction value set of a second user group with specific behavior characteristics are different by a preset range;
the analysis module 350 is configured to obtain a relationship between the specific behavior feature and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group.
Optionally, the first user group includes a plurality of first users, and the behavior feature set of the first user group includes the behavior feature of each first user; the acquisition module 310 has a function for: obtaining a first estimation value of each first user with specific behavior characteristics according to the behavior characteristics of each first user and the prediction model; and obtaining a first prediction value set according to all the first estimation values.
Optionally, the first set of prediction values comprises a plurality of first prediction values; the determining module 330 is specifically configured to: acquiring behavior characteristics of a plurality of undetermined users; obtaining a second predicted value of each undetermined user with specific behavior characteristics according to the behavior characteristics and the prediction model of each undetermined user; determining a plurality of target users from a plurality of pending users; the second estimation value of each target user is different from one first estimation value by a preset range; the second group of users includes all of the target users.
Optionally, the analysis module 350 is specifically configured to: obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group; and obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value.
The embodiment of the present invention further provides an electronic device, which includes a processor 120 and a memory 130, where the memory 130 stores a computer program, and when the processor executes the computer program, the user retention analysis method disclosed in the foregoing embodiment is implemented.
An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program, when executed by the processor 120, implements the user retention analysis method disclosed in the embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A user retention analysis method, the method comprising:
obtaining a first prediction value set of a first user group with specific behavior characteristics according to the behavior characteristic set of the first user group;
acquiring a second user group according to the first prediction value set; the second set of predicted values for the second group of users having the particular behavioral characteristic differs from the first set of predicted values by a preset range;
and obtaining the relation between the specific behavior characteristic and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group.
2. The method of claim 1, wherein the first group of users comprises a plurality of first users, and wherein the set of behavior characteristics of the first group of users comprises the behavior characteristics of each of the first users;
the step of obtaining a first prediction value set with specific behavior characteristics of the first user group according to the behavior characteristic set of the first user group includes:
obtaining a first estimation value of each first user with the specific behavior characteristic according to the behavior characteristic of each first user and a prediction model;
and obtaining the first prediction value set according to all the first estimation values.
3. The method according to claim 2, wherein the first set of prediction values includes a plurality of first prediction values;
the step of acquiring a second user group according to the first prediction value set includes:
acquiring behavior characteristics of a plurality of undetermined users;
obtaining a second estimated value of each undetermined user with the specific behavior characteristics according to the behavior characteristics of each undetermined user and the prediction model;
determining a plurality of target users from the plurality of pending users; the second estimation value of each target user is different from one first prediction value by a preset range; the second group of users includes all target users.
4. The method according to claim 1, wherein the step of deriving the relationship between the specific behavior feature and the retention rate according to a first retention rate of the first user group and a second retention rate of the second user group comprises:
obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group;
and obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value.
5. A user retention analysis apparatus, the apparatus comprising:
the device comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring a first prediction value set of a first user group with a specific behavior characteristic according to the behavior characteristic set of the first user group;
a determining module, configured to obtain a second user group according to the first prediction value set; the first set of predicted values differs from a second set of predicted values for the second group of users having the particular behavioral characteristic by a preset range;
and the analysis module is used for obtaining the relation between the specific behavior characteristic and the retention rate according to the first retention rate of the first user group and the second retention rate of the second user group.
6. The apparatus of claim 5, wherein the first group of users comprises a plurality of first users, and wherein the set of behavior characteristics of the first group of users comprises the behavior characteristics of each of the first users; the acquisition module has a processor configured to:
obtaining a first estimation value of each first user with the specific behavior characteristic according to the behavior characteristic of each first user and a prediction model;
and obtaining the first prediction value set according to all the first estimation values.
7. The apparatus of claim 6, wherein the first set of prediction values comprises a plurality of first prediction values; the determining module is specifically configured to:
acquiring behavior characteristics of a plurality of undetermined users;
obtaining a second estimated value of each undetermined user with the specific behavior characteristics according to the behavior characteristics of each undetermined user and the prediction model;
determining a plurality of target users from the plurality of pending users; the second estimation value of each target user is different from one first prediction value by a preset range; the second group of users includes all target users.
8. The apparatus of claim 5, wherein the analysis module is specifically configured to:
obtaining a retention rate difference value according to a first retention rate of the first user group and a second retention rate of the second user group;
and obtaining the relation between the characteristic behavior characteristics and the retention rate according to the preset range and the retention rate difference value.
9. An electronic device, comprising a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method of any of claims 1 to 4.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110915583.0A CN113626705B (en) | 2021-08-10 | 2021-08-10 | User retention analysis method, device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110915583.0A CN113626705B (en) | 2021-08-10 | 2021-08-10 | User retention analysis method, device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113626705A true CN113626705A (en) | 2021-11-09 |
| CN113626705B CN113626705B (en) | 2023-10-20 |
Family
ID=78384133
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110915583.0A Active CN113626705B (en) | 2021-08-10 | 2021-08-10 | User retention analysis method, device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113626705B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114742569A (en) * | 2021-01-08 | 2022-07-12 | 广州视源电子科技股份有限公司 | User life stage prediction method and device, computer equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180053095A1 (en) * | 2016-08-19 | 2018-02-22 | International Business Machines Corporation | Iterative and Targeted Feature Selection |
| CN108648000A (en) * | 2018-04-24 | 2018-10-12 | 腾讯科技（深圳）有限公司 | Method and device, the electronic equipment that life cycle is assessed are retained to user |
| CN111459783A (en) * | 2020-04-03 | 2020-07-28 | 北京字节跳动网络技术有限公司 | Application program optimization method and device, electronic equipment and storage medium |
| CN113132762A (en) * | 2021-04-16 | 2021-07-16 | 广州虎牙科技有限公司 | Retention behavior factor analysis method and device, electronic equipment and readable storage medium |
-
2021
- 2021-08-10 CN CN202110915583.0A patent/CN113626705B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180053095A1 (en) * | 2016-08-19 | 2018-02-22 | International Business Machines Corporation | Iterative and Targeted Feature Selection |
| CN108648000A (en) * | 2018-04-24 | 2018-10-12 | 腾讯科技（深圳）有限公司 | Method and device, the electronic equipment that life cycle is assessed are retained to user |
| CN111459783A (en) * | 2020-04-03 | 2020-07-28 | 北京字节跳动网络技术有限公司 | Application program optimization method and device, electronic equipment and storage medium |
| CN113132762A (en) * | 2021-04-16 | 2021-07-16 | 广州虎牙科技有限公司 | Retention behavior factor analysis method and device, electronic equipment and readable storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114742569A (en) * | 2021-01-08 | 2022-07-12 | 广州视源电子科技股份有限公司 | User life stage prediction method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113626705B (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107040397B (en) | Service parameter acquisition method and device | |
| US11188785B2 (en) | Optimization of network data analysis device | |
| US20200257749A1 (en) | Method and a device for friend recommendation | |
| CN109831532B (en) | Data sharing method, device, equipment and medium | |
| CN111459783B (en) | Application program optimization method and device, electronic equipment and storage medium | |
| CN108243032B (en) | Method, device and equipment for acquiring service level information | |
| CN113194306B (en) | Frame rate fluctuation evaluation method and device, mobile terminal, system and storage medium | |
| US20140250334A1 (en) | Detection apparatus and detection method | |
| CN113485931A (en) | Test method, test device, electronic equipment and computer readable storage medium | |
| CN112035320A (en) | Service monitoring method and device, electronic equipment and readable storage medium | |
| CN113626705B (en) | User retention analysis method, device, electronic equipment and storage medium | |
| CN115729783A (en) | Fault risk monitoring method, apparatus, storage medium and program product | |
| CN110602207A (en) | Method, device, server and storage medium for predicting push information based on off-network | |
| CN109861843B (en) | Method, device and equipment for completely collecting and confirming log files | |
| EP2917882A1 (en) | Heuristics to quantify data quality | |
| CN111882349B (en) | Data processing method, device and storage medium | |
| CN111629336B (en) | Method, device and equipment for determining number of people in target area and storage medium | |
| US20150120940A1 (en) | Apparatus and method for changing resource using pattern information, and recording medium using the same | |
| CN116795628A (en) | Power consumption processing method of terminal equipment, terminal equipment and readable storage medium | |
| CN114064445A (en) | Test method, device, equipment and computer readable storage medium | |
| CN109726550B (en) | Abnormal operation behavior detection method and device and computer readable storage medium | |
| CN109922359B (en) | User processing method, device, equipment and storage medium | |
| CN114756698A (en) | Play behavior detection method, equipment and storage medium | |
| EP4349055A2 (en) | Dimensioning of telecommunication infrastructure | |
| CN108133046B (en) | Data analysis method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |