CN117236995A - Payment rate estimation method, device, equipment and storage medium - Google Patents

Payment rate estimation method, device, equipment and storage medium Download PDF

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CN117236995A
CN117236995A CN202311110566.5A CN202311110566A CN117236995A CN 117236995 A CN117236995 A CN 117236995A CN 202311110566 A CN202311110566 A CN 202311110566A CN 117236995 A CN117236995 A CN 117236995A
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month
historical
target
payment rate
newly
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李晨
赵爽
朱琴
李晓宁
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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Abstract

The disclosure provides a payment rate estimation method, a payment rate estimation device, payment rate estimation equipment and a storage medium, relates to the technical field of data processing, in particular to the technical fields of data estimation, data management and the like, and can be applied to scenes such as payment rate estimation, life cycle value estimation and the like. The specific implementation scheme comprises the following steps: the method comprises the steps of obtaining historical data and the payment rate of a new user in a target month, determining a reference month from the historical month according to the historical data and the payment rate of the new user in the target month, and determining the estimated payment rate of the new user in the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the new user in the reference month and the payment rate of the new user in the target month. The method and the system can timely estimate the payment rate of the month after the new month of the newly added user, so that an application program operator can timely adjust the operation strategy, and cost loss is reduced.

Description

Payment rate estimation method, device, equipment and storage medium
Technical Field
The disclosure relates to the technical field of data processing, in particular to the technical fields of data prediction, data management and the like, and can be applied to the scenes of charge rate prediction, life cycle value prediction and the like, and particularly relates to a charge rate prediction method, a device, equipment and a storage medium.
Background
In order to make a precise operation policy (such as advertisement investment, welfare activity, etc.), an operator of an application program uses a payment rate of a new user in the new month and a payment rate of the month after the new month to estimate a Life Time Value (LTV) of the user, and then adjusts the operation policy according to the life time value of the user.
At present, the estimation of the payment rate of the new user in the month after the new month cannot be completed after the payment rate of the new user in the new month is obtained, so that an application program operator cannot adjust the operation strategy in time, and cost loss is caused.
Disclosure of Invention
The invention provides a payment rate estimation method, a device, equipment and a storage medium, which can estimate the payment rate of a new user in the month after the new month in time, so that an application program operator can adjust an operation strategy in time, and the cost loss is reduced.
According to a first aspect of the present disclosure, there is provided a payment rate estimation method, including:
acquiring historical data and a payment rate of a new user in a target month, wherein the historical data comprises historical payment rates of the new user in the historical month before the target month, the historical month comprises the new month and other months of the new user, and the historical payment rate comprises the historical payment rate in the new month and the historical payment rate in other months; determining a reference month from the historical months according to the historical data and the payment rate of the new user in the target month, wherein the reference month is the historical month in which the relation between the historical payment rate of the new user in the new month and the payment rate of the new user in the target month meets the preset rule; and determining the estimated payment rate of the newly added user in the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the newly added user in the reference month and the payment rate of the newly added user in the target month.
According to a second aspect of the present disclosure, there is provided a payment rate estimation device, the device comprising: the device comprises an acquisition module, a first processing module and a second processing module.
The acquisition module is used for acquiring historical data and the payment rate of the newly added user in the target month, wherein the historical data comprises the historical payment rate of the newly added user in the historical month before the target month, the historical month comprises the newly added month and other months of the newly added user, and the historical payment rate comprises the historical payment rate in the newly added month and the historical payment rate in other months.
The first processing module is used for determining a reference month from the historical months according to the historical data and the payment rate of the new user in the target month, wherein the reference month is the historical month in which the relation between the historical payment rate of the new user in the new month and the payment rate of the new user in the target month meets the preset rule.
And the second processing module is used for determining the estimated payment rate of the newly added user of the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month in the target month.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as in the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to the first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method according to the first aspect.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flowchart of a payment rate estimation method according to an embodiment of the disclosure;
FIG. 2 is a flowchart of another method for estimating a payment rate according to an embodiment of the disclosure;
FIG. 3 is a schematic flow chart of a payment rate estimation method according to an embodiment of the disclosure;
fig. 4 is a schematic diagram of a payment rate estimating apparatus according to an embodiment of the disclosure;
fig. 5 is a schematic diagram of the composition of an electronic device according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be appreciated that in embodiments of the present disclosure, the character "/" generally indicates that the context associated object is an "or" relationship. The terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated.
In order to make a precise operation policy (such as advertisement investment, welfare activity, etc.), an operator of an application program uses a payment rate of a new user in the new month and a payment rate of the month after the new month to estimate a Life Time Value (LTV) of the user, and then adjusts the operation policy according to the life time value of the user.
At present, the estimation of the payment rate of the new user in the month after the new month cannot be completed after the payment rate of the new user in the new month is obtained, so that an application program operator cannot adjust the operation strategy in time, and cost loss is caused.
For example, a payment rate of 1 month for a new user of 1 month may be obtained just after 1 month, such as at the beginning of 2 months, but a payment rate of 2 months, 3 months, and other subsequent months for a new user of 1 month cannot be estimated from the payment rate of 1 month for a new user of 1 month alone.
Under the background technology, the invention provides a payment rate estimation method, which can estimate the payment rate of a new user in the month after the new month in time, so that an application program operator can adjust an operation strategy in time, and the cost loss is reduced.
The execution subject of the payment rate estimation method provided by the embodiment of the disclosure may be a computer or a server, or may also be other electronic devices with data processing capability; alternatively, the execution subject of the method may be a processor (e.g., a central processing unit (central processing unit, CPU)) in the above-described electronic device; still alternatively, the execution subject of the method may be an Application (APP) installed in the electronic device and capable of implementing the function of the method; alternatively, the execution subject of the method may be a functional module, a unit, or the like having the function of the method in the electronic device. The subject of execution of the method is not limited herein.
For example, in some implementations, the payment rate estimation method provided by the embodiments of the present disclosure may be applied to a client, where the client may be a mobile phone, a computer, or other devices. The client can provide a user interface for a user, and the user can operate on the user interface to interact with the client. The client can respond to the operation of the user to realize the payment rate estimation method provided by the embodiment of the disclosure.
The payment rate estimation method is exemplarily described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a payment rate estimation method according to an embodiment of the disclosure. As shown in fig. 1, the method may include:
s101, historical data and the payment rate of a new user in the target month are acquired.
Wherein the history data includes a history of the newly added user for a history month before the target month, the history month includes a new month and other months of the history of the newly added user, the history of the payment rate includes a history of the payment rate at the newly added month, and a history of the payment rate at other months.
Illustratively, the user may select the desired target month by clicking, touching, etc., through a user interface provided by the client. And the client responds to the operation of selecting the target month by the user, and acquires the historical data and the payment rate of the newly added user of the target month in the target month.
For example, all payment rate data may be stored in the database, and the client may determine historical data corresponding to the target month from the database and the payment rate of the new user for the target month and obtain the payment rate for the target month in response to the user selecting the target month. Of course, the client may send the target month to the database, and the database may determine the historical data and the payment rate of the new user in the target month by itself and send the payment rate to the client, which is not limited in the manner of acquiring the payment rate data.
Illustratively, taking the current actual month as 2023 8 month and the target month as 2023 7 month as an example, the payment rate data of the new user in 2023 7 month has only one data of the payment rate of the new user in 2023 7 month, and the payment rate of each month after 2023 7 month of the new user in 2023 7 month cannot be estimated according to the one data. Of course, the target month may be a month in which the number of payment rate data is greater than one, for example, 6 months of 2023, and cannot be estimated by 2 data, which is not limited herein.
Illustratively, continuing with the current actual month being 2023 8 months and the target month being 2023 7 months as an example, the historical data includes the historical payment rates of the newly added month and the other months of the user for the historical month (such as 2023 6 months, 2023 5 months, etc.) before 2023 7 months.
For example, taking the current actual month as 2023 8 months, the target month as 2023 7 months, and the history month as 2023 5 months as an example, the new month of the history newly added user of 2023 5 months is 2023 5 months, and the other months of the history newly added user of 2023 5 months are other months than 2023 5 months. Of course, it is easily conceivable that for the history of 5 months in 2023, there is no history payment rate data before 5 months in 2023, that is, other months of the history of 5 months in 2023 include 6 months in 2023 and 7 months in 2023.
For example, continuing to take the current actual month as 2023 8 months, the target month as 2023 7 months, and the history of 2023 5 months as an example, the history of 2023 5 months of the history of 2023 months increases the history of users' charge rates, including the history of 2023 5 months of the history of users at 2023 5 months, the history of 2023 5 months of users at 2023 6 months, and the history of 2023 5 months of users at 2023 7 months.
S102, determining a reference month from the historical month according to the historical data and the payment rate of the newly added user of the target month in the target month.
The reference month is a historical month in which the relation between the historical payment rate of the new user in the new month and the payment rate of the new user in the target month meets the preset rule.
For example, the client may determine the reference month after acquiring the history data and the payment rate of the new user for the target month.
Illustratively, taking the target month as 2023 and 7 months, the historical data includes the historical payment rate of the 2023 and 6 months of the historical newly added user, the historical payment rate of the 2023 and 5 months of the historical newly added user, the historical payment rate of the 2023 and 4 months of the historical newly added user, the historical payment rate of the 2023 and 3 months of the historical newly added user and the historical payment rate of the 2023 and 2 months of the historical newly added user as an example, and the reference month can be determined in 2023 and 2 months to 2023 and 6 months.
For example, the preset rule may be that an absolute value of a difference between a historical charge rate of the newly added user in the newly added month and a charge rate of the newly added user in the target month is less than or equal to a first threshold, and the size of the first threshold is not limited.
For example, taking the preset rule as the absolute value of the difference between the historical payment rate of the newly added user in the newly added month and the payment rate of the newly added user in the target month is smaller than or equal to the first threshold, the first threshold is 0.5%, the payment rate of the newly added user in the target month is 10%, and the historical months of the historical newly added user in the newly added month in the range of [9.5% and 10.5% ] all satisfy the preset rule, so that the historical months of the historical newly added user in the newly added month in the range of [9.5% and 10.5% ] can be determined as the reference month.
S103, determining the estimated payment rate of the newly added user in the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the newly added user in the reference month and the payment rate of the newly added user in the target month.
Illustratively, taking the target month as 2023, 7 months as an example, the future months after the target month may include the following months of 2023, 8 months, 2023, 9 months, 2023, 10 months, etc.; the estimated payment rate of the future month after the target month for the newly added user of the target month may include the estimated payment rate of the newly added user of the year 2023 and the month 8, the estimated payment rate of the newly added user of the year 2023 and the month 9, the estimated payment rate of the newly added user of the year 2023 and the month 7 and the month 2023 and the like.
By way of example, the method includes performing function fitting, establishing a gray model, training a neural network model and the like according to the historical charge rate of the historical newly-added user of the reference month to obtain the corresponding relationship between the historical charge rate of the historical newly-added user of the reference month and the month, and determining the estimated charge rate of the newly-added user of the target month in the future month after the target month according to the charge rate of the newly-added user of the target month and the corresponding relationship.
According to the embodiment of the disclosure, the reference month is determined by acquiring the historical data and the payment rate of the newly added user in the target month, and the estimated payment rate of the future month after the target month of the newly added user in the target month can be accurately determined by the relation between the historical payment rate of the newly added user in the reference month and the payment rate of the newly added user in the target month and the corresponding relation between the historical payment rate of the newly added user in the reference month and the month, so that an application program operator can timely adjust an operation strategy, and cost loss is reduced.
Fig. 2 is a schematic flow chart of another payment rate estimation method according to an embodiment of the disclosure. As shown in fig. 2, determining the estimated payment rate of the future month after the target month for the newly added user of the target month according to the corresponding relationship between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month may include:
S201, according to the history of the reference month, the historical charge rate of the user is increased, and a function is preset, so that a fitting function is obtained.
The fitting function is used for representing the corresponding relation between the historical charge rate of the user and the month, wherein the historical charge rate of the user is increased according to the history of the reference month.
Illustratively, the preset function may be a polynomial function, a power function, a logarithmic function, an exponential function, or the like, which is not limited.
S202, obtaining estimated charge rates of the newly-added users in other months according to the fitting function, wherein the estimated charge rates are used in the other months.
Illustratively, after the fitting function is obtained, the positive integer independent variables (such as independent variable 1, independent variable 2, independent variable 3, etc.) may be substituted into the fitting function to obtain the corresponding dependent variable value. The value of the dependent variable corresponding to the independent variable 1 is the estimated charging rate of the history newly added user in the reference month in the newly added month, and the value of the dependent variable corresponding to the independent variable 2, the independent variable 3 and the like is the estimated charging rate of the history newly added user in the reference month in other months.
For example, with reference month of 2023 years of 3 months, the preset function is a power function, and the obtained fitting function is y= 0.0846x -0.58 For example, the values of y corresponding to the independent variables x=1, 2,3,4,5,6 can be calculated to be 8.46%, 5.66%, 4.47%, 3.79%, 3.33%, 2.99%, respectively. Then 8.46% is the estimated pay rate of the new user in 2023 month 3 in 2023, 5.66% is the estimated pay rate of the new user in 2023 month 3 in 2023 month 4, 4.47% is the estimated pay rate of the new user in 2023 month 3 in 2023 month 5, 3.79% is the estimated pay rate of the new user in 2023 month 3 in 2023 month 6, 3.33% is the estimated pay rate of the new user in 2023 month 3 in 2023 month 7, and 2.99% is the estimated pay rate of the new user in 2023 month 3 in 2023 month 8.
S203, determining the relative relation according to the historical payment rate of the new user in the new month and the payment rate of the new user in the target month.
The relative relation is used for indicating the numerical relation between the historical payment rate of the historical newly-added user of the reference month and the payment rate of the newly-added user of the target month.
For example, the relative relationship may be a difference, a ratio, or the like between the estimated payment rate of the new user in the reference month and the payment rate of the new user in the target month, which are not limited thereto.
S204, obtaining the estimated charging rate of the new user in the target month in the future month after the target month according to the estimated charging rate and the relative relation of the new user in other months in the history of the reference month.
For example, taking the example that the target month is 7 months in 2023, the payment rate of the new added user in the target month is 12.2%, the historical payment rate of the new added user in the reference month is 11.5%, the relative relationship is the ratio of the payment rate of the new added user in the target month to the estimated payment rate of the new added user in the reference month in 2023, the estimated payment rate of the new added user in the other months in 2023 is 6.00%, 4.74%, 4.02%, 3.53% and 17.53% respectively, the calculated ratio (12.2%/11.5%) of the payment rate of the new added user in the target month to the historical payment rate of the new added user in the reference month is about 1.0609, and the estimated payment rates of the new added user in 2023, 7 months, 2023, 10 months, 3, 11 months and 3, 12 months are 6.00%, 4.74%, 4.02%, 3.53% respectively.
In a possible implementation, S203 may also be performed before S201.
According to the embodiment, the fitting function capable of reflecting the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month is obtained according to the historical charge rate of the historical newly-added user of the reference month and the preset function, the estimated charge rate of the historical newly-added user of the reference month in other months is obtained, and according to the numerical relation between the historical charge rate of the historical newly-added user of the reference month in the newly-added month and the charge rate of the newly-added user of the target month in the target month and the corresponding relation between the charge rate of the historical newly-added user of the reference month and the month in the target month, the fitting function is combined, and the estimated charge rate of the future month of the newly-added user of the target month after the newly-added month is obtained, so that accurate estimation of the charge rate of the newly-added user in the month after the newly-added month can be achieved.
In a possible embodiment, the preset function is a power function, and the number of data of the historical charge rate of the user added to the history of the reference month is greater than or equal to 4.
In this embodiment, by using the power function as the preset function, the correspondence between the payment rate of the newly added user and the month of the history of the reference month can be more accurately approximated, and the accuracy of estimating the payment rate of the month of the newly added user after the newly added month can be further improved.
Fig. 3 is a schematic flow chart of a payment rate estimation method according to an embodiment of the disclosure. As shown in fig. 3, the determining the estimated charging rate of the future month after the target month for the newly added user of the target month according to the corresponding relationship between the historical charging rate of the newly added user of the reference month and the charging rate of the newly added user of the target month is determined by the data amount of the historical charging rate of the newly added user of the reference month being greater than or equal to 4, may include:
s301, generating an original sequence according to the historical charge rate of the newly added user of the history of the reference month.
Illustratively, the original sequence x may be assumed (0) ＝{x (0) (1)，x (0) (2)，...，x (0) (n) } wherein x (0) (1) Representing the historical charge rate x of the new user in the added month of the history of the reference month (0) (2) Historical charge rate x representing 1 st month of the new user with reference to month after the new month (0) (n) represents a historical charge rate of the new user of the reference month at the n-1 th month after the new month.
S302, performing level inspection according to the original sequence.
Illustratively, the level verification can be performed by the following formula:
σ(k)＝x (0) (k-1)/x (0) (k)
where k=2, 3,..n.
When meeting the requirementsWhen the level check is passed.
In some possible implementations, the original sequence may be updated when the level check fails, e.g., as original sequence x (0) Plus a positive number such that the updated original sequence passes the verification.
S303, determining that the level test passes, and performing transformation processing on the original sequence to obtain a transformation sequence.
Illustratively, after the level verification is passed, the original sequence x may be subjected to (0) Performing transformation processing to obtain a transformation sequence x (1) ＝{x (1) (1)，x (1) (2)，...，x (1) (n) }, wherein,
s304, establishing a gray model according to the transformation sequence.
The gray model is used for representing the corresponding relation between the historical charge rate of the user and the month, wherein the historical charge rate of the user is increased according to the history of the reference month.
Illustratively, the sequence x may be transformed according to (1) Generating x (1) Mean value series z of (2) (1) ＝{z (1) (2)，z (1) (3)，...，z (1) (n) }, wherein,then according to grey theory, converting sequence x (1) A gray model is established, and the form of the gray model is not limited.
In one possible embodiment, the gray model may be:
wherein a represents the development coefficient, b represents the gray action amount, x (1) Representing the transformation sequence.
By setting the gray model, the corresponding relation between the payment rate of the new user with the history of the reference month and the month can be more accurately represented, and the accuracy of estimating the payment rate of the month after the new month of the new user can be further improved.
S305, obtaining a target equation set according to the gray model.
Illustratively, one can letSolving for α by least squares method, α= (B) T B) -1 B T Y. Substituting alpha into a gray model, and solving the gray model to obtain a target equation set:
wherein the values of a and b have been obtained by solving for α, which is a known quantity; t is a positive integer.
S306, determining the estimated payment rate of the new added user of the target month in the future month after the target month according to the target equation set and the payment rate of the new added user of the target month in the target month.
Illustratively, the rate of payment by the new user for the target month at the target month may be taken as x (0) (1) Substituting into the target equation set, and changing the value of t to obtainThe newly added user as the target month estimates the charge rate at the t-th month after the target month. When t is 1, ">Equal to x (0) (1)。
For example, t may be 1, and the result isEstimated charge rate of the 1 st month after the target month as the newly added user of the target month; let t be 2, the +.>The newly added user as the target month estimates the charge rate in the 2 nd month after the target month. And obtaining the estimated charging rate of the new user of the target month in the future month after the target month by changing the value of t.
According to the embodiment, an original sequence is generated through historical charge rates of the new users with reference to the month, after the original sequence level inspection is passed, a transformation sequence with obvious trend is obtained according to the original sequence, a corresponding relation gray model capable of reflecting the charge rate of the new users with reference to the month is established according to the transformation sequence, the gray model is solved, a target equation set capable of accurately reflecting the corresponding relation between the charge rate of the new users with reference to the month is obtained, and according to the target equation set and the charge rate of the new users with reference to the month, the estimated charge rate of the new users with reference to the month after the month is obtained, and the accurate estimation of the charge rate of the new users with reference to the month after the new month is achieved.
In a possible embodiment, determining the estimated payment rate of the future month after the target month for the newly added user of the target month according to the corresponding relationship between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month, may include:
Inputting the payment rate of the newly added user of the target month in the target month into a target prediction model, and outputting the estimated payment rate of the newly added user of the target month in the future month after the target month through the target prediction model;
the target prediction model is obtained by using the historical payment rate of the historical newly-added user of the reference month in the newly-added month as the input of the neural network and using the historical payment rate of the historical newly-added user of the reference month in other months as the output training of the neural network.
According to the embodiment, the target prediction model obtained by training the historical payment rate of the newly added user of the target month at the historical payment rate of the newly added user of the reference month is input, the estimated payment rate of the future month of the newly added user of the target month after the target month is output through the target prediction model, and the target prediction model can learn the corresponding relation between the payment rate of the newly added user and the month, so that the target prediction model can accurately obtain the estimated payment rate of the future month of the newly added user of the target month after the target month according to the payment rate of the newly added user of the target month at the target month.
In one possible embodiment, the preset rule is that a difference between a historical payment rate of the newly added user at the newly added month and a payment rate of the newly added user at the target month is minimum.
For example, when the target month 2023 month 7 charging rate is 12.2% and the 2023 month 6 history charging rate is 11.3% and the 2023 month 5 history charging rate is 13.2% and the 2023 month 4 history charging rate is 13.0% and the 2023 month 3 history charging rate is 11.5% and the 2023 month 2 history charging rate is 11.3% for the 2023 month (2023 year 2 month) history charging rate is 2023.3% for the 2023 month 7 charging rate, 2023 year 7 month (2023 year 4 month) history charging rate is 2023.5% and 2023 year 2 month history charging rate is 2023 year 2 month (2023 year 2 month) history charging rate is 11.3% for the new year (2023 year 3 month).
According to the embodiment, the preset rule is set to be that the difference between the historical payment rate of the newly added user in the newly added month and the payment rate of the newly added user in the target month is minimum, so that the reference month which is closest to the target month can be determined, and the accuracy of the estimated payment rate of the newly added user in the target month in the future month after the target month is further improved.
The foregoing description of the embodiments of the present disclosure has been presented primarily in terms of methods. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. The technical aim may be to use different methods to implement the described functions for each particular application, but such implementation should not be considered beyond the scope of the present disclosure.
In an exemplary embodiment, the embodiment of the present disclosure further provides a payment rate estimation device, which may be used to implement the payment rate estimation method according to the foregoing embodiment.
Fig. 4 is a schematic diagram of a payment rate estimating apparatus according to an embodiment of the disclosure. As shown in fig. 4, the apparatus may include: an acquisition module 401, a first processing module 402 and a second processing module 403.
The obtaining module 401 is configured to obtain historical data, a payment rate of a new user in a target month, where the historical data includes a historical payment rate of a new user in a historical month before the target month, the historical month includes a new month and other months of the new user, and the historical payment rate includes a historical payment rate in the new month and a historical payment rate in other months.
The first processing module 402 is configured to determine, from the history months, a reference month according to the history data and a payment rate of the new user in the target month, where the reference month is a history month in which a relation between a history payment rate of the new user in the new month and a payment rate of the new user in the target month satisfies a preset rule.
The second processing module 403 is configured to determine an estimated payment rate of the newly added user of the target month in the future month after the target month according to the corresponding relationship between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month in the target month.
In a possible implementation manner, the second processing module 403 is specifically configured to:
obtaining a fitting function according to the historical charge rate of the newly added user of the history of the reference month and a preset function, wherein the fitting function is used for representing the corresponding relation between the historical charge rate of the newly added user of the history of the reference month and the month; obtaining estimated charge rates of the newly-added users in other months according to the fitting function; determining a relative relation according to the historical charge rate of the historical newly-added user of the reference month in the newly-added month and the charge rate of the newly-added user of the target month in the target month, wherein the corresponding relation is used for indicating the numerical relation between the historical charge rate of the historical newly-added user of the reference month in the newly-added month and the charge rate of the newly-added user of the target month in the target month; and obtaining the estimated charging rate of the new user in the target month in the future month after the target month according to the estimated charging rate and the relative relation of the new user in other months in the history of the reference month.
In one possible implementation, the preset function is a power function, and the number of data of the historical charge rate of the user added to the history of the reference month is greater than or equal to 4.
In a possible implementation manner, the number of data of the historical payment rate of the user added with reference to the history of months is greater than or equal to 4, and the second processing module 403 is specifically configured to:
Generating an original sequence according to the historical charge rate of the newly increased user in the history of the reference month; performing level inspection according to the original sequence; determining that the level test passes, and carrying out transformation processing on the original sequence to obtain a transformation sequence; according to the transformation sequence, a gray model is established, and the gray model is used for representing the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month; obtaining a target equation set according to the gray model; and determining the estimated payment rate of the new added user of the target month in the future month after the target month according to the target equation set and the payment rate of the new added user of the target month in the target month.
In one possible implementation, the gray model is:
wherein a represents the development coefficient, b represents the gray action amount, x (1) Representing the transformation sequence.
In a possible implementation manner, the second processing module 403 is specifically configured to:
inputting the payment rate of the newly added user of the target month in the target month into a target prediction model, and outputting the estimated payment rate of the newly added user of the target month in the future month after the target month through the target prediction model; the target prediction model is obtained by using the historical payment rate of the historical newly-added user of the reference month in the newly-added month as the input of the neural network and using the historical payment rate of the historical newly-added user of the reference month in other months as the output training of the neural network.
In one possible implementation, the preset rule is that a difference between a historical payment rate of the newly added user at the newly added month and a payment rate of the newly added user at the target month is minimum.
It should be noted that the division of the modules in fig. 4 is schematic, and is merely a logic function division, and other division manners may be implemented in practice. For example, two or more functions may also be integrated in one processing module. The embodiments of the present disclosure are not limited in this regard. The integrated modules may be implemented in hardware or in software functional modules.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
In an exemplary embodiment, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as in the above embodiments. The electronic device may be the computer or server described above.
In an exemplary embodiment, the readable storage medium may be a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method according to the above embodiments.
In an exemplary embodiment, the computer program product comprises a computer program which, when executed by a processor, implements a method according to the above embodiments.
Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 may also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in electronic device 500 are connected to I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the payment rate estimation method. For example, in some embodiments, the payment rate estimation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of the payment rate estimation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the payment rate estimation method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server) or that includes a middleware component (e.g., an application server) or that includes a front-end component through which a user can interact with an implementation of the systems and techniques described here, or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (17)

1. A payment rate estimation method, the method comprising:
acquiring historical data and a payment rate of an added user of a target month in the target month, wherein the historical data comprises historical payment rates of the added user of a historical month before the target month, the historical month comprises the added month of the added user and other months, and the historical payment rate comprises the historical payment rates of the added month and the historical payment rates of the other months;
Determining a reference month from the historical months according to the historical data and the payment rate of the newly added user in the target month, wherein the reference month is the historical month in which the relation between the historical payment rate of the newly added user in the history month and the payment rate of the newly added user in the target month meets a preset rule;
and determining the estimated payment rate of the new added user of the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the new added user of the reference month and the payment rate of the new added user of the target month in the target month.
2. The method of claim 1, wherein determining the estimated payment rate of the future month after the target month for the newly added user of the target month according to the correspondence between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month comprises:
obtaining a fitting function according to the historical charge rate of the historical newly-added user of the reference month and a preset function, wherein the fitting function is used for representing the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month;
Obtaining estimated charge rates of the historical newly-added users of the reference month in the other months according to the fitting function;
determining a relative relation according to the historical payment rate of the historical newly-added user of the reference month in the newly-added month and the payment rate of the newly-added user of the target month in the target month, wherein the relative relation is used for indicating the numerical relation between the historical payment rate of the historical newly-added user of the reference month in the newly-added month and the payment rate of the newly-added user of the target month in the target month;
and obtaining the estimated charging rate of the new user in the target month in the future month after the target month according to the estimated charging rate of the new user in the other months in the history of the reference month and the relative relation.
3. The method of claim 2, wherein the predetermined function is a power function, and the number of data of the historical charge rate of the historical added user of the reference month is greater than or equal to 4.
4. The method of claim 1, wherein the number of data of the historical payment rates of the historical added users of the reference month is greater than or equal to 4, and the determining the estimated payment rate of the added users of the target month in the future month after the target month according to the corresponding relationship between the historical payment rates of the historical added users of the reference month and the months, includes:
Generating an original sequence according to the historical charge rate of the newly added user in the history of the reference month;
performing level inspection according to the original sequence;
determining that the level test passes, and carrying out transformation processing on the original sequence to obtain a transformation sequence;
according to the transformation sequence, a gray model is established, wherein the gray model is used for representing the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month;
obtaining a target equation set according to the gray model;
and determining the estimated payment rate of the newly added user of the target month in the future month after the target month according to the target equation set and the payment rate of the newly added user of the target month in the target month.
5. The method of claim 4, the gray model being:
wherein a represents the development coefficient, b represents the gray action amount, x (1) Representing the transformation sequence.
6. The method of claim 1, wherein determining the estimated payment rate of the future month after the target month for the newly added user of the target month according to the correspondence between the historical payment rate of the newly added user of the reference month and the payment rate of the newly added user of the target month comprises:
Inputting the payment rate of the newly added user of the target month into a target prediction model, and outputting the estimated payment rate of the newly added user of the target month in the future month after the target month through the target prediction model;
the target prediction model is obtained by using the historical payment rate of the historical newly-added user of the reference month in the newly-added month as a neural network input and using the historical payment rate of the historical newly-added user of the reference month in other months as a neural network output training.
7. The method of any of claims 1-6, wherein the predetermined rule is that a difference between a historical payment rate of the newly added user at the newly added month and a payment rate of the newly added user at the target month is minimized.
8. A payment rate estimation device, the device comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring historical data and a payment rate of an added user in a target month, the historical data comprises a historical payment rate of a historical added user in a historical month before the target month, the historical month comprises the added month and other months of the historical added user, and the historical payment rate comprises a historical payment rate in the added month and a historical payment rate in the other months;
The first processing module is used for determining a reference month from the historical months according to the historical data and the payment rate of the newly added user in the target month, wherein the reference month is the historical month in which the relation between the historical payment rate of the newly added user in the history month and the payment rate of the newly added user in the target month meets a preset rule;
and the second processing module is used for determining the estimated payment rate of the new added user of the target month in the future month after the target month according to the corresponding relation between the historical payment rate of the new added user of the reference month and the payment rate of the new added user of the target month.
9. The apparatus of claim 8, the second processing module being specifically configured to:
obtaining a fitting function according to the historical charge rate of the historical newly-added user of the reference month and a preset function, wherein the fitting function is used for representing the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month;
obtaining estimated charge rates of the historical newly-added users of the reference month in the other months according to the fitting function;
Determining a relative relation according to the historical payment rate of the historical newly-added user of the reference month in the newly-added month and the payment rate of the newly-added user of the target month in the target month, wherein the corresponding relation is used for indicating the numerical relation between the historical payment rate of the historical newly-added user of the reference month in the newly-added month and the payment rate of the newly-added user of the target month in the target month;
and obtaining the estimated charging rate of the new user in the target month in the future month after the target month according to the estimated charging rate of the new user in the other months in the history of the reference month and the relative relation.
10. The apparatus of claim 9, the preset function being a power function, the number of data of the historical charge rate of the historical added user of the reference month being greater than or equal to 4.
11. The apparatus of claim 8, wherein the number of data of the historical charge rate of the historical added user of the reference month is greater than or equal to 4, and the second processing module is specifically configured to:
generating an original sequence according to the historical charge rate of the newly added user in the history of the reference month;
performing level inspection according to the original sequence;
Determining that the level test passes, and carrying out transformation processing on the original sequence to obtain a transformation sequence;
according to the transformation sequence, a gray model is established, wherein the gray model is used for representing the corresponding relation between the historical charge rate of the historical newly-added user of the reference month and the month;
obtaining a target equation set according to the gray model;
and determining the estimated payment rate of the newly added user of the target month in the future month after the target month according to the target equation set and the payment rate of the newly added user of the target month in the target month.
12. The apparatus of claim 11, the gray model being:
wherein a represents the development coefficient, b represents the gray action amount, x (1) Representing the transformation sequence.
13. The apparatus of claim 8, the second processing module being specifically configured to:
inputting the payment rate of the newly added user of the target month into a target prediction model, and outputting the estimated payment rate of the newly added user of the target month in the future month after the target month through the target prediction model;
the target prediction model is obtained by using the historical payment rate of the historical newly-added user of the reference month in the newly-added month as a neural network input and using the historical payment rate of the historical newly-added user of the reference month in other months as a neural network output training.
14. The apparatus of any of claims 8-13, the preset rule being that a difference between a historical payment rate of a historical addition user at an addition month and a payment rate of the addition user at the target month is minimal.
15. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-7.
CN202311110566.5A 2023-08-30 2023-08-30 Payment rate estimation method, device, equipment and storage medium Pending CN117236995A (en)

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