CN110827086A - Product marketing prediction method and device, computer equipment and readable storage medium - Google Patents
Product marketing prediction method and device, computer equipment and readable storage medium Download PDFInfo
- Publication number
- CN110827086A CN110827086A CN201911079665.5A CN201911079665A CN110827086A CN 110827086 A CN110827086 A CN 110827086A CN 201911079665 A CN201911079665 A CN 201911079665A CN 110827086 A CN110827086 A CN 110827086A
- Authority
- CN
- China
- Prior art keywords
- product
- marketing
- marketed
- target
- prediction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 230000004044 response Effects 0.000 claims abstract description 72
- 238000000605 extraction Methods 0.000 claims abstract description 24
- 238000012549 training Methods 0.000 claims description 67
- 238000013527 convolutional neural network Methods 0.000 claims description 17
- 238000012216 screening Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 238000011176 pooling Methods 0.000 claims description 7
- 238000009472 formulation Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 19
- 238000010586 diagram Methods 0.000 description 16
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000009471 action Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 235000013305 food Nutrition 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 208000008589 Obesity Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 235000020824 obesity Nutrition 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The application provides a product marketing prediction method, a product marketing prediction device, computer equipment and a readable storage medium, and relates to the field of marketing management. According to the method and the device, the consumer characteristics of the target consumer group, the product information of at least one product to be marketed and the scheme content of at least one marketing scheme to be screened are obtained, the product information of the product to be marketed and the scheme content of the marketing scheme to be screened are subjected to characteristic extraction respectively, corresponding product characteristics and marketing characteristics are obtained, then the consumer characteristics of the target consumer group, the product characteristics of all products to be marketed and the marketing characteristics of all marketing schemes to be screened are input into a product promotion prediction model for characteristic matching, and the promotion response rate of the target consumer group to various products to be marketed under different marketing schemes to be screened is obtained, so that the manual participation degree in the product marketing prediction process is reduced, the prediction precision, the prediction accuracy and the prediction efficiency are improved, and the high-precision product marketing prediction result is obtained quickly.
Description
Technical Field
The application relates to the field of marketing management, in particular to a product marketing prediction method, a product marketing prediction device, computer equipment and a readable storage medium.
Background
With the continuous development of economy, consumers can freely select and buy various products under different marketing means according to the self acceptance degree of different marketing means and the purchasing demand degree of various products. In order to ensure successful product promotion, most enterprises need to predict the promotion response degree of target consumer groups to the current products to be sold and marketing means to be used before marketing the products, so as to adjust the products and/or marketing means in time. However, currently, each industry generally carries out statistics on actual sales data of existing products, and sets different weighted influence factors for the statistical results in combination with expert opinions to carry out manual analysis, so as to complete corresponding product marketing prediction operations. In the process, the prediction result is greatly influenced by human factors, and the overall prediction precision, prediction accuracy and prediction efficiency are not high.
Disclosure of Invention
In view of this, an object of the present application is to provide a product marketing prediction method, a product marketing prediction device, a computer device, and a readable storage medium, which can implement automated operation of a product marketing prediction process, reduce human participation in a prediction process, and improve prediction accuracy, and prediction efficiency, thereby obtaining a high-accuracy product marketing prediction result quickly.
In order to achieve the above purpose, the embodiments of the present application employ the following technical solutions:
in a first aspect, an embodiment of the present application provides a product marketing prediction method, where the method includes:
acquiring consumer characteristics of a target consumer group, product information of at least one product to be marketed and scheme content of at least one marketing scheme to be screened;
respectively extracting the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and correspondingly obtaining the product characteristics of each product to be marketed and the marketing characteristics of each marketing scheme to be screened;
and inputting the consumer characteristics of the target consumer group, the product characteristics of all products to be marketed and the marketing characteristics of all marketing schemes to be screened into a product promotion prediction model for characteristic matching to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
In an alternative embodiment, the method further comprises:
screening out target products to be marketed and target marketing schemes with corresponding promotion response rates not less than a preset response rate threshold according to the promotion response rates of the target consumer groups on the products to be marketed under different marketing schemes to be screened;
and generating a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
In an alternative embodiment, the method further comprises:
and configuring corresponding preset response rate thresholds aiming at different consumer groups.
In an alternative embodiment, the method further comprises:
acquiring a training data set with a promotion response relationship, wherein the training data set comprises a plurality of reference data samples, and each reference data sample comprises consumer data of a consumer group with the promotion response relationship, product data of a marketed product and marketing content data of a used marketing scheme;
performing feature extraction on the training data set to obtain a corresponding training feature set, wherein the training feature set comprises a plurality of reference feature samples, and each reference feature sample comprises consumer features of consumer groups with promotion response relations, product features of marketed products and marketing features of a used marketing scheme;
carrying out feature preprocessing on the obtained training feature set, and inputting the preprocessed training feature set into an original convolutional neural network;
and training an input layer, a convolutional layer, a time sequence maximum pooling layer, a full-link layer, a multi-class classification layer and an output layer of the original convolutional neural network based on the preprocessed training feature set to obtain the product popularization prediction model.
In a second aspect, an embodiment of the present application provides a product marketing prediction device, including:
the data acquisition module is used for acquiring the consumer characteristics of the target consumer group, the product information of at least one product to be marketed and the scheme content of at least one marketing scheme to be screened;
the characteristic extraction module is used for respectively extracting the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and correspondingly obtaining the product characteristic of each product to be marketed and the marketing characteristic of each marketing scheme to be screened;
and the promotion prediction module is used for inputting the consumer characteristics of the target consumer group, the product characteristics of all the products to be marketed and the marketing characteristics of all the marketing schemes to be screened into the product promotion prediction model for characteristic matching to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
In an alternative embodiment, the apparatus further comprises:
the target screening module is used for screening out target products to be marketed and target marketing schemes, of which the corresponding promotion response rates are not less than a preset response rate threshold value, according to the promotion response rates of the target consumer groups to the products to be marketed under different marketing schemes to be screened;
and the strategy formulation module is used for generating a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
In an alternative embodiment, the apparatus further comprises:
and the response configuration module is used for configuring corresponding preset response rate thresholds aiming at different consumer group types.
In an optional embodiment, the apparatus further comprises a feature processing module and a model training module;
the data acquisition module is further used for acquiring a training data set with a promotion response relationship, wherein the training data set comprises a plurality of reference data samples, and each reference data sample comprises consumer data of a consumer group with the promotion response relationship, product data of a marketed product and marketing content data of a used marketing scheme;
the feature extraction module is further configured to perform feature extraction on the training data set to obtain a corresponding training feature set, where the training feature set includes a plurality of reference feature samples, and each reference feature sample includes consumer features of a consumer group having a promotion response relationship, product features of a marketed product, and marketing features of a used marketing scheme;
the characteristic processing module is used for carrying out characteristic preprocessing on the obtained training characteristic set and inputting the preprocessed training characteristic set into an original convolutional neural network;
and the model training module is used for training an input layer, a convolutional layer, a time sequence maximum pooling layer, a full connection layer, a multi-class classification layer and an output layer of the original convolutional neural network based on the preprocessed training feature set to obtain the product popularization prediction model.
In a third aspect, an embodiment of the present application provides a computer device, including a processor and a memory, where the memory stores machine executable instructions that can be executed by the processor, and the processor can execute the machine executable instructions to implement the product marketing prediction method according to any one of the foregoing embodiments.
In a fourth aspect, the present application provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the product marketing prediction method according to any one of the foregoing embodiments.
Compared with the background art, the method has the following beneficial effects:
according to the method, the characteristics of consumers of a target consumer group, the product information of at least one product to be marketed and the scheme content of at least one marketing scheme to be screened are obtained, the characteristics of the product information of each product to be marketed and the scheme content of each marketing scheme to be screened are extracted respectively to obtain the product characteristics of each product to be marketed and the marketing characteristics of each marketing scheme to be screened, and then the characteristics of the consumers of the target consumer group, the product characteristics of all products to be marketed and the marketing characteristics of all marketing schemes to be screened are input into a product promotion prediction model for characteristic matching to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened, so that the automatic operation of a product marketing prediction process is realized, the manual participation degree in the prediction process is reduced, the prediction precision is improved, and the cost is reduced, The prediction accuracy and the prediction efficiency are improved, so that a high-precision product marketing prediction result is obtained quickly.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a block diagram illustrating a computer device according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a product marketing prediction method according to an embodiment of the present disclosure;
fig. 3 is a second flowchart of a product marketing prediction method according to an embodiment of the present application;
fig. 4 is a third schematic flowchart of a product marketing prediction method according to an embodiment of the present application;
FIG. 5 is a fourth flowchart illustrating a product marketing prediction method according to an embodiment of the present application;
FIG. 6 is a functional block diagram of a product marketing prediction device according to an embodiment of the present disclosure;
FIG. 7 is a second functional block diagram of a product marketing prediction device according to an embodiment of the present application;
FIG. 8 is a third functional block diagram of a product marketing prediction device according to an embodiment of the present application;
fig. 9 is a fourth functional block diagram of a product marketing prediction device according to an embodiment of the present disclosure.
Icon: 10-a computer device; 11-a memory; 12-a processor; 13-a communication unit; 100-product marketing prediction means; 110-a data acquisition module; 120-a feature extraction module; 130-a promotion prediction module; 140-a target screening module; 150-a policy making module; 160-response configuration module; 170-feature processing module; 180-model training module.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic block diagram of a computer device 10 according to an embodiment of the present disclosure. In the embodiment of the present application, the computer device 10 may be configured to predict the promotion response degree of a specific consumer group to various products to be marketed under different marketing schemes, so as to implement automatic operation of a product marketing prediction process, reduce the human participation degree in the prediction process, and improve the prediction accuracy, the prediction accuracy and the prediction efficiency, thereby obtaining a high-precision product marketing prediction result quickly. In the embodiment, the computer device 10 may be, but is not limited to, a personal computer, a tablet computer, a server, and the like.
In the present embodiment, the computer device 10 includes a product marketing prediction device 100, a memory 11, a processor 12 and a communication unit 13. The various elements of the memory 11, the processor 12 and the communication unit 13 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the memory 11, the processor 12 and the communication unit 13 may be electrically connected to each other through one or more communication buses or signal lines.
In this embodiment, the memory 11 may be used for storing a program, and the processor 12 may execute the program accordingly after receiving the execution instruction. The Memory 11 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 11 may also be used to store a promotion prediction model, which is used to predict promotion response conditions of different consumers to different products under different marketing schemes.
In this embodiment, the processor 12 may be an integrated circuit chip having signal processing capabilities. The Processor 12 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that implements or executes the methods, steps and logic blocks disclosed in the embodiments of the present application.
In this embodiment, the communication unit 13 is configured to establish a communication connection between the computer device 10 and another device through a network, and to transmit and receive data through the network. For example, the computer device 10 acquires any one or a combination of information on a target consumer group, information on a product to be marketed, and information on a marketing plan to be used at another device through the communication unit 13.
In the present embodiment, the product marketing prediction device 100 includes at least one software function module which can be stored in the memory 11 or solidified in the operating system of the computer device 10 in the form of software or firmware. The processor 12 may be used to execute executable modules stored by the memory 11, such as software functional modules and computer programs included by the product marketing prediction device 100. The computer device 10 realizes the automatic operation of the product marketing prediction process through the product marketing prediction device 100, reduces the manual participation degree in the prediction process, and improves the prediction precision, the prediction accuracy and the prediction efficiency, thereby quickly obtaining the product marketing prediction result with high precision.
It will be appreciated that the block diagram shown in fig. 1 is merely a structural component diagram of the computer device 10, and that the computer device 10 may include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
In the present application, in order to ensure that the computer device 10 can implement automatic operation of a product marketing prediction process, reduce the manual participation degree of the prediction process, and improve the prediction accuracy, and the prediction efficiency, thereby quickly obtaining a high-accuracy product marketing prediction result, the present application implements the above functions by providing a product marketing prediction method applied to the computer device 10. The product marketing prediction method provided by the application is described correspondingly below.
Optionally, referring to fig. 2, fig. 2 is a schematic flowchart of a product marketing prediction method according to an embodiment of the present disclosure. In the embodiment of the present application, the specific flow and steps of the product marketing prediction method shown in fig. 2 are as follows.
Step S210, consumer characteristics of the target consumer group, product information of at least one product to be marketed and scheme content of at least one marketing scheme to be screened are obtained.
In this embodiment, the target consumer group may be selected by a predictor according to a requirement, and consumer characteristics of different consumer groups may be partially the same or completely different, where the consumer characteristics include age, height, gender, hobby, weight, obesity degree, payment capability, and the like of a corresponding consumer. The product to be marketed can also be selected by a predictor according to the requirement, and the product information of different products to be marketed can be partially the same or completely different, wherein the product information can be different along with the different types of the products to be marketed, for example, the product information of a hardware product comprises the appearance, color, material, function, performance, component, brand, comfort and the like of the corresponding product, and the product information of a food product comprises the appearance, smell, taste, mouth feel, safety, food material source and the like of the corresponding product. The marketing scheme to be screened can also be selected by a predictor according to the requirement, the scheme contents of different products to be marketed can be partially the same or completely different, wherein the scheme contents comprise product speakers, product packages, product promotion modes (including network promotion and offline leaflet propaganda), product sale modes (including online sale and offline sale), product sale areas, product promotion platforms and the like of corresponding schemes.
Step S220, feature extraction is respectively carried out on the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and the product features of each product to be marketed and the marketing features of each marketing scheme to be screened are correspondingly obtained.
In this embodiment, the computer device 10 may perform feature extraction operations such as keyword extraction and statement analysis on the obtained product information of each product to be marketed to obtain product features of each product to be marketed, and perform feature extraction operations such as keyword extraction and statement analysis on the obtained scheme content of each marketing scheme to be screened to obtain marketing features of each marketing scheme to be screened.
And step S230, inputting the consumer characteristics of the target consumer group, the product characteristics of all the products to be marketed and the marketing characteristics of all the marketing schemes to be screened into the product promotion prediction model for characteristic matching, and obtaining the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
In this embodiment, the product promotion prediction model is obtained by training consumer data of consumer groups having promotion response relationships, product data of marketed products, and marketing content data of used marketing schemes, and is used for predicting promotion response conditions of different consumer groups facing various products under different marketing schemes. The computer device 10 inputs the acquired consumer characteristics of the target consumer group, the product characteristics of all products to be marketed and the marketing characteristics of all marketing schemes to be screened into the product promotion prediction model, and the product promotion prediction model performs characteristic matching on the acquired consumer characteristics, product characteristics and marketing characteristics, so that the promotion response rate of the target consumer group facing different marketing schemes to be screened is calculated, the automatic operation of a product marketing prediction process is realized, the manual participation degree of the prediction process is reduced, the prediction precision, the prediction accuracy and the prediction efficiency are improved, a high-precision product marketing prediction result is rapidly obtained, the product to be marketed is not required to belong to a sold product, and the marketing scheme to be screened is not required to belong to a used scheme.
Optionally, referring to fig. 3, fig. 3 is a second flowchart of the product marketing prediction method according to the embodiment of the present application. In the embodiment of the present application, compared with the product marketing prediction method shown in fig. 2, the product marketing prediction method shown in fig. 3 may further include steps S240 and S250.
And S240, screening out the target product to be marketed and the target marketing scheme with the corresponding promotion response rate not less than a preset response rate threshold according to the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
In this embodiment, the computer device 10 stores the corresponding relationship between the types of different consumers and the preset response rate threshold, where the preset response rate thresholds corresponding to the types of different consumers may be the same or different. The computer device 10 may determine a preset response rate threshold corresponding to the target consumer group according to the type of the target consumer group, and then, according to the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened, screen out a target product to be marketed and a target marketing scheme, of which the corresponding promotion response rate is not less than the determined preset response rate threshold, from the at least one product to be marketed and the at least one marketing scheme to be screened.
And step S250, generating a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
In this embodiment, after the computer device 10 determines the target product to be marketed and the target marketing plan corresponding to the target consumer group, the information arrangement is performed on the mutually matched target product to be marketed and the target marketing plan, so as to obtain a product promotion strategy including the target product to be marketed and the target marketing plan, thereby promoting the promotion effect on the target product to be marketed.
Optionally, referring to fig. 4, fig. 4 is a third flowchart illustrating a product marketing prediction method according to an embodiment of the present application. In the embodiment of the present application, compared with the product marketing prediction method shown in fig. 3, the product marketing prediction method shown in fig. 4 may further include step S209.
Step S209, configuring corresponding preset response rate thresholds for different consumer types.
In this embodiment, the preset response rate threshold is used to represent the minimum promotion response effect that the forecaster wants to achieve for the corresponding consumer group. The preset response rate threshold values corresponding to different consumer groups can be configured by the forecaster according to requirements.
Optionally, referring to fig. 5, fig. 5 is a fourth flowchart illustrating a product marketing prediction method according to an embodiment of the present application. In the embodiment of the present application, the product marketing prediction method shown in any one of fig. 2, 3 and 4 may further include steps S205 to S208.
And step S205, acquiring a training data set with a promotion response relation.
In this embodiment, the training data set includes a plurality of reference data samples, each of which includes consumer data of a consumer group having a promotion response relationship, product data of a marketed product, and marketing content data of a used marketing plan.
And S206, performing feature extraction on the training data set to obtain a corresponding training feature set.
In this embodiment, the training feature set includes a plurality of reference feature samples, and each reference feature sample includes consumer features of a consumer group having a promotion response relationship, product features of a marketed product, and marketing features of a used marketing scheme. The computer device 10 performs feature extraction on all data in each reference data sample included in the training data set by using the feature extraction manner in step S220 to obtain the training feature set.
And step S207, performing feature preprocessing on the obtained training feature set, and inputting the preprocessed training feature set into the original convolutional neural network.
In this embodiment, the computer device 10 performs feature preprocessing operations such as feature cleaning, feature complementing and feature vectorization on the consumer features of the consumer group, the product features of the marketed product, and the marketing features of the used marketing scheme in each reference feature sample to obtain a corresponding preprocessed training feature set, and inputs the preprocessed training feature set into the original convolutional neural network for model training to train and generate the product promotion prediction model.
And S208, training an input layer, a convolutional layer, a time sequence maximum pooling layer, a full-link layer, a multi-class classification layer and an output layer of the original convolutional neural network based on the preprocessed training feature set to obtain a product popularization prediction model.
In this embodiment, the original convolutional neural network includes an input layer, a convolutional layer, a time sequence maximum pooling layer, a full-link layer, a multi-class classification layer, and an output layer, and the computer device 10 may train the original convolutional neural network by using a training feature set after preprocessing in a feature iterative feedback manner, so as to ensure that the original convolutional neural network has a capability of predicting a promotion response condition of a specific consumer group when the original convolutional neural network faces various products under different marketing schemes, thereby obtaining the product promotion prediction model.
In the present application, in order to ensure that the product marketing prediction apparatus 100 included in the computer device 10 can be normally implemented, the functions of the product marketing prediction apparatus 100 are implemented by dividing functional modules. The following describes the specific components of the product marketing prediction device 100 provided in the present application.
Optionally, referring to fig. 6, fig. 6 is a functional module schematic diagram of a product marketing prediction device 100 according to an embodiment of the present disclosure. In the embodiment of the present application, the product marketing prediction device 100 includes a data acquisition module 110, a feature extraction module 120, and a promotion prediction module 130.
The data obtaining module 110 is configured to obtain consumer characteristics of a target consumer group, product information of at least one product to be marketed, and scheme content of at least one marketing scheme to be screened.
The feature extraction module 120 is configured to perform feature extraction on the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and correspondingly obtain the product feature of each product to be marketed and the marketing feature of each marketing scheme to be screened.
The promotion prediction module 130 is configured to input the consumer characteristics of the target consumer group, the product characteristics of all products to be marketed, and the marketing characteristics of all marketing schemes to be screened into the product promotion prediction model for characteristic matching, so as to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
Optionally, referring to fig. 7, fig. 7 is a second functional module schematic diagram of the product marketing prediction device 100 according to the embodiment of the present application. In the embodiment of the present application, the product marketing prediction apparatus 100 may further include a target screening module 140 and a policy making module 150.
The target screening module 140 is configured to screen out a target product to be marketed and a target marketing scheme, of which the corresponding promotion response rate is not less than a preset response rate threshold, according to the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
The strategy formulation module 150 is configured to generate a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
Optionally, referring to fig. 8, fig. 8 is a third functional module schematic diagram of the product marketing prediction apparatus 100 according to the embodiment of the present application. In the embodiment of the present application, the product marketing prediction apparatus 100 may further include a response configuration module 160.
The response configuration module 160 is configured to configure corresponding preset response rate thresholds for different consumer group types.
Optionally, referring to fig. 9, fig. 9 is a fourth functional module schematic diagram of the product marketing prediction device 100 according to the embodiment of the present application. In the embodiment of the present application, the product marketing prediction apparatus 100 may further include a feature processing module 170 and a model training module 180.
The data obtaining module 110 is further configured to obtain a training data set with a promotion response relationship, where the training data set includes a plurality of reference data samples, and each reference data sample includes consumer data of a consumer group with a promotion response relationship, product data of a marketed product, and marketing content data of a used marketing scheme.
The feature extraction module 120 is further configured to perform feature extraction on the training data set to obtain a corresponding training feature set, where the training feature set includes a plurality of reference feature samples, and each reference feature sample includes consumer features of a consumer group having a promotion response relationship, product features of a marketed product, and marketing features of a used marketing scheme.
The feature processing module 170 is configured to perform feature preprocessing on the obtained training feature set, and input the preprocessed training feature set into the original convolutional neural network.
The model training module 180 is configured to train an input layer, a convolutional layer, a time sequence maximum pooling layer, a full link layer, a multi-class classification layer, and an output layer of the original convolutional neural network based on the preprocessed training feature set, so as to obtain a product promotion prediction model.
It should be noted that the basic principle and the generated technical effect of the product marketing prediction apparatus 100 provided in the embodiment of the present application are the same as those of the product marketing prediction method described above, and for a brief description, reference may be made to the corresponding description contents for the product marketing prediction method described above for the sake of brevity.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In summary, in the product marketing prediction method, the product marketing prediction device, the computer device, and the readable storage medium provided by the present application, the consumer characteristics of the target consumer group, the product information of at least one product to be marketed, and the scenario content of at least one marketing scenario to be screened are obtained, the product information of each product to be marketed and the scenario content of each marketing scenario to be screened are respectively subjected to characteristic extraction, so as to obtain the product characteristics of each product to be marketed and the marketing characteristics of each marketing scenario to be screened, and then the consumer characteristics of the target consumer group, the product characteristics of all products to be marketed, and the marketing characteristics of all marketing scenarios to be screened are input into the product promotion prediction model for characteristic matching, so as to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing scenarios to be screened, therefore, automatic operation of the product marketing prediction process is realized, the manual participation degree in the prediction process is reduced, the prediction precision, the prediction accuracy and the prediction efficiency are improved, and the high-precision product marketing prediction result is quickly obtained.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A product marketing prediction method, the method comprising:
acquiring consumer characteristics of a target consumer group, product information of at least one product to be marketed and scheme content of at least one marketing scheme to be screened;
respectively extracting the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and correspondingly obtaining the product characteristics of each product to be marketed and the marketing characteristics of each marketing scheme to be screened;
and inputting the consumer characteristics of the target consumer group, the product characteristics of all products to be marketed and the marketing characteristics of all marketing schemes to be screened into a product promotion prediction model for characteristic matching to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
2. The method of claim 1, further comprising:
screening out target products to be marketed and target marketing schemes with corresponding promotion response rates not less than a preset response rate threshold according to the promotion response rates of the target consumer groups on the products to be marketed under different marketing schemes to be screened;
and generating a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
3. The method of claim 2, further comprising:
and configuring corresponding preset response rate thresholds aiming at different consumer groups.
4. The method according to any one of claims 1-3, further comprising:
acquiring a training data set with a promotion response relationship, wherein the training data set comprises a plurality of reference data samples, and each reference data sample comprises consumer data of a consumer group with the promotion response relationship, product data of a marketed product and marketing content data of a used marketing scheme;
performing feature extraction on the training data set to obtain a corresponding training feature set, wherein the training feature set comprises a plurality of reference feature samples, and each reference feature sample comprises consumer features of consumer groups with promotion response relations, product features of marketed products and marketing features of a used marketing scheme;
carrying out feature preprocessing on the obtained training feature set, and inputting the preprocessed training feature set into an original convolutional neural network;
and training an input layer, a convolutional layer, a time sequence maximum pooling layer, a full-link layer, a multi-class classification layer and an output layer of the original convolutional neural network based on the preprocessed training feature set to obtain the product popularization prediction model.
5. A product marketing prediction apparatus, the apparatus comprising:
the data acquisition module is used for acquiring the consumer characteristics of the target consumer group, the product information of at least one product to be marketed and the scheme content of at least one marketing scheme to be screened;
the characteristic extraction module is used for respectively extracting the product information of each product to be marketed and the scheme content of each marketing scheme to be screened, and correspondingly obtaining the product characteristic of each product to be marketed and the marketing characteristic of each marketing scheme to be screened;
and the promotion prediction module is used for inputting the consumer characteristics of the target consumer group, the product characteristics of all the products to be marketed and the marketing characteristics of all the marketing schemes to be screened into the product promotion prediction model for characteristic matching to obtain the promotion response rate of the target consumer group to each product to be marketed under different marketing schemes to be screened.
6. The apparatus of claim 5, further comprising:
the target screening module is used for screening out target products to be marketed and target marketing schemes, of which the corresponding promotion response rates are not less than a preset response rate threshold value, according to the promotion response rates of the target consumer groups to the products to be marketed under different marketing schemes to be screened;
and the strategy formulation module is used for generating a product promotion strategy corresponding to the target product to be marketed according to the obtained target product to be marketed and the target marketing scheme.
7. The apparatus of claim 6, further comprising:
and the response configuration module is used for configuring corresponding preset response rate thresholds aiming at different consumer group types.
8. The apparatus according to any one of claims 5-7, wherein the apparatus further comprises a feature processing module and a model training module;
the data acquisition module is further used for acquiring a training data set with a promotion response relationship, wherein the training data set comprises a plurality of reference data samples, and each reference data sample comprises consumer data of a consumer group with the promotion response relationship, product data of a marketed product and marketing content data of a used marketing scheme;
the feature extraction module is further configured to perform feature extraction on the training data set to obtain a corresponding training feature set, where the training feature set includes a plurality of reference feature samples, and each reference feature sample includes consumer features of a consumer group having a promotion response relationship, product features of a marketed product, and marketing features of a used marketing scheme;
the characteristic processing module is used for carrying out characteristic preprocessing on the obtained training characteristic set and inputting the preprocessed training characteristic set into an original convolutional neural network;
and the model training module is used for training an input layer, a convolutional layer, a time sequence maximum pooling layer, a full connection layer, a multi-class classification layer and an output layer of the original convolutional neural network based on the preprocessed training feature set to obtain the product popularization prediction model.
9. A computer device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor to implement the product marketing prediction method of any one of claims 1-4.
10. A readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the product marketing prediction method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911079665.5A CN110827086A (en) | 2019-11-07 | 2019-11-07 | Product marketing prediction method and device, computer equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911079665.5A CN110827086A (en) | 2019-11-07 | 2019-11-07 | Product marketing prediction method and device, computer equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110827086A true CN110827086A (en) | 2020-02-21 |
Family
ID=69553041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911079665.5A Pending CN110827086A (en) | 2019-11-07 | 2019-11-07 | Product marketing prediction method and device, computer equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110827086A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111340546A (en) * | 2020-02-25 | 2020-06-26 | 中信银行股份有限公司 | Method, device, computer equipment and readable storage medium for improving marketing efficiency of banking business |
| CN111639972A (en) * | 2020-05-29 | 2020-09-08 | 中国联合网络通信集团有限公司 | Multimedia data delivery method and device |
| CN112270572A (en) * | 2020-11-03 | 2021-01-26 | 恩亿科(北京)数据科技有限公司 | Automated marketing method, system, electronic device and computer readable storage medium |
| CN112529634A (en) * | 2020-12-18 | 2021-03-19 | 恩亿科(北京)数据科技有限公司 | Transformation link analysis method and system based on big data and computer equipment |
| CN113626538A (en) * | 2021-07-14 | 2021-11-09 | 上海齐网网络科技有限公司 | Medical information intelligent classification method and system based on big data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160148271A1 (en) * | 2014-11-20 | 2016-05-26 | Adobe Systems Incorporated | Personalized Marketing Based on Sequence Mining |
| CN108416625A (en) * | 2018-02-28 | 2018-08-17 | 阿里巴巴集团控股有限公司 | The recommendation method and apparatus of marketing product |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
-
2019
- 2019-11-07 CN CN201911079665.5A patent/CN110827086A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160148271A1 (en) * | 2014-11-20 | 2016-05-26 | Adobe Systems Incorporated | Personalized Marketing Based on Sequence Mining |
| CN108416625A (en) * | 2018-02-28 | 2018-08-17 | 阿里巴巴集团控股有限公司 | The recommendation method and apparatus of marketing product |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111340546A (en) * | 2020-02-25 | 2020-06-26 | 中信银行股份有限公司 | Method, device, computer equipment and readable storage medium for improving marketing efficiency of banking business |
| CN111639972A (en) * | 2020-05-29 | 2020-09-08 | 中国联合网络通信集团有限公司 | Multimedia data delivery method and device |
| CN112270572A (en) * | 2020-11-03 | 2021-01-26 | 恩亿科(北京)数据科技有限公司 | Automated marketing method, system, electronic device and computer readable storage medium |
| CN112529634A (en) * | 2020-12-18 | 2021-03-19 | 恩亿科(北京)数据科技有限公司 | Transformation link analysis method and system based on big data and computer equipment |
| CN113626538A (en) * | 2021-07-14 | 2021-11-09 | 上海齐网网络科技有限公司 | Medical information intelligent classification method and system based on big data |
| CN113626538B (en) * | 2021-07-14 | 2023-11-24 | 上海德衡数据科技有限公司 | Medical information intelligent classification method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110827086A (en) | Product marketing prediction method and device, computer equipment and readable storage medium | |
| US11436617B2 (en) | Behavior pattern search system and behavior pattern search method | |
| CN107040397A (en) | A kind of service parameter acquisition methods and device | |
| US20210241293A1 (en) | Apparatuses, computer-implemented methods, and computer program products for improved model-based determinations | |
| CN111681051A (en) | Purchasing intention degree prediction method, device, storage medium and terminal | |
| KR101567684B1 (en) | Method for selecting recommendation type in product recommeding system | |
| CN110704706B (en) | Training method and classification method of classification model, related equipment and classification system | |
| CN111966886A (en) | Object recommendation method, object recommendation device, electronic equipment and storage medium | |
| CN111861679A (en) | Commodity recommendation method based on artificial intelligence | |
| JP7210958B2 (en) | Product recommendation device and program | |
| CN108595580B (en) | News recommendation method, device, server and storage medium | |
| CN111523914A (en) | User satisfaction evaluation method, device and system and data display platform | |
| CN113159213A (en) | Service distribution method, device and equipment | |
| CN116151857A (en) | Marketing model construction method and device | |
| US11798036B2 (en) | Real-time predictive recommendation system using per-set optimization | |
| CN111984637B (en) | Missing value processing method and device in data modeling, equipment and storage medium | |
| CN110992071B (en) | Service strategy making method and device, storage medium and electronic equipment | |
| CN111046285B (en) | Recommendation ordering determining method, device, server and storage medium | |
| CN109191192B (en) | Data estimation method, apparatus and computer-readable storage medium | |
| CN109934604B (en) | Sales data processing method and system, storage medium and electronic equipment | |
| CN110956027A (en) | Method and device for generating digital short message content | |
| CN112765448A (en) | User label mining method, device, server and storage medium | |
| KR20200122652A (en) | Nutrient Profiling-based Pet Food Recommendation System | |
| CN111915339A (en) | Data processing method, device and equipment | |
| CN110866605A (en) | Data model training method and device, electronic equipment and readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200221 |