Application feasibility evaluation method, device, electronic device, and storage medium

Abstract

The present disclosure relates to a method, apparatus, electronic device, and storage medium for evaluating the feasibility of an application. The method comprises: obtaining business data of experimental group users and business data of control group users, respectively, the business data including user retention rates for each time unit; generating a user value difference between the experimental group users and the control group users based on the user retention rates for each time unit of the experimental group users and the user retention rates for each time unit of the control group users; and evaluating the feasibility of an application executed by the experimental group users based on a first policy based on the user value difference. The present disclosure retains the user retention rate and generates a user value difference based on the user retention rate. The feasibility of an application can be evaluated based on the user value difference, thereby improving the accuracy of the application feasibility assessment.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular to a method, device, electronic device, and storage medium for evaluating the feasibility of an application. Background Art

[0002] Currently, before promoting an application, it is usually necessary to conduct a feasibility assessment on the application to be promoted, and decide whether it is suitable to promote the application based on the feasibility assessment results.

[0003] In the related art, a common feasibility assessment scheme for an application selects two groups of users. One group of users is the experimental group, and the application executed by the experimental group users is an application based on one policy; the other group of users is the control group, and the application executed by the control group users is an application based on another policy. In other words, the strategies of the applications executed by the experimental group users and the control group users are different. In addition, an observation period is set to collect business data of the experimental group users and the control group users during the observation period. The collected business data includes user retention rate. However, since the time period of the observation period is short, people often subjectively believe that the difference between the user retention rate of the experimental group users and the user retention rate of the control group users is very small, and it is easy to ignore the user retention rate in the business data, resulting in inaccurate feasibility assessment results of the application. Summary of the Invention

[0004] The present disclosure provides a method, device, electronic device, and storage medium for evaluating the feasibility of an application program to at least address the problem of inaccurate feasibility evaluation results for applications in related technologies. The technical solutions of the present disclosure are as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a method for evaluating the feasibility of an application is provided, wherein users of the application include experimental group users and control group users, the experimental group users execute the application based on a first strategy, and the control group users execute the application based on a second strategy, the method comprising: respectively obtaining business data of the experimental group users within a preset time range and business data of the control group users within the time range, the business data including a user retention rate for each time unit; generating a user value difference between the experimental group users and the control group users based on the user retention rate for each time unit of the experimental group users and the user retention rate for each time unit of the control group users; and evaluating the feasibility of the application based on the first strategy executed by the experimental group users based on the user value difference.

[0006] Optionally, the business data also includes average revenue per user (ARPU); generating the user value difference between the experimental group users and the control group users based on the user retention rate of each time unit of the experimental group users and the user retention rate of each time unit of the control group users includes: generating the average retention time of the experimental group users based on the user retention rate of each time unit of the experimental group users, and generating the average retention time of the control group users based on the user retention rate of each time unit of the control group users; generating the user value difference based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

[0007] Optionally, generating the average retention duration of the experimental group users based on the user retention rate of each time unit of the experimental group users includes: generating the average retention duration of the experimental group users based on the user retention rate of each time unit of the experimental group users and a preset retention rate decay function of the experimental group users; generating the average retention duration of the control group users based on the user retention rate of each time unit of the control group users includes: generating the average retention duration of the control group users based on the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users.

[0008] Optionally, generating the average retention time of the experimental group users based on the user retention rate of each time unit of the experimental group users and a preset retention rate decay function of the experimental group users includes: inputting the user retention rate of each time unit of the experimental group users into the following formula representing the retention rate decay function of the experimental group users: p(t)=A1*exp(-b1t); wherein, p represents the user retention rate of each time unit of the experimental group users, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users; obtaining A1 and b1 from the retention rate decay function of the experimental group users based on a regression algorithm; and taking the ratio of A1 and b1 as the average retention time of the experimental group users.

[0009] Optionally, generating the average retention duration of the control group users based on the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users includes: inputting the user retention rate of each time unit of the control group users into the following formula representing the retention rate decay function of the control group users: q(t)=A2*exp(-b2t); wherein, q represents the user retention rate of each time unit of the control group users, t represents the time unit, A2 represents the weight coefficient of the total retention duration of the control group users, and b2 represents the weight coefficient of each time unit of the control group users; obtaining A2 and b2 from the retention rate decay function of the control group users based on a regression algorithm; and taking the ratio of A2 to b2 as the average retention duration of the control group users.

[0010] Optionally, generating the user value difference based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users, includes: subtracting the product of the average retention time of the control group users and the ARPU of the control group users from the product of the average retention time of the experimental group users and the ARPU of the experimental group users, to obtain the user value difference.

[0011] Optionally, the feasibility of the application based on the first strategy executed by the users of the experimental group is evaluated based on the user value difference, including: when the numerical value of the user value difference is greater than a preset threshold, determining that the application based on the first strategy executed by the users of the experimental group is a feasible application.

[0012] According to a second aspect of an embodiment of the present disclosure, a device for evaluating the feasibility of an application is provided, wherein users of the application include experimental group users and control group users, the experimental group users execute the application based on a first strategy, and the control group users execute the application based on a second strategy, the device comprising: an acquisition module configured to respectively acquire business data of the experimental group users within a preset time range and business data of the control group users within the time range, the business data including user retention rates of each time unit; a generation module configured to generate a user value difference between the experimental group users and the control group users based on the user retention rates of each time unit of the experimental group users and the user retention rates of each time unit of the control group users; and an evaluation module configured to evaluate the feasibility of the application based on the first strategy executed by the experimental group users based on the user value difference.

[0013] Optionally, the business data also includes average revenue per user (ARPU); the generation module includes: an average retention time generation module, configured to generate the average retention time of the experimental group users according to the user retention rate of each time unit of the experimental group users, and to generate the average retention time of the control group users according to the user retention rate of each time unit of the control group users; a user value difference generation module, configured to generate the user value difference according to the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

[0014] Optionally, the average retention time generation module is configured to generate the average retention time of the experimental group users based on the user retention rate of each time unit of the experimental group users and a preset retention rate decay function of the experimental group users; the average retention time generation module is configured to generate the average retention time of the control group users based on the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users.

[0015] Optionally, the average retention time generation module includes: a user retention rate input module, configured to input the user retention rate of each time unit of the experimental group users into the following formula representing the retention rate decay function of the experimental group users: p(t)=A1*exp(-b1t); wherein, p represents the user retention rate of each time unit of the experimental group users, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users; a regression module, configured to obtain A1 and b1 from the retention rate decay function of the experimental group users based on a regression algorithm; and a determination module, configured to use the ratio of A1 and b1 as the average retention time of the experimental group users.

[0016] Optionally, the user retention rate input module is further configured to input the user retention rate of each time unit of the control group users into the following formula representing the retention rate decay function of the control group users: q(t) = A2*exp(-b2t); wherein, q represents the user retention rate of each time unit of the control group users, t represents the time unit, A2 represents the weight coefficient of the total retention time of the control group users, and b2 represents the weight coefficient of each time unit of the control group users; the regression module is further configured to output A2 and b2 from the retention rate decay function of the control group users based on a regression algorithm; the determination module is further configured to use the ratio of A2 and b2 as the average retention time of the control group users.

[0017] Optionally, the user value difference generation module is configured to obtain the user value difference by subtracting the product of the average retention time of the users in the control group and the ARPU of the users in the control group from the product of the average retention time of the users in the experimental group and the ARPU of the users in the experimental group.

[0018] Optionally, the evaluation module is configured to determine that the application based on the first strategy executed by the users in the experimental group is a feasible application when the value of the user value difference is greater than a preset threshold.

[0019] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the feasibility assessment method for an application as described in the first aspect.

[0020] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the feasibility assessment method of the application as described in the first aspect.

[0021] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a readable program code, wherein the readable program code can be executed by a processor of an electronic device to complete the feasibility assessment method of the application described in the first aspect above.

[0022] According to a sixth aspect of an embodiment of the present disclosure, an application is provided, comprising a first strategy and a second strategy, wherein users of the application comprise an experimental group user and a control group user, wherein the experimental group users execute the application based on the first strategy, and the control group users execute the application based on the second strategy.

[0023] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0024] The embodiment of the present disclosure provides a feasibility assessment scheme for an application, in which users in the experimental group execute an application based on a first strategy, and users in the control group execute an application based on a second strategy. Business data of the users in the experimental group within a preset time range and business data of the users in the control group within the above time range are obtained respectively. The business data may include the user retention rate of each time unit. Then, based on the user retention rate of each time unit of the users in the experimental group and the user retention rate of each time unit of the users in the control group, the user value difference between the users in the experimental group and the users in the control group is generated, and then the feasibility of the application based on the first strategy executed by the users in the experimental group is evaluated based on the user value difference.

[0025] The embodiments of the present disclosure retain the user retention rate when performing feasibility evaluation on an application, and generate user value differences based on the user retention rate. The feasibility of the application can be evaluated based on the user value differences, thereby improving the accuracy of the feasibility evaluation of the application.

[0026] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0028] Figure 1 The figure is a flowchart showing a method for evaluating the feasibility of an application program according to an exemplary embodiment.

[0029] Figure 2 The figure is a schematic diagram showing steps for generating user value differences according to an exemplary embodiment.

[0030] Figure 3 The figure is a block diagram of a device for evaluating the feasibility of an application program according to an exemplary embodiment.

[0031] Figure 4 The figure is a block diagram of an electronic device for evaluating the feasibility of an application according to an exemplary embodiment.

[0032] Figure 5 The figure is a block diagram of another electronic device for evaluating the feasibility of an application according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] In order to enable ordinary people in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0034] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0035] Figure 1FIG. 1 is a flow chart showing a method for evaluating the feasibility of an application according to an exemplary embodiment. Figure 1 As shown, the users of the application used for evaluation by the method may include users in the experimental group and users in the control group, and the method is mainly used to evaluate the feasibility of the application used by the users in the experimental group. The method may specifically include the following steps.

[0036] In step S11 , the business data of the users in the experimental group within a preset time range and the business data of the users in the control group within a preset time range are obtained respectively.

[0037] In the embodiments of the present disclosure, the number of users in the experimental group is usually set to be the same as the number of users in the control group. For example, the number of users in the experimental group and the number of users in the control group are both 500. Among them, the application executed by the users in the experimental group may be an application based on the first strategy, and the application executed by the users in the control group may be an application based on the second strategy. It should be noted that the application executed by the users in the experimental group and the application executed by the users in the control group are the same application, and the difference between the application executed by the users in the experimental group and the application executed by the users in the control group lies in the specific execution strategy. For example, the first strategy based on which the application executed by the users in the experimental group is to display advertisements only after 7 days of registration of new users in the application APP01, and the second strategy based on which the application executed by the users in the control group is to display advertisements only after 14 days of registration of new users in the application APP01. The embodiments of the present disclosure do not impose specific restrictions on the content, rules, conditions, etc. of the above-mentioned first strategy and second strategy.

[0038] The embodiments of the present disclosure can pre-set an observation period, that is, a preset time range, such as 14 days. Within the preset time range, both the users in the experimental group and the users in the control group execute the applications of their respective strategies and generate their respective business data. Among the business data, an important data included is the user retention rate of each time unit. In the Internet industry, a certain number of users start using an application within a certain period of time. After a period of time, the users who continue to use the application are considered to be retained users. The proportion of retained users to a certain number of users who started using the application is the user retention rate. Retained users and user retention rate can reflect the quality of the application and its ability to retain users. The time unit can be day, week, month, etc. Usually, the time unit can be set to day.

[0039] In step S12 , the user value difference between the users in the experimental group and the users in the control group is generated based on the user retention rate of the users in each time unit of the experimental group and the user retention rate of the users in each time unit of the control group.

[0040] In embodiments of the present disclosure, the difference in user value between the experimental group and the control group can reflect the difference between the value generated by the experimental group users when executing applications based on the first strategy and the value generated by the control group users when executing applications based on the second strategy. Generally, the more retained users and the higher the user retention rate, the more value these retained users generate when executing applications based on the corresponding strategy; conversely, the fewer retained users and the lower the user retention rate, the less value these retained users generate when executing applications based on the corresponding strategy.

[0041] In step S13 , the feasibility of the application based on the first strategy executed by the users in the experimental group is evaluated according to the user value difference.

[0042] In the embodiment of the present disclosure, it can be determined based on the user value difference whether the application based on the first strategy executed by the experimental group users can generate more value than the application based on the second strategy executed by the control group users.

[0043] The embodiment of the present disclosure provides a feasibility assessment scheme for an application, in which users in the experimental group execute an application based on a first strategy, and users in the control group execute an application based on a second strategy. Business data of the users in the experimental group within a preset time range and business data of the users in the control group within the above time range are obtained respectively. The business data may include the user retention rate of each time unit. Then, based on the user retention rate of each time unit of the users in the experimental group and the user retention rate of each time unit of the users in the control group, the user value difference between the users in the experimental group and the users in the control group is generated, and then the feasibility of the application based on the first strategy executed by the users in the experimental group is evaluated based on the user value difference.

[0044] The embodiments of the present disclosure retain the user retention rate when performing feasibility evaluation on an application, and generate user value differences based on the user retention rate. The feasibility of the application can be evaluated based on the user value differences, thereby improving the accuracy of the feasibility evaluation of the application.

[0045] In an exemplary embodiment of the present disclosure, the execution process of the above step S12 may include the following steps:

[0046] In step S21 , the average retention time of the users in the experimental group and the average retention time of the users in the control group are generated respectively.

[0047] In an embodiment of the present disclosure, the average retention duration of the experimental group users can be generated based on the user retention rate of the experimental group users in each time unit. In actual applications, the average retention duration of the experimental group users can be generated based on the user retention rate of the experimental group users in each time unit and a preset retention rate decay function of the experimental group users.

[0048] The retention rate decay function of the experimental group users can be expressed by the following formula:

[0049] p(t)=A1*exp(-b1t);

[0050] Where p represents the user retention rate of the experimental group users in each time unit, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users.

[0051] During implementation, the user retention rates for each time unit of the experimental group can be input into the retention rate decay function for the experimental group. A1 and b1 are then output from the retention rate decay function for the experimental group using a regression algorithm. The ratio of A1 to b1 is then used as the average retention duration of the experimental group's users.

[0052] For example, the user retention rates of the experimental group on each day are r1, r2, r3, ..., r 14 . They represent the user retention rate on the first day, the user retention rate on the second day, the user retention rate on the third day, ..., and the user retention rate on the fourteenth day. 14 Input to p(t), and the above r1, r2, r3, ..., r 14 The corresponding t are 1, 2, 3, ..., 14. Finally, A1 and b1 are calculated using the regression algorithm.

[0053] A1 / b1=Integral (from 0 to infinity)p(t)dt

[0054] = integral (from 0 to infinity) Pr(stay=1|t, experimental group)

[0055] = integral (from 0 to infinity) E(stay|t, experimental group)

[0056] =E(integral (from 0 to infinity) stay|t, experimental group)

[0057] That is, A1 / b1 represents the average retention time of users in the experimental group.

[0058] Similarly, the average retention duration of the control group users can be generated based on the user retention rate of the control group users in each time unit. In actual applications, the average retention duration of the control group users can be generated based on the user retention rate of the control group users in each time unit and a preset retention rate decay function for the control group users.

[0059] The retention rate decay function of the control group users can be expressed as follows:

[0060] q(t)=A2*exp(-b2t);

[0061] Where q represents the user retention rate of the control group users in each time unit, t represents the time unit, A2 represents the weight coefficient of the total retention time of the control group users, and b2 represents the weight coefficient of each time unit of the control group users.

[0062] During implementation, the user retention rate of the control group users at each time unit can be input into the control group user retention rate decay function. A2 and b2 are then output from the control group user retention rate decay function based on a regression algorithm. The ratio of A2 to b2 is then used as the average retention duration of the control group users.

[0063] For example, the user retention rates of the control group users on each day are s1, s2, s3, ..., s 14 . It represents the user retention rate on the first day, the user retention rate on the second day, the user retention rate on the third day, ..., the user retention rate on the fourteenth day. 14 Input to q(t), and the above s1, s2, s3, ..., s 14 The corresponding t are 1, 2, 3, ..., 14. Finally, A2 and b2 are calculated using the regression algorithm.

[0064] A2 / b2=Integral (from 0 to infinity)q(t)dt

[0065] = integral (from 0 to infinity) Pr(stay=1|t, control group)

[0066] = integral (from 0 to infinity) E(stay|t, control group)

[0067] =E(integral (from 0 to infinity) stay|t, control group)

[0068] That is, A2 / b2 represents the average retention time of users in the control group.

[0069] In step S22 , a user value difference is generated based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

[0070] In an embodiment of the present disclosure, the business data of the experimental group users within a preset time range may also include the average revenue per user (ARPU) of the experimental group users. ARPU refers to the average business revenue contributed by each user within a period of time (usually one month or one year). The business data of the control group users within a preset time range may also include the ARPU of the control group users. In actual applications, the product of the average retention time of the experimental group users and the ARPU of the experimental group users can be subtracted from the product of the average retention time of the control group users and the ARPU of the control group users to obtain the user value difference. The ARPU in the embodiment of the present disclosure can be expressed as the average daily ARPU. That is, the user value difference = (A1 / b1)*the average daily ARPU of the experimental group-(A2 / b2)*the average daily ARPU of the control group.

[0071] In an exemplary embodiment of the present disclosure, during the execution of the above-mentioned step S13, if the numerical value of the user value difference is greater than a preset threshold value, the application based on the first strategy executed by the users of the experimental group is determined to be a feasible application. It should be noted that the feasible application here can be understood as the value, economic benefits, etc. brought are higher than those of the application based on the second strategy executed by the users of the control group. The above-mentioned preset threshold value can be set according to actual needs. For example, the preset threshold value is set to 0. If the numerical value of the user value difference is less than or equal to the preset threshold value, the application based on the first strategy executed by the users of the experimental group is determined to be an unfeasible application. It should be noted that the unfeasible application here can be understood as the value, economic benefits, etc. brought are lower than those of the application based on the second strategy executed by the users of the control group.

[0072] In an exemplary embodiment of the present disclosure, business data may include not only user retention rates for each time unit, but also user consumption data and user application duration. User consumption data can be understood as the amount of money a user spends on applications corresponding to a policy, and user application duration can be understood as the time a user spends on applications corresponding to a policy. Furthermore, the correspondence between user application duration and consumption data can be derived using existing estimation models.

[0073] Ultimately, based on the business data of the experimental group users and the business data of the control group users, the differences in consumption data, application duration, and user value between the experimental group users and the control group users can be obtained. Since application duration is data collected during the observation period, it only represents the short-term impact on the feasibility of the application. User value differences can represent the long-term impact on the feasibility of the application. The differences in consumption data, application duration, and user value are converted into the same unit of measurement (e.g., money), and the short-term and long-term feasibility of the application based on the first strategy can be evaluated. For example, when evaluating the short-term feasibility of an application based on the first strategy, the differences in consumption data and application duration, which belong to the same unit of measurement, can be added together. If the sum is greater than 0 or a certain value, the application based on the first strategy is considered feasible in the short term. When evaluating the long-term feasibility of an application based on the first strategy, the differences in consumption data and user value, which belong to the same unit of measurement, can be added together. If the sum is greater than 0 or a certain value, the application based on the first strategy is considered feasible in the long term.

[0074] The embodiment of the present disclosure provides a feasibility assessment scheme for an application, in which users in the experimental group execute an application based on a first strategy, and users in the control group execute an application based on a second strategy. Business data of the users in the experimental group within a preset time range and business data of the users in the control group within the above time range are obtained respectively. The business data may include the user retention rate of each time unit. Then, based on the user retention rate of each time unit of the users in the experimental group and the user retention rate of each time unit of the users in the control group, the user value difference between the users in the experimental group and the users in the control group is generated, and then the feasibility of the application based on the first strategy executed by the users in the experimental group is evaluated based on the user value difference.

[0075] The embodiments of the present disclosure retain the user retention rate when performing feasibility evaluation on an application, and generate user value differences based on the user retention rate. The feasibility of the application can be evaluated based on the user value differences, thereby improving the accuracy of the feasibility evaluation of the application.

[0076] The embodiments of the present disclosure can also evaluate the feasibility of an application in the short term and the credibility in the long term based on data such as user retention rate, user consumption data, and user application time in business data, providing multiple options for feasibility evaluation of the application.

[0077] Figure 3This is a block diagram of an application feasibility assessment device according to an exemplary embodiment. The application users include an experimental group of users and a control group of users. The experimental group of users executes the application based on a first strategy, while the control group of users executes the application based on a second strategy. The device may specifically include the following modules.

[0078] An acquisition module 31 is configured to respectively acquire business data of the experimental group users within a preset time range and business data of the control group users within the same time range, wherein the business data includes a user retention rate for each time unit;

[0079] A generating module 32 is configured to generate a user value difference between the users in the experimental group and the users in the control group based on the user retention rate of the users in the experimental group at each time unit and the user retention rate of the users in the control group at each time unit;

[0080] The evaluation module 33 is configured to evaluate the feasibility of the application based on the first strategy executed by the users in the experimental group according to the user value difference.

[0081] In an exemplary embodiment of the present disclosure, the business data further includes average revenue per user (ARPU); the generating module 32 includes:

[0082] an average retention duration generating module, configured to generate an average retention duration of the experimental group users based on the user retention rate of each time unit of the experimental group users, and to generate an average retention duration of the control group users based on the user retention rate of each time unit of the control group users;

[0083] The user value difference generating module is configured to generate the user value difference based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

[0084] In an exemplary embodiment of the present disclosure, the average retention duration generating module is configured to generate the average retention duration of the experimental group users based on the user retention rate of each time unit of the experimental group users and a preset retention rate decay function of the experimental group users;

[0085] The average retention duration generation module is configured to generate the average retention duration of the control group users based on the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users.

[0086] In an exemplary embodiment of the present disclosure, the average retention duration generation module includes:

[0087] The user retention rate input module is configured to input the user retention rate of each time unit of the experimental group users into the following formula representing the retention rate decay function of the experimental group users:

[0088] p(t)=A1*exp(-b1t);

[0089] Wherein, p represents the user retention rate of the experimental group users in each time unit, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users;

[0090] A regression module is configured to obtain A1 and b1 from the retention rate decay function of the experimental group users based on a regression algorithm;

[0091] The determination module is configured to use the ratio of A1 to b1 as the average retention time of the users in the experimental group.

[0092] In an exemplary embodiment of the present disclosure, the user retention rate input module is further configured to input the user retention rate of each time unit of the control group users into the following formula representing the retention rate decay function of the control group users:

[0093] q(t)=A2*exp(-b2t);

[0094] Wherein, q represents the user retention rate of the control group users in each time unit, t represents the time unit, A2 represents the weight coefficient of the total retention time of the control group users, and b2 represents the weight coefficient of each time unit of the control group users;

[0095] The regression module is further configured to obtain A2 and b2 from the retention rate decay function of the control group users based on a regression algorithm;

[0096] The determination module is further configured to use the ratio of A2 to b2 as the average retention time of the users in the control group.

[0097] In an exemplary embodiment of the present disclosure, the user value difference generation module is configured to obtain the user value difference by subtracting the product of the average retention time of the users in the control group and the ARPU of the users in the control group from the product of the average retention time of the users in the experimental group and the ARPU of the users in the experimental group.

[0098] In an exemplary embodiment of the present disclosure, the evaluation module 33 is configured to determine that the application based on the first strategy executed by the users in the experimental group is a feasible application when the value of the user value difference is greater than a preset threshold.

[0099] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0100] Figure 4 4 is a block diagram of an electronic device for evaluating the feasibility of an application according to an exemplary embodiment. For example, the electronic device 400 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0101] Reference Figure 4 , electronic device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 406 , a multimedia component 408 , an audio component 410 , an input / output (I / O) interface 412 , a sensor component 414 , and a communication component 416 .

[0102] The processing component 402 generally controls the overall operation of the electronic device 400, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

[0103] The memory 404 is configured to store various types of data to support operations on the electronic device 400. Examples of such data include instructions for any application or method operating on the electronic device 400, contact data, phone book data, messages, images, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0104] The power supply assembly 406 provides power to the various components of the electronic device 400. The power supply assembly 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 400.

[0105] The multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front camera and / or a rear camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0106] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 400 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

[0107] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0108] The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the electronic device 400. For example, the sensor assembly 414 can detect the open / closed state of the electronic device 400, the relative positioning of components, such as the display and keypad of the electronic device 400. The sensor assembly 414 can also detect changes in the position of the electronic device 400 or a component of the electronic device 400, the presence or absence of user contact with the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and temperature changes of the electronic device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0109] The communication component 416 is configured to facilitate wired or wireless communication between the electronic device 400 and other devices. The electronic device 400 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0110] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described methods.

[0111] In an exemplary embodiment, a storage medium including instructions is further provided, such as a memory 404 including instructions, and the instructions can be executed by the processor 420 of the electronic device 400 to perform the above method. Alternatively, the storage medium can be a non-transitory computer-readable storage medium, for example, a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0112] In an exemplary embodiment, a computer program product is further provided, comprising a readable program code, which can be executed by the processor 420 of the electronic device 400 to perform the above method. Optionally, the program code can be stored in a storage medium of the electronic device 400, which can be a non-transitory computer-readable storage medium, such as a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0113] In an exemplary embodiment, an application is also provided, which includes a first strategy and a second strategy. Users of the application include experimental group users and control group users. The experimental group users execute the application based on the first strategy, and the control group users execute the application based on the second strategy.

[0114] Figure 5 FIG. 5 is a block diagram of another electronic device for evaluating the feasibility of an application according to an exemplary embodiment. For example, the electronic device 500 may be provided as a server. Figure 5 The electronic device 500 includes a processing component 522, which further includes one or more processors, and a memory resource represented by a memory 532 for storing instructions executable by the processing component 522, such as an application. The application stored in the memory 532 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 522 is configured to execute the instructions to perform the feasibility assessment method of the application.

[0115] The electronic device 500 may further include a power supply component 526 configured to perform power management of the electronic device 500, a wired or wireless network interface 550 configured to connect the electronic device 500 to a network, and an input / output (I / O) interface 558. The electronic device 500 may operate based on an operating system stored in the memory 532, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0116] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0117] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A feasibility assessment method for an application, characterized in that: Users of the application include experimental group users and control group users, the experimental group users execute the application based on a first policy, and the control group users execute the application based on a second policy, and the method includes: Obtaining business data of the experimental group users within a preset time range and business data of the control group users within the same time range, respectively, wherein the business data includes user retention rate for each time unit; generating a user value difference between the experimental group users and the control group users based on the user retention rates of the experimental group users at each time unit and the user retention rates of the control group users at each time unit; the user value difference is determined based on the average retention duration of the experimental group users and the average retention duration of the control group users; the average retention duration of the experimental group users is determined based on the user retention rates of the experimental group users at each time unit and a preset retention rate decay function for the experimental group users; the average retention duration of the control group users is determined based on the user retention rates of the control group users at each time unit and a preset retention rate decay function for the control group users; When the value of the user value difference is greater than a preset threshold, the application based on the first policy executed by the users in the experimental group is determined to be a feasible application.

2. The method according to claim 1, characterized in that The business data further includes average revenue per user (ARPU); and generating a user value difference between the experimental group users and the control group users based on the user retention rate of the experimental group users in each time unit and the user retention rate of the control group users in each time unit includes: Generating an average retention duration of the experimental group users according to the user retention rate of each time unit of the experimental group users, and generating an average retention duration of the control group users according to the user retention rate of each time unit of the control group users; The user value difference is generated according to the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

3. The method according to claim 2, characterized in that Generating the average retention duration of the experimental group users according to the user retention rate of each time unit of the experimental group users includes: Generate an average retention time of the users in the experimental group according to the user retention rate of the users in the experimental group in each time unit and a preset retention rate decay function of the users in the experimental group; Generating the average retention duration of the control group users according to the user retention rate of each time unit of the control group users includes: The average retention duration of the control group users is generated according to the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users.

4. The method according to claim 3, characterized in that Generating the average retention duration of the experimental group users according to the user retention rate of each time unit of the experimental group users and a preset retention rate attenuation function of the experimental group users includes: The user retention rate of the experimental group users in each time unit is input into the following formula representing the retention rate decay function of the experimental group users: p(t)=A1*exp(-b1t); Wherein, p represents the user retention rate of the experimental group users in each time unit, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users; Obtaining A1 and b1 from the retention rate decay function of the experimental group users based on a regression algorithm; The ratio of A1 to b1 is used as the average retention time of the users in the experimental group.

5. The method according to claim 3, characterized in that Generating the average retention duration of the control group users according to the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users includes: The user retention rate of the control group users at each time unit is input into the following formula representing the retention rate decay function of the control group users: q(t)=A2*exp(-b2t); Wherein, q represents the user retention rate of the control group users in each time unit, t represents the time unit, A2 represents the weight coefficient of the total retention time of the control group users, and b2 represents the weight coefficient of each time unit of the control group users; Obtaining A2 and b2 from the retention rate decay function of the control group users based on a regression algorithm; The ratio of A2 to b2 is used as the average retention time of the users in the control group.

6. The method according to claim 2, characterized in that Generating the user value difference based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users, includes: The user value difference is obtained by subtracting the product of the average retention time of the users in the control group and the ARPU of the users in the control group from the product of the average retention time of the users in the experimental group and the ARPU of the users in the experimental group.

7. A feasibility assessment device for an application, characterized in that: The users of the application include experimental group users and control group users, the experimental group users execute the application based on a first strategy, and the control group users execute the application based on a second strategy, and the apparatus includes: an acquisition module configured to respectively acquire business data of the experimental group users within a preset time range and business data of the control group users within the same time range, wherein the business data includes a user retention rate for each time unit; a generating module configured to generate a user value difference between the experimental group users and the control group users based on the user retention rate of the experimental group users at each time unit and the user retention rate of the control group users at each time unit; the user value difference is determined based on the average retention duration of the experimental group users and the average retention duration of the control group users; the average retention duration of the experimental group users is determined based on the user retention rate of the experimental group users at each time unit and a preset retention rate decay function of the experimental group users; the average retention duration of the control group users is determined based on the user retention rate of the control group users at each time unit and a preset retention rate decay function of the control group users; The evaluation module is configured to determine that the application based on the first strategy executed by the users in the experimental group is a feasible application when the value of the user value difference is greater than a preset threshold.

8. The device according to claim 7, characterized in that The business data further includes average revenue per user (ARPU); the generating module includes: an average retention duration generating module, configured to generate an average retention duration of the experimental group users based on the user retention rate of each time unit of the experimental group users, and to generate an average retention duration of the control group users based on the user retention rate of each time unit of the control group users; The user value difference generating module is configured to generate the user value difference based on the average retention time of the experimental group users and the average retention time of the control group users, as well as the ARPU of the experimental group users and the ARPU of the control group users.

9. The device according to claim 8, characterized in that The average retention duration generating module is configured to generate the average retention duration of the experimental group users according to the user retention rate of each time unit of the experimental group users and a preset retention rate decay function of the experimental group users; The average retention duration generation module is configured to generate the average retention duration of the control group users based on the user retention rate of each time unit of the control group users and a preset retention rate decay function of the control group users.

10. The device according to claim 8, characterized in that The average retention time generation module includes: The user retention rate input module is configured to input the user retention rate of each time unit of the experimental group users into the following formula representing the retention rate decay function of the experimental group users: p(t)=A1*exp(-b1t); Wherein, p represents the user retention rate of the experimental group users in each time unit, t represents the time unit, A1 represents the weight coefficient of the total retention time of the experimental group users, and b1 represents the weight coefficient of each time unit of the experimental group users; A regression module is configured to obtain A1 and b1 from the retention rate decay function of the experimental group users based on a regression algorithm; The determination module is configured to use the ratio of A1 to b1 as the average retention time of the users in the experimental group.

11. The device according to claim 10, characterized in that The user retention rate input module is further configured to input the user retention rate of the control group users in each time unit into the following formula representing the retention rate decay function of the control group users: q(t)=A2*exp(-b2t); Wherein, q represents the user retention rate of the control group users in each time unit, t represents the time unit, A2 represents the weight coefficient of the total retention time of the control group users, and b2 represents the weight coefficient of each time unit of the control group users; The regression module is further configured to obtain A2 and b2 from the retention rate decay function of the control group users based on a regression algorithm; The determination module is further configured to use the ratio of A2 to b2 as the average retention time of the users in the control group.

12. The device according to claim 8, characterized in that The user value difference generating module is configured to obtain the user value difference by subtracting the product of the average retention time of the users in the control group and the ARPU of the users in the control group from the product of the average retention time of the users in the experimental group and the ARPU of the users in the experimental group.

13. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

14. A storage medium, characterized in that When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method according to any one of claims 1 to 6.

15. A computer program product, characterized in that The device comprises a readable program code, which can be executed by a processor of an electronic device to implement the method according to any one of claims 1 to 6.

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