CN112365282A - Marketing big data modeling method and platform - Google Patents
Marketing big data modeling method and platform Download PDFInfo
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- CN112365282A CN112365282A CN202011181002.7A CN202011181002A CN112365282A CN 112365282 A CN112365282 A CN 112365282A CN 202011181002 A CN202011181002 A CN 202011181002A CN 112365282 A CN112365282 A CN 112365282A
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Abstract
The invention discloses a marketing big data modeling method and a platform in the technical field of data modeling, which comprises the following steps: acquiring data, fusing multi-platform data and self-supporting data according to service conditions, and performing data standardization management; building a model, modeling a large amount of data by using a real-time big data processing method, and optimizing and predicting the result; analyzing results, analyzing sales conditions of similar products, and distributing marketing investment according to the sales conditions; the method comprises the steps of generating strategies, executing different marketing strategies according to the marketing investment in different stages, automatically distinguishing and executing, processing sales condition data of the same product of multiple platforms in real time through the existing Lambda framework, and communicating with experts or the same row by utilizing an communication unit in combination with self-operation data to improve marketing modes and investment.
Description
Technical Field
The invention relates to the technical field of data modeling, in particular to a marketing big data modeling method and a marketing big data modeling platform.
Background
The big data marketing is a marketing mode which is based on a large amount of data of multiple platforms and applied to the internet advertisement industry on the basis of a big data technology. Big data marketing derives from the internet industry and also acts on the internet industry. By means of multi-platform big data acquisition and the analysis and prediction capabilities of big data technologies, advertisements can be more accurate and effective, and higher investment return rate is brought to brand enterprises. In the digital era, it is a common digital marketing means to achieve a better marketing effect by utilizing big data analysis, however, the existing data modeling platform provides more machine learning training platforms, encapsulates common machine learning algorithms, is only limited in the product ecological layout or data pool of the platform, and has the advantages of other resources, cloud platforms, private data, computing accelerators and the like, and does not provide a general modeling platform for data marketing or business application, and the business application degree is not high, some needs professional technicians to complete the operation, and the service attachment degree is low, so that a platform with high business application degree and high service attachment degree and standardization degree is urgently needed to be designed.
Disclosure of Invention
The invention aims to provide a marketing big data modeling method and a marketing big data modeling platform to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a marketing big data modeling method comprises the following steps:
s1: acquiring data, fusing multi-platform data and self-supporting data according to service conditions, and performing data standardization management;
s2: building a model, modeling a large amount of data by using a real-time big data processing method, and optimizing and predicting the result;
s3: analyzing results, analyzing sales conditions of similar products, and distributing marketing investment according to the sales conditions;
s4: and strategy generation, wherein different marketing strategies are executed according to the marketing investment in different stages, and the execution is automatically distinguished.
Furthermore, the multi-platform data and the self-operation data are real-time data of multiple types of same products for simultaneous sale.
Furthermore, a Lambda framework is adopted in the model building process, the principles of offline calculation, real-time calculation, fusion of non-variability, read-write separation and complexity isolation framework are integrated, various large data assemblies of Hadoop, Kafka, Storm, Spark and Hbase are integrated, the processing of multi-platform data and self-supporting data is completed, and results are obtained.
Further, the processing method of the multi-platform data and the self-supporting data is specifically reasonable division of three-Layer architectures of a Batch Layer, a Speed Layer and a Serving Layer.
Further, in the result analysis process, according to the sales condition of the similar products, the marketing input conditions of different platforms to the product sales are divided; and in the strategy generation process, corresponding classification is carried out according to marketing investment and sales conditions, the sales conditions of the same product in different time periods are obtained, a timing mechanism is established, and sellers are automatically reminded to change marketing modes.
The platform of the marketing big data modeling method comprises a data acquisition module, a modeling unit, a data analysis module and a strategy generation module, wherein the data acquisition module is connected with the modeling unit through signals, the modeling unit is connected with the data analysis module through signals, and the data analysis module is connected with the strategy generation module through signals.
Furthermore, the data acquisition module comprises a multi-platform data acquisition unit, a data input unit, a data classification unit, a controller and a database, wherein the data classification unit is in signal connection with the multi-platform data acquisition unit, the data input unit and the controller, and the controller is in signal connection with the database.
Furthermore, the data analysis module comprises an exchange unit, a recording unit, a central processing unit, a request unit and a memory, wherein the central processing unit is in signal connection with the exchange unit, the recording unit, the request unit and the memory.
Compared with the prior art, the invention has the beneficial effects that: the marketing method and the marketing system have the advantages that the marketing mode and the input are improved by processing the sales condition data of the same product of multiple platforms in real time through the existing Lambda framework and communicating with experts or the same row by utilizing the communication unit in combination with the self-supporting data, the method is suitable for the marketing requirements of different products, the commercial application degree is high, and the business fitting degree and the standardization degree are also high.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a schematic diagram of the data acquisition module of FIG. 2;
FIG. 4 is a schematic diagram of the data analysis module of FIG. 2.
In the drawings, the components represented by the respective reference numerals are listed below:
the system comprises a data acquisition module 1, a modeling unit 2, a data analysis module 3, a strategy generation module 4, a multi-platform data acquisition unit 100, a data input unit 101, a data classification unit 102, a controller 103, a database 104, a communication unit 300, a recording unit 301, a central processing unit 302, a request unit 303 and a memory 304.
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.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1-4, the present invention provides a technical solution: a marketing big data modeling method comprises the following steps:
s1: acquiring data, fusing multi-platform data and self-supporting data according to service conditions, and performing data standardization management;
s2: building a model, modeling a large amount of data by using a real-time big data processing method, and optimizing and predicting the result;
s3: analyzing results, analyzing sales conditions of similar products, and distributing marketing investment according to the sales conditions;
s4: and strategy generation, wherein different marketing strategies are executed according to the marketing investment in different stages, and the execution is automatically distinguished.
The multi-platform data and the self-operation data are real-time data of multiple types of same products for sale at the same time.
A Lambda framework is adopted in the model building process, the principles of offline calculation, real-time calculation, fusion invariability, read-write separation and complexity isolation framework are integrated, various large data assemblies of Hadoop, Kafka, Storm, Spark and Hbase are integrated, multi-platform data and self-supporting data are processed, and results are obtained.
The processing method of the multi-platform data and the self-supporting data is specifically reasonable division of three-Layer architectures of a Batch Layer, a Speed Layer and a Serving Layer.
In the result analysis process, according to the sales condition of the similar products, the marketing input conditions of different platforms to the product sales are divided; and in the strategy generation process, corresponding classification is carried out according to marketing investment and sales conditions, the sales conditions of the same product in different time periods are obtained, a timing mechanism is established, and sellers are automatically reminded to change marketing modes.
A platform of a marketing big data modeling method comprises a data acquisition module 1, a modeling unit 2, a data analysis module 3 and a strategy generation module 4, wherein the data acquisition module 1 is in signal connection with the modeling unit 2, the modeling unit 2 is in signal connection with the data analysis module 3, the data analysis module 3 is in signal connection with the strategy generation module 4, the modeling unit 2 utilizes the existing Lambda framework and has the characteristics of high fault tolerance, low delay and expandability, the strategy generation module 4 records the conclusion obtained by the data analysis module 3 and regularly starts a reminding mechanism to guide a seller to sell through real-time monitoring data.
The data acquisition module 1 comprises a multi-platform data acquisition unit 100, a data input unit 101, a data classification unit 102, a controller 103 and a database 104, wherein the data classification unit 102 is in signal connection with the multi-platform data acquisition unit 100, the data input unit 101 and the controller 103, the controller 103 is in signal connection with the database 104, the data input unit 101 is used for inputting self-operation data, the data classification unit 102 classifies the data to enable sales data of the same product to be collected together, and the controller 103 and the database 104 are matched for data storage and use by the modeling unit 2.
The data analysis module 3 comprises an exchange unit 300, a recording unit 301, a central processing unit 302, a request unit 303 and a storage 304, the central processing unit 302 is in signal connection with the exchange unit 300, the recording unit 301, the request unit 303 and the storage 304, a seller starts the exchange unit 300 through the request unit 303 and the central processing unit 302, the exchange unit 300 completes contact exchange with a market expert or a same line, the recording unit 301 is used for inputting a sales rule acquired by the expert or the same line, and the storage 304 stores information.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A marketing big data modeling method is characterized by comprising the following steps:
s1: acquiring data, fusing multi-platform data and self-supporting data according to service conditions, and performing data standardization management;
s2: building a model, modeling a large amount of data by using a real-time big data processing method, and optimizing and predicting the result;
s3: analyzing results, analyzing sales conditions of similar products, and distributing marketing investment according to the sales conditions;
s4: and strategy generation, wherein different marketing strategies are executed according to the marketing investment in different stages, and the execution is automatically distinguished.
2. The marketing big data modeling method of claim 1, wherein: the multi-platform data and the self-operation data are real-time data of simultaneous sale of multiple types of the same products.
3. The marketing big data modeling method of claim 1, wherein: the Lambda framework is adopted in the model building process, the principles of off-line calculation, real-time calculation, fusion of non-degeneration, read-write separation and complexity isolation framework are integrated, various large data assemblies of Hadoop, Kafka, Storm, Spark and Hbase are integrated, multi-platform data and self-operation data are processed, and results are obtained.
4. The marketing big data modeling method of claim 3, wherein: the processing method of the multi-platform data and the self-operation data is specifically reasonable division of three-Layer architectures of a Batch Layer, a Speed Layer and a Serving Layer.
5. The marketing big data modeling method and platform according to claim 1, wherein: in the result analysis process, according to the sales condition of the similar products, the marketing input conditions of different platforms to the product sales are divided; and in the strategy generation process, corresponding classification is carried out according to marketing investment and sales conditions, the sales conditions of the same product in different time periods are obtained, a timing mechanism is established, and sellers are automatically reminded to change marketing modes.
6. The platform of a modeling method for marketing big data according to any one of claims 1 to 5, characterized in that: the device comprises a data acquisition module (1), a modeling unit (2), a data analysis module (3) and a strategy generation module (4), wherein the data acquisition module (1) is in signal connection with the modeling unit (2), the modeling unit (2) is in signal connection with the data analysis module (3), and the data analysis module (3) is in signal connection with the strategy generation module (4).
7. The marketing big data modeling platform of claim 6, wherein: the data acquisition module (1) comprises a multi-platform data acquisition unit (100), a data input unit (101), a data classification unit (102), a controller (103) and a database (104), wherein the data classification unit (102) is in signal connection with the multi-platform data acquisition unit (100), the data input unit (101) and the controller (103), and the controller (103) is in signal connection with the database (104).
8. The marketing big data modeling platform of claim 6, wherein: the data analysis module (3) comprises an alternating current unit (300), a recording unit (301), a central processing unit (302), a request unit (303) and a memory (304), wherein the central processing unit (302) is in signal connection with the alternating current unit (300), the recording unit (301), the request unit (303) and the memory (304).
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Cited By (1)
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| CN115471272A (en) * | 2022-09-29 | 2022-12-13 | 江苏通卡数字科技有限公司 | Business marketing big data analysis system, method and device |
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