Plann generation method and device, server and storage medium

Abstract

The invention relates to the field of data processing, and discloses a plan generation method and device, a server and a storage medium. The method comprises the following steps: collecting marketing data of a target product from a preset data source, wherein the marketing data comprises market data and operation data; data mining operation and data analysis operation are executed on the marketing data, market insight data are obtained, and the market insight data comprise market trend information, consumer behavior mode information and market pricing information; according to the market insight data, plan information corresponding to the target product is generated, and the plan information comprises current pricing information and promotion activity information. In the embodiment of the invention, the real-time performance of product plan generation is ensured.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a plan generation method, device, server and storage medium. Background Art

[0002] In the fast-moving consumer goods (FMCG) industry, effective management of product programs is critical to maintaining competitiveness and market share.

[0003] Traditional product plan generation methods rely on historical data and regular manual analysis, which makes it impossible for marketing decisions to quickly adapt to market changes, such as fluctuations in consumer trends and changes in competitor strategies. It can be seen that the efficiency of product plan generation is low. A new technical means is needed to solve the above technical problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem of low efficiency in generating product plans.

[0005] A first aspect of the present invention provides a method for generating a plan, the method comprising: Collect marketing data of the target product from a preset data source, wherein the marketing data includes market data and operation data; Performing data mining and data analysis operations on the marketing data to obtain market insight data, wherein the market insight data includes market trend information, consumer behavior pattern information, and market pricing information; According to the market insight data, plan information corresponding to the target product is generated, and the plan information includes current pricing information and promotion activity information.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the step of performing data mining and data analysis operations on the marketing data to obtain market insight data includes: Based on the marketing data, the data analysis operation is performed to obtain the market trend information and the consumer behavior pattern information, and based on the marketing data, the data mining operation is performed to obtain the market pricing information.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of generating planning information corresponding to the target product according to the market insight data includes: Determine a price sensitivity coefficient based on the consumer behavior pattern information, the market pricing information and preset historical market insight data, and, based on preset competitor information, filter out target pricing information corresponding to the competitor information from the market pricing information; The current pricing information is calculated based on the price sensitivity coefficient and the target pricing information, and the promotion activity information corresponding to the target product is determined based on the market insight data.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of generating planning information corresponding to the target product according to the market insight data includes: According to the market insight data and preset business rule information, the promotion activity information corresponding to the target product is generated, and according to the market insight data, the current pricing information corresponding to the target product is determined.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the step of performing data mining operations and data analysis operations on the marketing data to obtain market insight data includes: Performing a data aggregation operation on the marketing data to obtain first marketing data; Performing a standardization operation on the first marketing data to obtain second marketing data; The data analysis operation is performed on the second marketing data to obtain the market insight data.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of executing a preset RPA task to collect initial marketing data of the target product from at least one of the preset data sources includes: Executing a preset RPA task to collect initial marketing data of the target product from at least one of the preset data sources; Data preprocessing is performed on the initial marketing data to obtain the marketing data, wherein the data preprocessing includes data cleaning operations and data format conversion operations.

[0011] Optionally, in a sixth implementation manner of the first aspect of the present invention, the step of generating planning information corresponding to the target product according to the market insight data includes: Inputting the market insight data into a pre-trained first neural network model to obtain the current pricing information output by the first neural network model, and inputting the market insight data into a pre-trained second neural network model to obtain the promotion activity information output by the second neural network model; The plan information corresponding to the target product is determined according to the current pricing information and the promotion activity information.

[0012] A second aspect of the present invention provides a plan generation device, comprising a memory and a processor, wherein the processor is used to execute program instructions stored in the memory so that the plan generation device executes the above-mentioned plan generation method.

[0013] A third aspect of the present invention provides a server, comprising a memory and a processor, wherein the processor is configured to execute program instructions stored in the memory so that the server executes the above-mentioned plan generation method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned plan generation method.

[0015] In an embodiment of the present invention, marketing data of a target product is collected from a preset data source, and the marketing data includes market data and operational data; data mining operations and data analysis operations are performed on the marketing data to obtain market insight data, and the market insight data includes market trend information, consumer behavior pattern information, and market pricing information; based on the market insight data, plan information corresponding to the target product is generated, and the plan information includes current pricing information and promotional activity information. By automatically collecting and analyzing market and operational data, the decision-making response speed can be improved. By generating plans in real time, market changes such as fluctuations in consumption trends and adjustments in competitors' strategies can be responded to in a timely manner. At the same time, by using data mining technology to mine market trends, consumer behavior and pricing information, accurate market insights can be provided for the plan, which improves the effectiveness of the plan and promotes the increase in product market share and sales. Compared with the traditional method that relies on historical data and manual analysis, it ensures real-time performance while reducing manual intervention and related costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an embodiment of a method for generating a plan in an embodiment of the present invention; Figure 2 A schematic diagram of a specific embodiment of step 101 of the method for generating a plan in an embodiment of the present invention; Figure 3 A schematic diagram of a specific embodiment of step 101 of the method for generating a plan in an embodiment of the present invention; Figure 4 A schematic diagram of an embodiment of a generating device planned in an embodiment of the present invention; Figure 5 FIG. 1 is a schematic diagram of an embodiment of a server in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The embodiment of the present invention provides a plan generation method, device, server and storage medium.

[0018] The embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments disclosed in the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments disclosed in the present invention are only for exemplary purposes and are not intended to limit the scope of protection disclosed in the present invention.

[0019] In the description of the embodiments disclosed in the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] The terms involved in the method of generating the plan include but are not limited to: For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the method for generating a plan in an embodiment of the present invention includes: 101. Collect marketing data of the target product from a preset data source, where the marketing data includes market data and operation data; Specifically, a preset data source is configured and initialized. The data source may cover various aspects of market and internal operation data, including but not limited to sales data, customer feedback, social media trends, competitor pricing information, and supply chain status.

[0021] The initialization of the data source includes setting the API interface, RPA task parameters, internal system interface, etc.

[0022] Execute the preset RPA tasks to collect the initial marketing data of the target product from the configured data source. The initial marketing data includes but is not limited to sales records, customer reviews, discussions on social media, competitor price information, etc.

[0023] Preprocess the collected initial marketing data, including but not limited to data cleaning (removing duplicate, erroneous or incomplete data records) and data format conversion (unifying data in different formats) to obtain marketing data that can be used for subsequent analysis.

[0024] 102. Performing data mining and data analysis operations on the marketing data to obtain market insight data, where the market insight data includes market trend information, consumer behavior pattern information, and market pricing information; Specifically, according to the marketing data, the data analysis operation is performed to obtain the market trend information and the consumer behavior pattern information, and, according to the marketing data, the data mining operation is performed to obtain the market pricing information. Among them, the data analysis operation is mainly used to obtain market trend information and consumer behavior pattern information, while the data mining operation focuses on obtaining market pricing information. The distinction makes the entire data processing flow clearer and improves the fit between the data processing results and the initial data.

[0025] Furthermore, data mining operations are performed on the pre-processed marketing data. For example, hidden patterns and association rules in the data are discovered through machine learning algorithms to obtain market pricing information, such as competitor pricing strategies, market average prices, etc.

[0026] Furthermore, data analysis operations are performed on the marketing data, including but not limited to statistical analysis, time series analysis, etc., to extract market trend information and consumer behavior pattern information. The market trend information and consumer behavior pattern information may include the changing trend of market demand, changes in consumer purchasing preferences, etc.

[0027] Specifically, based on the results of data mining and data analysis, market insight data is integrated. Market insight data includes market trend information, consumer behavior pattern information and market pricing information. Market insight data can provide a basis for subsequent planning.

[0028] 103. Generate planning information corresponding to the target product based on the market insight data, where the planning information includes current pricing information and promotional activity information.

[0029] Based on market insight data, use pricing optimization algorithms and promotion management tools to generate planning information for target products.

[0030] In terms of pricing, the price sensitivity coefficient is determined by considering consumer behavior pattern information, market pricing information and preset historical market insight data. At the same time, the target pricing information is screened out based on the preset competitor information. Then, the optimization algorithm is used to comprehensively consider the price sensitivity coefficient and target pricing information to calculate the current pricing information.

[0031] Promotional information can be generated based on market insight data and preset business rules, including determining the type, time, and scope of the promotion.

[0032] Finally, current pricing information and promotion information are integrated into planning information.

[0033] The generated plan information is output to the user interface for relevant personnel to review and adjust. Relevant personnel can formulate specific sales strategies and action plans based on the plan information, such as adjusting prices, planning promotional activities, optimizing inventory management, etc.

[0034] In an embodiment of the present invention, marketing data of a target product is collected from a preset data source, and the marketing data includes market data and operational data; data mining operations and data analysis operations are performed on the marketing data to obtain market insight data, and the market insight data includes market trend information, consumer behavior pattern information, and market pricing information; based on the market insight data, plan information corresponding to the target product is generated, and the plan information includes current pricing information and promotional activity information. By automatically collecting and analyzing market and operational data, the decision-making response speed can be improved. By generating plans in real time, market changes such as fluctuations in consumption trends and adjustments in competitors' strategies can be responded to in a timely manner. At the same time, by using data mining technology to mine market trends, consumer behavior and pricing information, accurate market insights can be provided for the plan, which improves the effectiveness of the plan and promotes the increase in product market share and sales. Compared with the traditional method that relies on historical data and manual analysis, it ensures real-time performance while reducing manual intervention and related costs.

[0035] The formulation of product plans usually relies on traditional market research and data analysis methods. Traditional methods require manual collection and processing of a large amount of market data and operational data, which is time-consuming, labor-intensive and prone to errors. Based on this, the present invention proposes an optional embodiment.

[0036] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a specific embodiment of step 103 of the method for generating a plan in an embodiment of the present invention. Step 103 also includes the following specific implementation methods: 1031. Determine a price sensitivity coefficient based on the consumer behavior pattern information, the market pricing information, and preset historical market insight data, and, based on preset competitor information, select target pricing information corresponding to the competitor information from the market pricing information; Specifically, market insight data is obtained from the previous steps, including market trend information, consumer behavior pattern information, and market pricing information.

[0037] The price sensitivity coefficient is calculated by a preset algorithm or model based on consumer behavior pattern information, market pricing information and preset historical market insight data. The sensitivity coefficient reflects the consumer's sensitivity to price changes.

[0038] Based on the preset competitor information, the pricing information related to competitors is filtered out from the market pricing information as the target pricing information.

[0039] 1032. Calculate the current pricing information based on the price sensitivity coefficient and the target pricing information, and determine the promotional activity information corresponding to the target product based on the market insight data.

[0040] Specifically, the price sensitivity coefficient and target pricing information are used to calculate the current pricing information through pricing strategies or algorithms. The current pricing information takes into account multiple factors such as market competition, consumer demand, and price sensitivity.

[0041] Based on market insight data (which may include market trends, consumer behavior patterns, etc.), combined with the company's promotion strategy and preset business rules information, determine the promotion information corresponding to the target product. Integrate the current pricing information and promotion information to form a complete plan information.

[0042] Optionally, based on the market insight data and preset business rule information, the promotional activity information corresponding to the target product is generated, and based on the market insight data, the current pricing information corresponding to the target product is determined. By combining the market insight data and the preset business rule information, the consumer preferences, purchasing habits and market trends of the target market can be accurately identified, so as to formulate promotional activity information that better meets consumer needs, so as to improve the participation and conversion rate of promotional activities.

[0043] In steps 1031-1032, by comprehensively considering consumer behavior patterns, market pricing information and historical market insight data, as well as competitors' pricing strategies, the price sensitivity coefficient and target pricing information can be determined more scientifically, thereby improving pricing accuracy and market competitiveness.

[0044] Without data aggregation and standardization, data from different data sources may have problems such as inconsistent formats and inconsistent units. These problems will lead to inaccurate and unreliable data analysis results, thereby affecting the formulation of plans. Based on this, the present invention proposes an optional embodiment.

[0045] Reference Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a specific embodiment of step 102 of the method for generating a plan in an embodiment of the present invention. Step 102 also includes the following specific implementation methods: 1021. Perform a data aggregation operation on the marketing data to obtain first marketing data; Specifically, a data aggregation operation is performed on the collected marketing data, with the purpose of integrating marketing data from different data sources, different formats or different time periods to obtain a unified data set (first marketing data).

[0046] 1022. Perform a standardization operation on the first marketing data to obtain second marketing data; Specifically, standardization operations are performed on the aggregated first marketing data. Standardization operations include data cleaning (such as removing duplicate data, processing missing values, etc.) and data format conversion (such as unifying data formats, unit conversion, etc.) to ensure data consistency and comparability. This step obtains a second marketing data that has been cleaned and format-converted.

[0047] 1023. Perform the data analysis operation on the second marketing data to obtain the market insight data.

[0048] Specifically, data analysis operations are performed on the standardized secondary marketing data. The purpose is to extract valuable information and insights from the data through data analysis techniques and methods (such as statistical analysis, machine learning, etc.) to form market insight data. Market insight data includes market trend information, consumer behavior pattern information, market pricing information, etc.

[0049] In steps 1021-1023, data aggregation and standardization operations are performed to ensure that the data input into the data analysis phase is complete, accurate, and consistent, thereby improving the accuracy and reliability of data analysis and obtaining more valuable market insight data.

[0050] For the generation of the plan, the enterprise may need to manually collect data from multiple data sources, which is time-consuming and error-prone. At the same time, the data formats and standards between different data sources may be inconsistent, resulting in difficulties in data integration and analysis. Based on this, the present invention proposes an optional embodiment.

[0051] Step 101 also includes the following specific implementations: 1011. Execute a preset RPA task to collect initial marketing data of the target product from at least one of the preset data sources; Specifically, according to the preset RPA task configuration, the data collection process is automatically started in at least one preset data source. The data source can be the company's internal database, external market research report, social media platform, etc.

[0052] The RPA task collects initial marketing data of the target product in the preset data source. The initial marketing data may include sales data, market share, advertising expenditure, consumer feedback, etc.

[0053] 1012. Perform data preprocessing on the initial marketing data to obtain the marketing data, wherein the data preprocessing includes data cleaning operations and data format conversion operations.

[0054] Specifically, the initial marketing data collected may contain erroneous, duplicated or inconsistently formatted information. Therefore, the data is preprocessed to ensure the accuracy and reliability of subsequent analysis. The data preprocessing steps include data cleaning operations (such as removing duplicate data, processing missing values, correcting erroneous data, etc.) and data format conversion operations (such as unifying data formats, converting data units, etc.).

[0055] After data preprocessing, the final marketing data for analysis is generated. The marketing data will serve as input for subsequent data mining and data analysis operations.

[0056] In steps 1011-1012, the execution of RPA tasks realizes the automation of data collection and reduces the possibility of manual intervention and errors. At the same time, the automation of data preprocessing steps also improves the efficiency of data processing and shortens the cycle of plan generation.

[0057] Plans are usually made by manually analyzing market data and operational data. This is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in inaccurate and inefficient planning information. Based on this, the present invention proposes an optional embodiment.

[0058] Step 103 also includes the following specific implementations: 1033. Input the market insight data into a pre-trained first neural network model to obtain the current pricing information output by the first neural network model, and input the market insight data into a pre-trained second neural network model to obtain the promotion activity information output by the second neural network model; Specifically, the marketing data of the target product is collected, including market data and operational data. The marketing data is preprocessed, such as data cleaning and data format conversion, to obtain a data format that can be used for neural network input.

[0059] The first neural network model is trained using historical market insight data and other relevant information, which is used to output current pricing information. A second neural network model is trained using a similar dataset, which is used to output promotion information.

[0060] For example, collect market data and operational data from the past few years, including sales volume, sales, prices, promotions, etc. Clean and organize the data.

[0061] Extract features from the data that are relevant to current pricing and promotions, such as market trends, consumer behavior patterns, competitor pricing, etc.

[0062] The above features are standardized or normalized to improve the training effect of the model.

[0063] The extracted features and corresponding target variables (such as current pricing and promotion information) are used to train a neural network model.

[0064] Apply trained models to new market insights to generate current pricing and promotion information.

[0065] Formulate corresponding sales strategies and promotion plans based on the generated planning information.

[0066] 1034. Determine the plan information corresponding to the target product according to the current pricing information and the promotion activity information.

[0067] Specifically, the current market insight data is input into the first neural network model to obtain the output current pricing information.

[0068] The same market insight data is input into the second neural network model to obtain the output promotion information.

[0069] In steps 1011-1012, the neural network model can learn and predict based on a large amount of historical data and current market insight data, thereby improving the accuracy of planning information. Compared with traditional manual analysis methods, the use of neural network models can also generate planning information faster, thereby improving work efficiency.

[0070] Reference Figure 4, the embodiment of the present disclosure provides a plan generation device 400, including a plan generation device body, and a plan generation device 405. The plan generation device 405 includes a processor (processor) 404 and a memory (memory) 401 storing program instructions. Optionally, the device may also include a communication interface (CommunicationInterface) 402 and a bus 403. Among them, the processor 404, the communication interface 402, and the memory 401 can complete mutual communication through the bus 403. The communication interface 402 can be used for information transmission. The processor 404 can call the program instructions in the memory 401 to execute the plan generation method of the above embodiment. In addition, the logical instructions in the above-mentioned memory 401 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. The memory 401, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 404 executes the functional application and data processing by running the program instructions / modules stored in the memory 401, that is, realizing the generation method of the plan in the above embodiment. The memory 401 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 401 may include a high-speed random access memory and may also include a non-volatile memory.

[0071] Reference Figure 5 , the embodiment of the present disclosure provides a server 500, including a processor (processor) 504 and a memory (memory) 501 storing program instructions. Optionally, the server may also include a communication interface (CommunicationInterface) 502 and a bus 503. The processor 504, the communication interface 502, and the memory 501 may communicate with each other through the bus 503. The communication interface 502 may be used for information transmission. The processor 504 may call the program instructions in the memory 501 to execute the plan generation method of the above embodiment.

[0072] In addition, the logic instructions in the above-mentioned memory 501 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. The memory 501, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 504 executes functional applications and data processing by running the program instructions / modules stored in the memory 501, that is, implements the planned generation method in the above-mentioned embodiment. The memory 501 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 501 may include a high-speed random access memory and may also include a non-volatile memory.

[0073] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0074] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0075] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0076] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0077] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for generating a plan, characterized in that: The method for generating the plan includes: Collect marketing data of the target product from a preset data source, wherein the marketing data includes market data and operation data; Performing data mining and data analysis operations on the marketing data to obtain market insight data, wherein the market insight data includes market trend information, consumer behavior pattern information, and market pricing information; According to the market insight data, plan information corresponding to the target product is generated, and the plan information includes current pricing information and promotion activity information.

2. The method for generating a plan according to claim 1, characterized in that: The step of performing data mining and data analysis on the marketing data to obtain market insight data includes: Based on the marketing data, the data analysis operation is performed to obtain the market trend information and the consumer behavior pattern information, and based on the marketing data, the data mining operation is performed to obtain the market pricing information.

3. The method for generating a plan according to claim 1, characterized in that: The step of generating planning information corresponding to the target product according to the market insight data includes: Determine a price sensitivity coefficient based on the consumer behavior pattern information, the market pricing information and preset historical market insight data, and, based on preset competitor information, filter out target pricing information corresponding to the competitor information from the market pricing information; The current pricing information is calculated based on the price sensitivity coefficient and the target pricing information, and the promotion activity information corresponding to the target product is determined based on the market insight data.

4. The method for generating a plan according to claim 1, characterized in that: The step of generating planning information corresponding to the target product according to the market insight data comprises: According to the market insight data and preset business rule information, the promotion activity information corresponding to the target product is generated, and according to the market insight data, the current pricing information corresponding to the target product is determined.

5. The method for generating a plan according to claim 1, characterized in that: The step of performing data mining and data analysis on the marketing data to obtain market insight data includes: Performing a data aggregation operation on the marketing data to obtain first marketing data; Performing a standardization operation on the first marketing data to obtain second marketing data; The data analysis operation is performed on the second marketing data to obtain the market insight data.

6. The method for generating a plan according to claim 1, characterized in that: The step of executing a preset RPA task to collect initial marketing data of the target product from at least one of the preset data sources includes: Executing a preset RPA task to collect initial marketing data of the target product from at least one of the preset data sources; Data preprocessing is performed on the initial marketing data to obtain the marketing data, wherein the data preprocessing includes data cleaning operations and data format conversion operations.

7. The method for generating a plan according to claim 1, characterized in that: The step of generating planning information corresponding to the target product according to the market insight data comprises: Inputting the market insight data into a pre-trained first neural network model to obtain the current pricing information output by the first neural network model, and inputting the market insight data into a pre-trained second neural network model to obtain the promotion activity information output by the second neural network model; The plan information corresponding to the target product is determined according to the current pricing information and the promotion activity information.

8. A plan generation device, characterized in that, It comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the plan generation method described in any one of claims 1 to 7.

9. A server, characterized in that: It comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the plan generation method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating a plan according to any one of claims 1 to 7 is implemented.

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