CN115936749A - Activity information pushing method and device - Google Patents
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- CN115936749A CN115936749A CN202211505158.5A CN202211505158A CN115936749A CN 115936749 A CN115936749 A CN 115936749A CN 202211505158 A CN202211505158 A CN 202211505158A CN 115936749 A CN115936749 A CN 115936749A
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Abstract
The invention provides an activity information pushing method and device, and relates to the technical field of artificial intelligence. The activity information pushing method comprises the following steps: acquiring customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain activity response types of all customers in the current customer group; determining the response rate of each activity in the current passenger group according to the activity response type and the number of passengers in the current passenger group; and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate. The invention can push accurate activity information to the guest group, thereby improving the customer experience.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an activity information pushing method and device.
Background
At present, with the development of internet technology, more and more business personnel configure marketing activities through certain media (such as mobile phone APP and short messages). If the response effect of the marketing campaign can be known in real time, business personnel can adjust the configuration strategy of the marketing campaign in time.
The evaluation of the response effect of the marketing campaign is usually important in three stages of before the marketing campaign is configured, after the marketing campaign is on-line and after the marketing campaign is finished. Generally speaking, before marketing campaign configuration, a campaign marketing effect is often required to be estimated, and a marketing strategy with the maximum benefit is formulated according to an estimation result; after the marketing campaign is online, the marketing campaign may be influenced by external environmental factors such as weather, natural disasters and the like, and the marketing strategy needs to be updated in real time; after the marketing campaign is finished, the business personnel need to know the response effect of the marketing campaign so that the business personnel can finish the work of summarizing a report and the like.
Therefore, the appropriate activity information is pushed to the client, so that the response effect can be monitored in real time in each life cycle of the activity, the quality and the quantity are guaranteed, and the marketing of the activity is efficiently completed.
The prior art only predicts the customers, is not suitable for the active customer groups, and cannot provide an accurate activity information pushing scheme.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a method and a device for pushing activity information, so as to push accurate activity information to a customer group and improve customer experience.
In order to achieve the above object, an embodiment of the present invention provides an activity information pushing method, including:
obtaining customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model established based on training characteristic data to obtain activity response types of all customers in the current customer group;
determining the response rate of each activity in the current passenger group according to the activity response type and the number of passengers in the current passenger group;
and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
In one embodiment, creating the activity response model based on the training feature data comprises:
acquiring historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
screening from historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics to obtain training characteristic data;
and training the initial model according to the training characteristic data and the corresponding actual activity type to obtain an activity response model.
In one embodiment, the step of obtaining training characteristic data by screening from the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics comprises the following steps:
determining the accumulated contribution rate of the characteristic data; the characteristic data comprises historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as training feature data.
In one embodiment, pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate comprises:
and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
An embodiment of the present invention further provides an activity information pushing apparatus, including:
the acquisition module is used for acquiring the client information characteristics, the client behavior characteristics and the environment characteristics in the current client group, inputting the client information characteristics, the client behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data, and acquiring the activity response type of each client in the current client group;
the response rate module is used for determining the response rate of each activity in the current passenger group according to the activity response type of each client in the current passenger group and the number of passengers in the passenger group;
and the pushing module is used for pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
In one embodiment, the method further comprises the following steps: an active response model creation module to:
acquiring historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics to obtain training characteristic data;
and training the initial model according to the training characteristic data and the corresponding actual activity type to obtain an activity response model.
In one embodiment, the activity response model creation module is specifically configured to:
determining the accumulated contribution rate of the characteristic data; the characteristic data comprises historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as training feature data.
In one embodiment, the pushing module is specifically configured to:
and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of the activity information pushing method are implemented.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the activity information pushing method.
Embodiments of the present invention further provide a computer program product, which includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the steps of the activity information pushing method are implemented.
According to the activity information pushing method and device, the client information characteristics, the client behavior characteristics and the environment characteristics in the current guest group are input into the activity response model to obtain the activity response types of all clients in the current guest group, the response rate of all activities in the current guest group is determined according to the activity response types of all clients in the current guest group and the number of the guests in the guest group, and finally the corresponding activity information is pushed to the clients in the guest group according to the comparison result of the response rate of all the activities and the target response rate, so that accurate activity information can be pushed to the guest group, and the client experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of an activity information pushing method in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for pushing activity information according to another embodiment of the present invention;
FIG. 3 is a flow diagram of creating an active response model in an embodiment of the present invention;
fig. 4 is a block diagram of the configuration of the activity information pushing apparatus in the embodiment of the present invention;
fig. 5 is a block diagram of a computer device in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
The terms involved in the present invention are explained as follows:
response rate: the probability of response of the active client is predicted by the data.
Adaboot: an iterative algorithm trains different classifiers, and a strong final classifier can be formed by collecting a plurality of classifiers.
In view of the fact that the prior art only predicts customers, is not suitable for activity customer groups, and cannot provide an accurate activity information pushing scheme, the embodiment of the invention provides an activity information pushing method, which can realize screening of potential features in different life cycles of an activity, so as to realize estimation of marketing response rate before marketing activity, during marketing activity and after marketing activity is finished. In addition, the method and the system can predict the marketing customer response condition in real time before the marketing campaign is configured and after the marketing campaign is on line, and give prompt information according to the prediction condition. The present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an activity information pushing method in an embodiment of the present invention. Fig. 2 is a flowchart of an activity information pushing method according to another embodiment of the present invention. As shown in fig. 1-2, the activity information pushing method includes:
s101: and obtaining the customer information characteristics, the customer behavior characteristics and the environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain the activity response type of each customer in the current customer group.
FIG. 3 is a flow diagram of creating an active response model in an embodiment of the present invention. As shown in FIG. 3, creating an activity response model based on training feature data includes:
s201: and acquiring historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics.
In specific implementation, a user can select a historical activity type, historical customer groups or characteristic data of customers from a management end, and clean the characteristic data, including processing missing values and filtering abnormal values, filtering empty activities and filtering invalid activities (namely activities which are not of the business system or activities which have failed), normalizing data and the like. For example: in the sex characteristic data of the client, 0 represents female, 1 represents male, and if non-0, non-1 or null value exists, the sex characteristic data represents unknown.
The historical customer information features the age, academic history, industry and the like of the customer. The historical client behavior characteristics comprise the clicking behavior of the client (such as the function of the top three of the ranking of the clicking amount), the activity participation behavior (activities of participating in telephone charge recharging, having good gift for check-in and the like), the browsing duration coefficient of the client key page (such as the browsing duration of the check-in activity is more than 10 s) and the like. The historical environmental characteristics are potential characteristics, and if a scenic spot card punching activity is configured for a certain region passenger group, the natural environment has influence on the activity.
S202: and screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics to obtain training characteristic data.
In one embodiment, S202 includes:
determining the accumulated contribution rate of the characteristic data; wherein the characteristic data comprises historical customer information characteristics, historical customer behavior characteristics and historical environmental characteristics.
In specific implementation, when the number of the historical clients is n, and the three types of characteristics, such as the historical client information characteristic, the historical client behavior characteristic and the historical environment characteristic, include p characteristics, the sample matrix is:
after normalization, the sample matrix is:
the corresponding covariance matrix is:
The eigenvalues of the covariance matrix are: lambda [ alpha ] 1 ≥λ 2 ≥...≥λ p ≥0；λ p Is the characteristic value of the p-th characteristic.
The eigenvectors of the covariance matrix are represented as:
a p is the feature vector of the p-th feature. />
The contribution rate of the ith feature is:
the cumulative contribution of the ith feature is:
2. and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as training feature data.
And the feature value corresponding to the accumulated contribution rate is the finally selected feature, and the corresponding feature vector is training feature data. For example, when the cumulative contribution rate is greater than 0.8, the corresponding feature value is selected, and the feature vector corresponding to each feature value is the finally selected feature and the corresponding feature data.
S203: and training the initial model according to the training characteristic data and the corresponding actual activity type to obtain an activity response model.
The activity types comprise equity activities, client promotion activities, asset promotion activities, check-in activities, entry activities, financing activities and the like.
During specific implementation, historical characteristic data such as historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics are divided into a training set and a test set according to the proportion of 7:3 to train an initial Adaboot model until accuracy rate is converged, and the most accurate training model is used as an activity response model. A0 in the actual activity type indicates no response and a 1 indicates a response.
S102: and determining the response rate of each activity in the current customer group according to the activity response type and the number of the customers in the current customer group.
In specific implementation, a marketing campaign period is received, and if the marketing campaign period is before the campaign period, the response rate of each campaign in the whole customer group is calculated according to the campaign response type of each customer in the current customer group and the proportion of the number of the response people of each campaign in the whole customer group to the number of the customer group is used as the response rate of each campaign. And if the activity is finished, calculating the prediction response rate of the activity, comparing the model prediction result with the actual result to determine whether the model needs to be updated, and storing the characteristic data and the corresponding activity type into a database as training data.
S103: and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
In one embodiment, S103 includes: and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
During specific implementation, the activity effect rate of the guest group can be pushed to business personnel, so that the business personnel can conveniently check and analyze the activity effect rate.
The execution subject of the activity information pushing method shown in fig. 1 may be a computer. As can be seen from the process shown in fig. 1, the activity information pushing method according to the embodiment of the present invention first inputs the client information characteristics, the client behavior characteristics, and the environment characteristics in the current client group into the activity response model to obtain the activity response types of each client in the current client group, then determines the response rate of each activity in the current client group according to the activity response types of each client in the current client group and the number of clients in the client group, and finally pushes the corresponding activity information to the clients in the client group according to the comparison result between the response rate of each activity and the target response rate, so that accurate activity information can be pushed to the client group, and the client experience is improved.
The specific process of the embodiment of the invention is as follows:
1. and acquiring historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics.
2. And determining the importance parameters of the feature data, sequencing the importance parameters from large to small, and determining the feature data corresponding to the importance parameters sequenced before the preset threshold value as training feature data.
3. And training the initial model according to the training characteristic data and the corresponding actual activity type to obtain the activity response model.
4. And acquiring the customer information characteristics, the customer behavior characteristics and the environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model to obtain the activity response type of each customer in the current customer group.
5. And determining the response rate of each activity in the current customer group according to the activity response type and the number of the customers in the current customer group.
6. And pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
In summary, the activity information pushing method provided by the embodiment of the present invention has the following beneficial effects:
(1) The analysis and screening of a plurality of characteristics such as basic information characteristics of customers, behavior characteristics of customers, environmental characteristics, activity characteristics and the like are completed, and model analysis can be carried out in real time according to potential characteristics concerned by services;
(2) The method is suitable for activity response analysis of different life cycles before activity configuration, after activity online, after activity ending and the like, and can adaptively complete prediction and result estimation according to different life cycles, thereby being beneficial to developing of attention activities at all times;
(3) Effective activity information is pushed to the client, so that the client experience is improved, marketing with better effect is realized under the condition of low cost, the marketing cost is saved, and the marketing efficiency is improved;
(4) The method has certain expandability and can be applied to other marketing activities in non-financial fields, such as: traditional household appliance industry, retail industry and the like.
(5) Having availability, the response activity given the target customer base facilitates real-time tracking analysis of customers to improve marketing strategies in due course.
(6) Visualization is convenient for business personnel to check and analyze.
Based on the same inventive concept, the embodiment of the present invention further provides an activity information pushing apparatus, and as the principle of the apparatus for solving the problem is similar to the activity information pushing method, the implementation of the apparatus may refer to the implementation of the method, and the repeated parts are not described again.
Fig. 4 is a block diagram of the activity information pushing apparatus in the embodiment of the present invention. As shown in fig. 4, the activity information pushing apparatus includes:
the acquisition module is used for acquiring the client information characteristics, the client behavior characteristics and the environment characteristics in the current client group, inputting the client information characteristics, the client behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data, and acquiring the activity response type of each client in the current client group;
the response rate module is used for determining the response rate of each activity in the current passenger group according to the activity response type of each client in the current passenger group and the number of passengers in the passenger group;
and the pushing module is used for pushing the corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
In one embodiment, the method further comprises the following steps: an active response model creation module to:
obtaining historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics to obtain training characteristic data;
and training the initial model according to the training characteristic data and the corresponding actual activity type to obtain an activity response model.
In one embodiment, the activity response model creation module is specifically configured to:
determining the accumulated contribution rate of the characteristic data; the characteristic data comprises historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as training feature data.
In one embodiment, the pushing module is specifically configured to:
and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
In practical application, the activity information pushing device comprises:
and the input module comprises an acquisition module and is used for providing a management end for business personnel to complete a data input function. Screening of the support campaign life cycle (before marketing campaign configuration, during marketing campaign on-line, marketing campaign completed); supporting an input target response rate; and supporting the presentation of the analysis content.
And the model training module comprises an activity response model creating module and is used for taking the historical customer characteristic data, the activity characteristic data and the like as training set training models.
And the response rate calculation module comprises a response rate module and is used for finishing the functions of data cleaning, analysis and screening of various characteristics and response rate calculation.
And the result pushing module comprises a pushing module used for pushing the activity information corresponding to the guest group.
To sum up, the activity information pushing device of the embodiment of the present invention inputs the client information characteristics, the client behavior characteristics, and the environment characteristics in the current guest group into the activity response model to obtain the activity response types of each client in the current guest group, determines the response rate of each activity in the current guest group according to the activity response types of each client in the current guest group and the number of the guests in the current guest group, and pushes corresponding activity information to the clients in the guest group according to the comparison result between the response rate of each activity and the target response rate, so that accurate activity information can be pushed to the guest group, and the client experience is improved.
The embodiment of the present invention further provides a specific implementation manner of a computer device, which can implement all the steps in the activity information pushing method in the foregoing embodiment. Fig. 5 is a block diagram of a computer device in an embodiment of the present invention, and referring to fig. 5, the computer device specifically includes the following:
a processor (processor) 501 and a memory (memory) 502.
The processor 501 is configured to call a computer program in the memory 502, and the processor implements all the steps in the activity information pushing method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
obtaining customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain activity response types of each customer in the current customer group;
determining the response rate of each activity in the current passenger group according to the activity response type and the number of passengers in the current passenger group;
and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
To sum up, the computer device of the embodiment of the present invention first inputs the client information characteristics, the client behavior characteristics, and the environment characteristics in the current guest group into the activity response model to obtain the activity response types of each client in the current guest group, then determines the response rate of each activity in the current guest group according to the activity response types of each client in the current guest group and the number of the guest group, and finally pushes the corresponding activity information to the clients in the guest group according to the comparison result between the response rate of each activity and the target response rate, so that accurate activity information can be pushed to the guest group, and the client experience is improved.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the activity information pushing method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps in the activity information pushing method in the foregoing embodiment, for example, when the processor executes the computer program, implements the following steps:
acquiring customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain activity response types of all customers in the current customer group;
determining the response rate of each activity in the current passenger group according to the activity response type and the number of the current customers in the passenger group;
and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
To sum up, the computer-readable storage medium according to the embodiment of the present invention inputs the customer information characteristics, the customer behavior characteristics, and the environment characteristics of the current customer group into the activity response model to obtain the activity response types of the customers in the current customer group, determines the response rates of the activities in the current customer group according to the activity response types of the customers in the current customer group and the number of the customers in the customer group, and pushes the corresponding activity information to the customers in the customer group according to the comparison result between the response rates of the activities and the target response rate, so that accurate activity information can be pushed to the customer group, and customer experience is improved.
An embodiment of the present invention further provides a computer program product capable of implementing all the steps in the activity information pushing method in the foregoing embodiment, where the computer program product includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the computer program/instruction implements all the steps in the activity information pushing method in the foregoing embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
acquiring customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain activity response types of all customers in the current customer group;
determining the response rate of each activity in the current passenger group according to the activity response type and the number of passengers in the current passenger group;
and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
To sum up, the computer program product of the embodiment of the present invention first inputs the client information characteristics, the client behavior characteristics, and the environment characteristics in the current guest group into the activity response model to obtain the activity response types of each client in the current guest group, then determines the response rate of each activity in the current guest group according to the activity response types of each client in the current guest group and the number of the guest group, and finally pushes the corresponding activity information to the clients in the guest group according to the comparison result between the response rate of each activity and the target response rate, so that accurate activity information can be pushed to the guest group, and the client experience is improved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or units, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described in the embodiments of the present invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can comprise, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and that can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
Claims (11)
1. An activity information pushing method, comprising:
obtaining customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, and inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data to obtain activity response types of each customer in the current customer group;
determining the response rate of each activity in the current passenger group according to the activity response type of each client in the current passenger group and the number of passengers in the passenger group;
and pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
2. The activity information pushing method according to claim 1, wherein creating the activity response model based on the training feature data comprises:
acquiring historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics to obtain training characteristic data;
and training an initial model according to the training characteristic data and the corresponding actual activity type to obtain the activity response model.
3. The activity information pushing method according to claim 2, wherein the step of screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environmental characteristics to obtain training characteristic data comprises the steps of:
determining the accumulated contribution rate of the characteristic data; wherein the characteristic data comprises the historical customer information characteristics, the historical customer behavior characteristics, and the historical environmental characteristics;
and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as the training feature data.
4. The method according to claim 1, wherein pushing the corresponding activity information to the clients in the current client group according to the comparison result between the response rate of each activity and the target response rate comprises:
and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
5. An activity information pushing apparatus, comprising:
the acquisition module is used for acquiring customer information characteristics, customer behavior characteristics and environment characteristics in the current customer group, inputting the customer information characteristics, the customer behavior characteristics and the environment characteristics into an activity response model created based on training characteristic data, and acquiring the activity response type of each customer in the current customer group;
the response rate module is used for determining the response rate of each activity in the current passenger group according to the activity response type of each client in the current passenger group and the number of the passengers in the current passenger group;
and the pushing module is used for pushing corresponding activity information to the clients in the current client group according to the comparison result of the response rate of each activity and the target response rate.
6. The activity information pushing apparatus according to claim 5, further comprising: an active response model creation module to:
obtaining historical customer information characteristics, historical customer behavior characteristics and historical environment characteristics;
screening the historical customer information characteristics, the historical customer behavior characteristics and the historical environment characteristics to obtain training characteristic data;
and training an initial model according to the training characteristic data and the corresponding actual activity type to obtain the activity response model.
7. The activity information pushing apparatus according to claim 6, wherein the activity response model creating module is specifically configured to:
determining the accumulated contribution rate of the characteristic data; wherein the characteristic data comprises the historical customer information characteristics, the historical customer behavior characteristics, and the historical environmental characteristics;
and comparing the accumulated contribution rate with a preset threshold value, and determining the feature data corresponding to the accumulated contribution rate larger than the preset threshold value as the training feature data.
8. The activity information pushing device according to claim 5, wherein the pushing module is specifically configured to:
and when the response rate of the activity is greater than or equal to the target response rate, pushing corresponding activity information to the clients in the current client group.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the activity information pushing method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the activity information pushing method according to any one of claims 1 to 4.
11. A computer program product comprising computer programs/instructions, characterized in that said computer programs/instructions, when executed by a processor, implement the steps of the activity information pushing method of any of claims 1 to 4.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211505158.5A CN115936749A (en) | 2022-11-28 | 2022-11-28 | Activity information pushing method and device |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211505158.5A CN115936749A (en) | 2022-11-28 | 2022-11-28 | Activity information pushing method and device |
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| CN115936749A true CN115936749A (en) | 2023-04-07 |
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| CN202211505158.5A Pending CN115936749A (en) | 2022-11-28 | 2022-11-28 | Activity information pushing method and device |
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| CN (1) | CN115936749A (en) |
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