CN109214585B - User consumption prediction method and device, electronic equipment and storage medium - Google Patents

User consumption prediction method and device, electronic equipment and storage medium Download PDF

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CN109214585B
CN109214585B CN201811119739.9A CN201811119739A CN109214585B CN 109214585 B CN109214585 B CN 109214585B CN 201811119739 A CN201811119739 A CN 201811119739A CN 109214585 B CN109214585 B CN 109214585B
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user
consumption
predicted
registered
information
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CN109214585A (en
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陈伟源
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The embodiment of the invention provides a user consumption prediction method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring user information of registered users and consumption history information on a plurality of marketing activities; extracting user characteristics and consumption characteristics of the registered user from the user information and the consumption history information; training a consumption prediction model based on the user characteristics and the consumption characteristics; and predicting the consumption amount of the user to be predicted on each marketing activity in a future preset time period through the consumption prediction model. The technical scheme of the embodiment of the invention adopts an intelligent prediction scheme based on artificial intelligence analysis, and can automatically and accurately predict the consumption amount of the user in each marketing activity in a future predetermined time period, thereby being capable of carrying out accurate marketing on the user in a targeted manner.

Description

User consumption prediction method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a user consumption prediction method, a user consumption prediction apparatus, an electronic device, and a computer-readable storage medium.
Background
With the development of internet technology, a great amount of marketing activities are put on various network platforms every year, the number of newly added registered users is increasing day by day, and how to predict the consumption situation of the newly registered users in a future period becomes a focus of attention.
At present, in one technical scheme, the consumption situation of the newly registered user on each marketing activity in a future period is predicted through the user characteristics, such as age, academic calendar, occupation and the like, of the newly registered user. However, in this technical solution, it is difficult to accurately predict the consumption of the user in each marketing campaign based on the user characteristics, and accurate marketing cannot be performed on the user in a targeted manner.
Accordingly, it is desirable to provide a user consumption prediction method, a user consumption prediction apparatus, an electronic device, and a computer-readable storage medium capable of solving one or more of the above-mentioned problems.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the invention and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
An object of embodiments of the present invention is to provide a user consumption prediction method, a user consumption prediction apparatus, an electronic device, and a computer-readable storage medium, which overcome one or more of the problems due to the limitations and disadvantages of the related art, at least to some extent.
According to a first aspect of the embodiments of the present invention, there is provided a user consumption prediction method, including: acquiring user information of registered users and consumption history information on a plurality of marketing activities; extracting user characteristics and consumption characteristics of the registered user from the user information and the consumption history information; training a consumption prediction model based on the user characteristics and the consumption characteristics; and predicting the consumption amount of the user to be predicted on each marketing activity in a future preset time period through the consumption prediction model.
In some embodiments of the present invention, based on the foregoing solution, training a consumption prediction model based on the user characteristics and the consumption characteristics includes: dividing the user characteristics and the consumption characteristics into a training sample set and a verification sample set according to a preset proportion; and training the consumption prediction model based on the training sample set, and adjusting the parameters of the consumption prediction model based on the verification sample set.
In some embodiments of the present invention, based on the foregoing solution, the obtaining of the user information of the registered user and the consumption history information on a plurality of marketing campaigns comprises: acquiring the registration month of the user to be predicted based on the identification information of the user to be predicted; determining a time sequence corresponding to the future predetermined time period by adopting a time sequence reverse deduction method based on the registration month; obtaining user information of the registered user and consumption history information on a plurality of marketing campaigns based on the time series.
In some embodiments of the present invention, based on the foregoing solution, predicting, by the consumption prediction model, the consumption amount of the user to be predicted on each marketing campaign in a predetermined period of time in the future includes: predicting consumption amount of the registered user on each marketing activity in the registration month of the user to be predicted through the consumption prediction model; predicting an amount of consumption of the user to be predicted on each marketing campaign for the predetermined period of time in the future based on the amount of consumption of the registered user.
In some embodiments of the present invention, based on the foregoing solution, determining a time series corresponding to the future predetermined time period based on the registration month by using a time series reverse extrapolation method includes: dividing the future predetermined time period into a plurality of time series by months; determining a time series corresponding to the future predetermined time period using a time series back-extrapolation based on the registration month and the plurality of time series.
In some embodiments of the present invention, based on the foregoing scheme, predicting the consumption amount of the user to be predicted on each marketing campaign based on the consumption amount of the registered user in the future predetermined time period comprises: taking the ratio of the registration amount of the user to be predicted to the registration amount of the registered user as a first weight coefficient; taking the ratio of the age distribution characteristics of the user to be predicted to the age distribution characteristics of the registered user as a second weight coefficient; adjusting the consumption amount of the registered user in the registration month of the user to be predicted based on the first weight coefficient and the second weight coefficient; and predicting the consumption amount of the user to be predicted on each marketing activity in the future preset time period based on the adjusted consumption amount of the registered user.
In some embodiments of the invention, the consumption prediction model is a logistic regression model based on the foregoing scheme.
According to a second aspect of an embodiment of the present invention, there is provided a user consumption prediction apparatus, including: an information acquisition unit for acquiring user information of registered users and consumption history information on a plurality of marketing campaigns; a feature extraction unit configured to extract a user feature and a consumption feature of the registered user from the user information and the consumption history information; the model training unit is used for training a consumption prediction model based on the user characteristics and the consumption characteristics; and the prediction unit is used for predicting the consumption amount of the user to be predicted on each marketing activity in a future preset time period through the consumption prediction model.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including: a processor; and a memory having computer readable instructions stored thereon which, when executed by the processor, implement the user consumption prediction method as described in the first aspect above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the user consumption prediction method according to the first aspect described above.
In the technical solutions provided by some embodiments of the present invention, an intelligent prediction scheme based on artificial intelligence analysis is adopted, on one hand, user characteristics and consumption characteristics of registered users are extracted from user information and consumption history information of the registered users, a consumption prediction model is trained based on the user characteristics and consumption characteristics, and the model can be trained by combining the user characteristics and consumption characteristics of the registered users; on the other hand, the consumption amount of the user to be predicted on each marketing activity in the future preset time period is predicted through the consumption prediction model, the consumption amount of the user on each marketing activity in the future preset time period can be automatically and accurately predicted, and therefore accurate marketing can be performed on the user in a targeted mode.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 illustrates a flow diagram of a user consumption prediction method according to some embodiments of the invention;
FIG. 2 illustrates a flow diagram for obtaining consumption history information of a registered user using a time series back-stepping method, according to some embodiments of the invention;
FIG. 3 illustrates a schematic diagram of a time series extrapolation in accordance with some embodiments of the invention;
FIG. 4 shows a schematic block diagram of a user consumption prediction apparatus according to an exemplary embodiment of the present invention;
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
FIG. 1 illustrates a flow diagram of a user consumption prediction method according to some embodiments of the invention. Referring to fig. 1, the user consumption prediction method may include the steps of:
in step S110, user information of registered users and consumption history information on a plurality of marketing campaigns are acquired;
in step S120, extracting user features and consumption features of the registered user from the user information and the consumption history information;
in step S130, training a consumption prediction model based on the user characteristics and the consumption characteristics;
in step S140, the consumption amount of the user to be predicted on each marketing campaign in the future predetermined time period is predicted through the consumption prediction model.
According to the user consumption prediction method in the example embodiment of fig. 1, on one hand, the user characteristics and the consumption characteristics of the registered user are extracted from the user information and the consumption history information of the registered user, the consumption prediction model is trained based on the user characteristics and the consumption characteristics, and the model can be trained by combining the user characteristics and the consumption characteristics of the registered user; on the other hand, the consumption amount of the user to be predicted on each marketing activity in the future preset time period is predicted through the consumption prediction model, the consumption amount of the user on each marketing activity in the future preset time period can be automatically and accurately predicted, and therefore accurate marketing can be performed on the user in a targeted mode.
Next, a user consumption prediction method in the exemplary embodiment of fig. 1 will be described in detail.
In step S110, user information of the registered user and consumption history information on a plurality of marketing campaigns are acquired.
In an example embodiment, the user information may be information registered by the user at the website platform, and the user information may include information on the user's age, gender, income level, occupation type, academic calendar, assets, and the like. The plurality of marketing campaigns may include: marketing activities such as insurance-type activities, financing-type marketing activities, fund-type marketing activities, health-type marketing activities, and life-type marketing activities. The consumption history data of the user can comprise account detail information of the user, historical information of the marketing activity participated by the user, and historical information of products purchased by the user, such as financial products, fund products and insurance products purchased by the user.
In step S120, user characteristics and consumption characteristics of the registered user are extracted from the user information and the consumption history information.
In an example embodiment, characteristic information of the user's age, gender, income level, academic history, assets, and the like may be extracted from the user information. And extracting consumption characteristics such as consumption preference, consumption time and consumption type of the user from the consumption history information of the user.
In step S130, a consumption prediction model is trained based on the user characteristics and the consumption characteristics.
In an exemplary embodiment, the consumption prediction model is used for predicting the consumption amount of the user, and the consumption prediction model may be a logistic regression model, a neural network prediction model, or another suitable prediction model, which is not particularly limited in the present invention.
In an example embodiment, the user characteristics and the consumption characteristics may be divided into a training sample set and a verification sample set according to a predetermined ratio; and training the consumption prediction model based on the training sample set, and adjusting parameters of the consumption prediction model based on the verification sample set. Calculating parameters of the consumption prediction model based on the characteristic information and the consumption history information of the registered users in the training sample set, and adjusting the parameters of the consumption prediction model based on the characteristic information and the message history information of the registered users in the verification sample set.
In step S140, the consumption amount of the user to be predicted on each marketing campaign in the future predetermined time period is predicted through the consumption prediction model.
In an example embodiment, the user to be predicted may be a new user registered in the current month, the feature information of the user to be predicted, such as age, gender, income level, academic history, assets and the like, is extracted, and the feature information of the user to be predicted is input into the consumption prediction model, so that the consumption amount of the user to be predicted in each channel in a future predetermined time period can be obtained.
Further, in an example embodiment, a ratio of the registration amount of the user to be predicted to the registration amount of the registered user is used as a first weight coefficient; taking the ratio of the age distribution characteristics of the user to be predicted to the age distribution characteristics of the registered user as a second weight coefficient; adjusting the consumption amount of the registered user in the registration month of the user to be predicted based on the first weight coefficient and the second weight coefficient; predicting the consumption amount of the user to be predicted on each marketing activity in the future predetermined time period based on the adjusted consumption amount of the registered user.
The first weighting factor, i.e., the ratio of the registered amount, may reflect the ratio of the whole number of users, and the second weighting factor, i.e., the ratio of the age distribution characteristics, may reflect the ratio of the number of users at different ages, for example, when the ratio of the second weighting factor at 60 to 70 ages is larger, it means that the registered users have less distribution at 60 to 70 ages, i.e., the user group is less, and in order to improve the accuracy of prediction, the second weighting factor needs to be reduced.
FIG. 2 illustrates a flow diagram for obtaining consumption history information of a registered user using a time series back-stepping method according to some embodiments of the invention.
In fig. 2, in step S210, the registration month of the user to be predicted is obtained based on the identification information of the user to be predicted.
In an example embodiment, the registered month of the user to be predicted may be obtained from a target database based on the identification information of the user to be predicted, and the target database may be a MySQL database or an Oracle database, or may be a database in other forms.
In step S220, a time series corresponding to the future predetermined period of time is determined based on the registered month by a time series reverse method.
In an example embodiment, when the registered month of the user to be predicted is the 2017 year 5 month, in order to predict the consumption amount of the user to be predicted registered in the 2017 year 5 month on each marketing campaign in the next two years, a time series corresponding to the future predetermined time period is determined by a time series back-deduction method, for example, the future predetermined time period is divided into a plurality of time series monthly; determining a time series corresponding to the future predetermined period of time using a time series back-extrapolation based on the registration month and the plurality of time series, i.e., the time series of 2017 year 5 month to 2019 year 5 month corresponds to the time series of 2017 year 5 month to 2015 year 5 month, i.e., 2017 year 5 month corresponds to 2017 year 5 month, 2017 year 6 month corresponds to 2017 year 4 month, 2017 year 6 month corresponds to 2017 year 3 month, and so on, 2019 year 4 month corresponds to 2015 year 6 month, and 2019 year 5 month corresponds to 2015 year 5 month.
In step S230, user information of the registered users and consumption history information on a plurality of marketing campaigns are acquired based on the time series.
In an example embodiment, when the time series corresponding to 2017 year 5 month to 2019 year 5 month is 2017 year 5 month to 2015 year 5 month at the future predetermined time, the consumption history information of the user registered in 2017 year 5 month, the user registered in 2017 year 4 month, the user registered in 2017 year 3 month, and the user registered in 2015 year 5 month in 2017 month can be acquired.
Further, in some embodiments, the consumption amount of the registered user on each marketing campaign in the registration month of the user to be predicted is predicted by a consumption prediction model; predicting the consumption amount of the user to be predicted on each marketing activity in the future predetermined time period based on the consumption amount of the registered user.
FIG. 3 illustrates a schematic diagram of a time series extrapolation method according to some embodiments of the invention.
Referring to fig. 3, for the consumption amount of the user to be predicted in 5 months in 2017, the consumption amount of the user who has been registered in 5 months in 2017 can be predicted; for the consumption amount of the user to be predicted in 2017 in 6 months, the consumption amount of the user registered in 2017 in 4 months in 5 months in 2017 can be predicted; for the consumption amount of the user to be predicted in 2017 in 7 month, the consumption amount of the user registered in 2017 in 3 month can be predicted in 2017 in 5 month; by analogy, the consumption amount in the 4 th month in 2019 is predicted by the consumption amount in the 5 th month in 2017 of the user registered in the 6 th month in 2015, and the consumption amount in the 5 th month in 2019 is predicted by the consumption amount in the 5 th month in 2015 of the user registered in the 5 th month in 2015.
In addition, in the embodiment of the invention, a user consumption prediction device is also provided. Referring to fig. 4, the user consumption prediction apparatus 400 may include: an information acquisition unit 410, a feature extraction unit 420, a model training unit 430, and a prediction unit 440. The information acquisition unit 410 is used for acquiring user information of registered users and consumption history information on a plurality of marketing activities; the feature extraction unit 420 is configured to extract a user feature and a consumption feature of the registered user from the user information and the consumption history information; the model training unit 430 is configured to train a consumption prediction model based on the user characteristics and the consumption characteristics; the prediction unit 440 is used for predicting the consumption amount of the user to be predicted on each marketing activity in the future predetermined time period through the consumption prediction model.
In some embodiments of the present invention, based on the foregoing scheme, the model training unit 430 includes: the sample dividing unit is used for dividing the user characteristics and the consumption characteristics into a training sample set and a verification sample set according to a preset proportion; and the parameter determining unit is used for training the consumption prediction model based on the training sample set and adjusting the parameters of the consumption prediction model based on the verification sample set.
In some embodiments of the present invention, based on the foregoing scheme, the information obtaining unit 410 includes: the registration month determining unit is used for acquiring the registration month of the user to be predicted based on the identification information of the user to be predicted; the time reverse deducing unit is used for determining a time sequence corresponding to the future preset time period by adopting a time sequence reverse deducing method based on the registered month; a consumption information acquisition unit for acquiring the user information of the registered user and consumption history information on a plurality of marketing campaigns based on the time series.
In some embodiments of the invention, based on the foregoing scheme, the prediction unit 440 is configured to: predicting consumption amounts of the registered users on the respective marketing activities in the registration months of the users to be predicted through the consumption prediction model; predicting the consumption amount of the user to be predicted on each marketing activity in the future predetermined time period based on the consumption amount of the registered user.
In some embodiments of the present invention, based on the foregoing scheme, the time-reversal unit includes: a time division unit for dividing the future predetermined time period into a plurality of time series monthly; a reverse deduction unit, configured to determine a time series corresponding to the future predetermined time period by using a time series reverse deduction method based on the registration month and the plurality of time series.
In some embodiments of the present invention, based on the foregoing scheme, the prediction unit 440 is configured to: taking the ratio of the registration amount of the user to be predicted to the registration amount of the registered user as a first weight coefficient; taking the ratio of the age distribution characteristics of the user to be predicted to the age distribution characteristics of the registered user as a second weight coefficient; adjusting the consumption amount of the registered user in the registration month of the user to be predicted based on the first weight coefficient and the second weight coefficient; and predicting the consumption amount of the user to be predicted on each marketing activity in the future preset time period based on the adjusted consumption amount of the registered user.
In some embodiments of the invention, based on the foregoing scheme, the consumption prediction model is a logistic regression model.
Since each functional module of the user consumption prediction apparatus 400 according to the exemplary embodiment of the present invention corresponds to the steps of the above-described exemplary embodiment of the user consumption prediction method, it is not described herein again.
In an exemplary embodiment of the present invention, there is also provided an electronic device capable of implementing the above method.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system 500 of the electronic device shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. A drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted on the storage section 508 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the user consumption prediction method as described in the above embodiments.
For example, the electronic device may implement as shown in fig. 1: in step S110, user information of registered users and consumption history information on a plurality of marketing campaigns are acquired; in step S120, extracting user features and consumption features of the registered user from the user information and the consumption history information; in step S130, training a consumption prediction model based on the user characteristics and the consumption characteristics; in step S140, the consumption amount of the user to be predicted on each marketing campaign in the future predetermined time period is predicted through the consumption prediction model.
It should be noted that although in the above detailed description several modules or units of a device or apparatus for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (7)

1. A method for consumer consumption prediction, comprising:
acquiring user information of registered users and consumption history information on a plurality of marketing activities;
extracting user characteristics and consumption characteristics of the registered user from the user information and the consumption history information;
training a consumption prediction model based on the user characteristics and the consumption characteristics;
predicting the consumption amount of the registered user on each marketing activity in the registration month of the user to be predicted through the consumption prediction model, and predicting the consumption amount of the user to be predicted on each marketing activity in a future predetermined time period based on the consumption amount of the registered user;
wherein acquiring user information of registered users and consumption history information on a plurality of marketing activities comprises:
acquiring the registration month of the user to be predicted based on the identification information of the user to be predicted;
dividing the future scheduled time period into a plurality of time sequences according to months, and determining the time sequence corresponding to the future scheduled time period by adopting a time sequence back-deduction method based on the registration month and the time sequences;
obtaining user information of the registered user and consumption history information on a plurality of marketing campaigns based on the time series.
2. The user consumption prediction method of claim 1, wherein training a consumption prediction model based on the user characteristics and the consumption characteristics comprises:
dividing the user characteristics and the consumption characteristics into a training sample set and a verification sample set according to a preset proportion;
and training the consumption prediction model based on the training sample set, and adjusting the parameters of the consumption prediction model based on the verification sample set.
3. The user consumption prediction method of claim 1, wherein predicting the consumption amount of the user to be predicted on each marketing campaign for the predetermined period of time in the future based on the consumption amount of the registered user comprises:
taking the ratio of the registration amount of the user to be predicted to the registration amount of the registered user as a first weight coefficient;
taking the ratio of the age distribution characteristics of the user to be predicted to the age distribution characteristics of the registered user as a second weight coefficient;
adjusting the consumption amount of the registered user in the registration month of the user to be predicted based on the first weight coefficient and the second weight coefficient;
and predicting the consumption amount of the user to be predicted on each marketing activity in the future preset time period based on the adjusted consumption amount of the registered user.
4. The user consumption prediction method of claim 1, wherein the consumption prediction model is a logistic regression model.
5. A user consumption prediction apparatus, comprising:
an information acquisition unit for acquiring user information of registered users and consumption history information on a plurality of marketing campaigns;
a feature extraction unit configured to extract a user feature and a consumption feature of the registered user from the user information and the consumption history information;
the model training unit is used for training a consumption prediction model based on the user characteristics and the consumption characteristics;
the prediction unit is used for predicting the consumption amount of the registered user on each marketing activity in the registered month of the user to be predicted through the consumption prediction model and predicting the consumption amount of the user to be predicted on each marketing activity in a future preset time period based on the consumption amount of the registered user;
the method for acquiring the user information of the registered users and the consumption history information on a plurality of marketing activities comprises the following steps:
acquiring the registration month of the user to be predicted based on the identification information of the user to be predicted;
dividing the future scheduled time period into a plurality of time sequences according to months, and determining the time sequence corresponding to the future scheduled time period by adopting a time sequence reverse deduction method based on the registration month and the plurality of time sequences;
obtaining user information of the registered user and consumption history information on a plurality of marketing campaigns based on the time series.
6. An electronic device, comprising:
a processor; and
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the user consumption prediction method of any of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the user consumption prediction method of any one of claims 1 to 4.
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