Policy configuration method and device based on multi-platform data

Abstract

The invention discloses a strategy configuration method and device based on multi-platform data, relates to the technical field of data processing, and mainly aims to verify whether a marketing strategy is feasible or not and configure a more accurate marketing strategy for multiple marketing platforms. According to the main technical scheme, the method comprises the steps of obtaining historical marketing data of a plurality of marketing platforms according to a preset rule, and generating a historical marketing data table; training a preset model by using the historical marketing data table to obtain a marketing strategy verification model; receiving a target marketing strategy and environmental parameters; processing the target marketing means and the environmental parameters by using a marketing strategy verification model to obtain a marketing predicted value; and if the marketing predicted value is matched with the marketing expected value, configuring a target marketing strategy for the plurality of marketing platforms. The method and the device are used for configuring marketing strategies for a plurality of marketing platforms.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a policy configuration method and device based on multi-platform data. Background Art

[0002] In order to more clearly define and achieve business goals, companies usually have decision makers develop marketing management strategies that are more suitable for the company's current situation based on current market trends and corporate resources.

[0003] At present, due to the different data formats and management strategies of various sales platforms, the method of formulating unified marketing strategies for multiple platforms is mostly based on the historical sales data of each sales platform. Multiple sales platforms organize their historical sales data and generate corresponding data reports, and then aggregate the multiple data reports generated by multiple sales platforms through offline collation, so that decision makers can further formulate marketing strategies for multiple sales platforms based on the aggregated data.

[0004] However, the marketing strategy plan formulated in this way relies too much on human experience and judgment, is relatively subjective, lacks objective verification of whether the marketing strategy is feasible, and cannot determine the accuracy of the marketing strategy, which leads to unstable accuracy when configuring strategies for multiple platforms based on the marketing strategy. Summary of the invention

[0005] In view of the above problems, the present invention proposes a strategy configuration method and device based on multi-platform data, the main purpose of which is to verify whether the marketing strategy is feasible and configure more accurate marketing strategies to multiple marketing platforms.

[0006] In order to achieve the above object, the present invention mainly provides the following technical solutions:

[0007] In a first aspect, the present invention provides a policy configuration method based on multi-platform data, the method comprising:

[0008] Acquire historical marketing data of multiple marketing platforms according to preset rules, and generate a historical marketing data table, wherein the historical marketing data table at least includes historical marketing means and corresponding data trend values;

[0009] Using the historical marketing data table to train a preset model to obtain a marketing strategy verification model;

[0010] Receiving a target marketing strategy and environmental parameters, wherein the target marketing strategy includes target marketing means and marketing expectations;

[0011] Using the marketing strategy verification model to process the target marketing means and environmental parameters to obtain a marketing prediction value;

[0012] If the marketing prediction value matches the marketing expectation value, the target marketing strategy is configured to the multiple marketing platforms.

[0013] In a second aspect, the present invention provides a policy configuration device based on multi-platform data, the device comprising:

[0014] An acquisition unit, configured to acquire historical marketing data of multiple marketing platforms according to preset rules, and generate a historical marketing data table, wherein the historical marketing data table at least includes historical marketing means and corresponding data trend values;

[0015] A training unit, used to train a preset model using the historical marketing data table generated by the acquisition unit to obtain a marketing strategy verification model;

[0016] A receiving unit, configured to receive a target marketing strategy and environmental parameters, wherein the target marketing strategy includes target marketing means and marketing expectations;

[0017] A processing unit, configured to process the target marketing means and environmental parameters received by the receiving unit using the marketing strategy verification model to obtain a marketing prediction value;

[0018] A configuration unit is configured to configure the target marketing strategy to the multiple marketing platforms if the marketing prediction value obtained by the processing unit matches the marketing expectation value.

[0019] On the other hand, the present invention also provides a computing device, comprising: at least one processor, and a memory, wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor, so that the processor can execute the policy configuration method based on multi-platform data as described in the first aspect.

[0020] On the other hand, the present invention also provides a readable storage medium, which is used to store a computer program, wherein when the computer program is running, it controls the device where the storage medium is located to execute the policy configuration method based on multi-platform data as described in the first aspect.

[0021] Through the above technical scheme, the present invention provides a strategy configuration method and device based on multi-platform data, which can clearly reflect the correlation between the required data by generating a marketing data table through historical marketing data obtained from multiple marketing platforms; train a preset model according to the historical marketing data table so that the obtained marketing strategy verification model can fully reflect the different marketing means and the different marketing goals corresponding to their data trend values; when the marketing strategy verification model training is completed, the target marketing strategy to be verified and the corresponding environmental parameters can be received, wherein the environmental parameters can fully reflect the data change trend caused by external factors, and the marketing strategy verification model processes the environmental parameters and The target marketing means in the target marketing strategy can enable the marketing strategy verification model to compare and analyze the target marketing means and environmental parameters according to the prediction rules obtained through its training, and obtain a predicted marketing target, namely, predicted marketing data; the predicted marketing data is matched with the marketing expectation value in the target marketing strategy. If the two match, it means that the target marketing strategy fits the historical data trend and is feasible. The target marketing strategy is configured to multiple marketing platforms to achieve more accurate configuration of marketing strategies for multiple marketing platforms. After the model verifies that the target marketing strategy has passed, the system can directly configure the marketing strategy to each platform, which saves more time and improves the configuration efficiency of the marketing strategy.

[0022] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 A flow chart of a policy configuration method based on multi-platform data proposed in an embodiment of the present invention is schematically shown;

[0025] Figure 2 A flowchart of another policy configuration method based on multi-platform data proposed in an embodiment of the present invention is schematically shown;

[0026] Figure 3 The structure diagram of a policy configuration device based on multi-platform data proposed in an embodiment of the present invention is schematically shown;

[0027] Figure 4A schematic diagram of the structure of another policy configuration device based on multi-platform data proposed in an embodiment of the present invention is shown schematically. DETAILED DESCRIPTION

[0028] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of 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 limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0029] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present invention should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0030] At present, when an enterprise wants to formulate a unified marketing strategy for multiple marketing platforms under it, it usually generates corresponding data reports based on the historical data in each marketing platform's database, and then the staff will summarize the data reports of multiple marketing platforms to form a complete data report that can reflect the data of multiple platforms. According to the complete data report, a marketing strategy that integrates the product information of multiple platforms is manually determined, and it is issued to each marketing platform so that it can carry out product marketing according to this marketing strategy. Based on the current marketing strategy formulation method, the inventor of this case considers that the marketing strategy designed by relying on human experience and judgment is too subjective, and it will be found that there is a deviation from the expected marketing goal during the implementation process, so that the marketing strategy is inaccurate. Therefore, it is necessary to have a method that can verify the formulated marketing strategy, and historical data is the data that can best reflect the actual sales situation. Then, it is thought that a model that can verify the marketing strategy can be generated based on historical data to judge whether the marketing strategy formulated by humans is feasible. Taking into account that the feasibility of formulated marketing strategies is currently judged manually, when configuring marketing strategies for multiple marketing platforms, the staff also configures them for the marketing platforms one by one offline. If the marketing strategy is verified online based on the marketing strategy verification model, the system will directly configure the marketing strategy to each marketing platform, which greatly improves the configuration efficiency compared to manual configuration.

[0031] To this end, the inventor of this case proposed a strategy configuration method based on multi-platform data, which can train a preset model based on historical marketing data to obtain a marketing strategy verification model, verify the target marketing strategy through the marketing strategy verification model, and configure the target marketing strategy that matches the predicted marketing data to multiple marketing platforms, so as to efficiently configure accurate marketing strategies for multiple marketing platforms. The strategy configuration method based on multi-platform data in an embodiment of the present invention has the following specific steps: Figure 1 As shown, including:

[0032] 101. Obtain historical marketing data from multiple marketing platforms according to preset rules and generate a historical marketing data table.

[0033] In this embodiment, it should be noted that the marketing strategy designed based on multiple marketing platforms does not include a unique marketing goal. It can be the profit of direct sales or distribution, or sales volume, etc. The specific goal is determined by the person who formulates the marketing strategy and is not limited in this solution. Due to different marketing goals, the historical data of multiple marketing platforms required may also be different, and the databases in multiple platforms may contain data of the platform that is not related to the product. Therefore, when obtaining the historical marketing data of multiple marketing platforms, some useless data needs to be filtered out according to preset rules. The preset rules here can be to obtain all the historical data of multiple marketing platforms, and then retain the historical data related to the marketing goal as historical marketing data based on correlation analysis; or it can be to first obtain the data name of each platform, determine the relevant data name through correlation analysis, and directly obtain the historical data of the relevant data name from multiple platforms as historical marketing data.

[0034] The multiple marketing platforms in this solution may include but are not limited to one or more sales platforms, logistics platforms, trading platforms, etc., and there are data about the same order in multiple platforms; or multiple sales platforms, each of which stores complete information about the same order. After obtaining historical marketing data, the increase and decrease trend of the data can be analyzed based on continuous data, and the corresponding historical marketing means can be obtained based on the increase and decrease of all data. Among them, the historical marketing means and the data trend value correspond to each other. The historical marketing means may include but are not limited to means such as increasing the output of a certain product or reducing the unit price in a certain month. The corresponding data trend value is the increase and decrease trend of the marketing data of a certain month compared with the marketing data of the previous month by the historical marketing means. The generation unit of the historical marketing means and the corresponding data trend value is not limited to monthly, but is specifically determined according to the deadline of the marketing goal. If the staff wants to formulate a marketing strategy based on quarters, the historical marketing means and data trend values ​​should also be generated in quarters. Historical marketing means can also be analyzed in units of multiple marketing platforms, such as the sales volume and unit price of product 1 on platform A and the sales volume and unit price of product 2 on platform B. How to generate specific historical marketing means is determined according to the actual situation of the marketing platform.

[0035] The analyzed historical marketing means and the corresponding data trend values ​​are saved together to generate a historical marketing data table. The historical marketing data table includes at least the historical marketing means and the corresponding data trend values, and may also include the historical marketing target values ​​corresponding to each historical marketing means.

[0036] 102. Use the historical marketing data table to train the preset model and obtain the marketing strategy verification model.

[0037] In this step, the staff needs to first select a preset model, which can be a machine learning model or a deep learning model, which is not limited here. After obtaining the historical marketing means and the corresponding data trend values ​​based on the historical marketing data, the historical marketing means and the corresponding data trend values ​​can be used as input to train the preset model. Based on each historical marketing means and the corresponding data trend values, the preset model can obtain a complete data change trend during the training process, and generate a correlation relationship between the data involved in the marketing means that best fits the historical sales situation, such as how to allocate the weight ratios between different product data to obtain the target profit, and the allocation method must be in line with the actual situation rather than the ideal state, and finally obtain a marketing strategy verification model.

[0038] When training a preset model, you can also obtain historical environmental parameters corresponding to historical marketing methods based on network data information and use them for model training, so that the marketing strategy model can analyze the data trends corresponding to the historical environmental parameters.

[0039] 103. Receive target marketing strategies and environmental parameters.

[0040] After obtaining the marketing strategy verification model, the marketing strategy verification model can be used to verify the feasibility of the artificially formulated marketing strategy. Before verification, it is necessary to first receive the target marketing strategy to be verified. The target marketing strategy includes the target marketing means and the marketing expectation value, which is the marketing goal to be achieved. While receiving the target marketing strategy, the corresponding environmental parameters should also be received. The purpose is that due to the constant changes in the market and external factors such as seasons, the data trend values ​​corresponding to the historical marketing means are also affected by these external factors. In other words, when training the marketing strategy verification model, the final generated model will also reflect the results of the historical marketing data being marketed by external factors. Therefore, when verifying the target marketing strategy, adding environmental parameters will make the verification results more accurate. Among them, environmental parameters can be seasons, product popularity in the market, etc.

[0041] 104. Use the marketing strategy verification model to process the target marketing means and environmental parameters to obtain marketing prediction values.

[0042] According to the actual historical rules among the data involved in the historical marketing means obtained by the marketing strategy verification model, and according to the historical marketing target values ​​corresponding to the historical marketing means, the marketing target values ​​of the target marketing means of the existing plan can be predicted to obtain the marketing prediction values.

[0043] 105. If the marketing forecast value matches the marketing expectation value, the target marketing strategy is configured to multiple marketing platforms.

[0044] The marketing forecast value generated by the marketing strategy verification model is compared with the marketing expectation value to determine whether the two match, so as to determine whether the strategies of multiple marketing platforms can be configured according to the target marketing strategy. When judging whether the marketing forecast value matches the marketing expectation value, a numerical threshold can be preset to determine whether the difference between the two exceeds the preset numerical threshold. If not, the two match; or a ratio threshold can be preset to divide the difference between the two by the marketing forecast value to determine whether the ratio of the marketing expectation value higher or lower than the marketing forecast value exceeds the preset ratio threshold. If not, the two match.

[0045] It should be noted that when obtaining historical marketing data from multiple marketing platforms, the source of each data item is also saved, so that the marketing platform corresponding to each parameter item in the marketing strategy verification model is known. After using the marketing strategy verification model to determine that the marketing prediction value matches the marketing expectation value, the strategy plan corresponding to each marketing platform in the target marketing strategy can be directly sent to the corresponding marketing platform based on the content in the target marketing strategy and its correspondence with multiple marketing platforms.

[0046] Based on the above Figure 1 It can be seen from the implementation method that the embodiment of the present invention can obtain historical marketing data from multiple marketing platforms, analyze historical marketing means and corresponding data trend values, and provide data that is more in line with actual sales trends for training marketing strategy verification models, so that the trained marketing strategy verification model can better predict a marketing target value that is in line with actual sales trends. The received target marketing means and environmental parameters are input into the marketing strategy verification model, which can not only compare the target marketing means with the marketing rules in the marketing strategy verification model to obtain a marketing prediction value that conforms to the target marketing means, but also adjust the degree of influence of the target marketing means on the marketing prediction value according to the environmental parameters, so that the obtained marketing prediction value is more accurate. When the marketing expectation value conforms to the marketing prediction value, it proves that the target marketing strategy has been verified and is feasible. The system directly configures the target marketing strategy to each marketing platform, avoiding the complicated work of the staff configuring each platform one by one, and greatly improving the configuration efficiency.

[0047] Furthermore, based on the above Figure 1 The embodiment of the present invention shown in FIG. 1 is a detailed description of using historical marketing data to train a marketing strategy verification model, using the marketing strategy verification model to verify a target marketing strategy, and configuring the target marketing strategy. The specific steps are as follows: Figure 2 As shown, including:

[0048] 201. Obtain historical marketing data of multiple marketing platforms according to preset rules and generate a historical marketing data table.

[0049] In this step, historical marketing data of multiple marketing platforms is obtained by first obtaining all historical data from multiple marketing platforms. In order to analyze the required data in all historical data, marketing targets need to be obtained, that is, the first historical data and marketing targets of multiple marketing platforms are obtained.

[0050] Since there are multiple marketing platforms that are independently managed by their respective subsidiaries, there is isolated data storage between the multiple marketing platforms. The multiple marketing platforms have their own data structures, access rules and management systems, and lack effective integration and interaction mechanisms. This results in data islands among the multiple marketing platforms, and the inability to exchange information. If you want to uniformly manage multiple marketing platforms and uniformly configure marketing strategies for them, you need to aggregate the data information of multiple marketing platforms. The data lake can store a large amount of raw data that is not specifically formatted and structured. Therefore, in this solution, the preset data lake is used to introduce the first historical data from multiple marketing platforms into the preset data lake and store it. When the historical data of multiple marketing platforms is updated, when the historical marketing data is obtained again, only the required data that has not been obtained can be obtained from multiple marketing platforms, and the first historical data that has been obtained will be saved in the data lake and can be used to update the marketing strategy verification model at any time.

[0051] It should be noted that in order to ensure the accuracy of the stored original data, all historical data can be cleaned and preprocessed before entering the data lake, including data format conversion, supplementation, and deduplication.

[0052] After the marketing target and the first historical data are known, the first historical data is analyzed based on the marketing target to obtain historical marketing data and generate a historical marketing data table. Specifically, the first historical data is analyzed for correlation based on the marketing target to obtain relevant marketing features. The first marketing target data can be determined based on the first historical data, and the correlation and degree of correlation between the data in the first historical data and the first marketing target data can be analyzed. The relevant data features are extracted as relevant marketing features. The first marketing target data here is the basic data of the marketing target. If the marketing target is monthly profit, the first marketing target data is all profit data. When the data source collects data on a daily basis, all profit data is daily profit. The relevant marketing features can be the logistics company, unit price, sales volume, etc. of the product. Each relevant marketing feature has a large amount of corresponding data.

[0053] Since the unit of the marketing target can be daily, monthly or quarterly, the historical marketing data used to generate the historical marketing means should correspond to the unit of the marketing target. Therefore, after determining the relevant marketing features related to the marketing target, the first historical data is calculated according to the marketing target to obtain the historical marketing data corresponding to the relevant marketing features. If the marketing target is monthly profit, the relevant marketing feature data in the first historical data is summarized in units of months to generate the historical marketing data.

[0054] Analyze historical marketing data, determine historical marketing methods and corresponding data trend values, and generate historical marketing data tables.

[0055] 202. Use the historical marketing data table to train the preset model and obtain the marketing strategy verification model.

[0056] When training a preset model, it is necessary to clarify the input and output of the model. In this solution, the historical marketing means and data trend values ​​are used as input, and the historical marketing target values ​​are used as output to train the preset model and obtain a marketing strategy verification model. Among them, the input items can also include environmental parameters.

[0057] Regarding the historical marketing target value, the historical marketing target value can be directly calculated based on the marketing target and the first historical data, or it can be calculated based on the historical marketing data and then calculated to obtain the historical marketing target value based on the historical marketing data. In this step, the calculation is based on the historical marketing data to obtain the historical marketing target value corresponding to the historical marketing means.

[0058] 203. Receive target marketing strategies and environmental parameters.

[0059] The content in this step is consistent with that in step 103, so it will not be described in detail.

[0060] 204. Use the marketing strategy verification model to process the target marketing means and environmental parameters to obtain marketing prediction values.

[0061] The content in this step is consistent with that in step 104, so it will not be described in detail.

[0062] 205. If the marketing forecast value matches the marketing expectation value, the target marketing strategy is configured to multiple marketing platforms.

[0063] The content in this step is consistent with that in step 105, so it will not be described in detail.

[0064] 206. If the marketing prediction value does not match the marketing expectation value, the prediction marketing strategy corresponding to the marketing expectation value is determined according to the marketing strategy verification model, and the prediction marketing strategy is configured to multiple marketing platforms.

[0065] Since the marketing target value is affected not only by marketing means but also by environmental factors, the sales trends of multiple marketing platforms can be determined according to environmental parameters, the adjustment priorities of multiple marketing platforms can be determined based on the sales trends, the target marketing strategy can be adjusted according to the adjustment priorities, and a predictive marketing strategy can be generated.

[0066] Specifically, determining the sales trend can be to determine multiple historical marketing methods and corresponding data trend values ​​related to environmental parameters based on preset conditions. The marketing strategy verification model searches for multiple historical environmental parameters with high similarity in the historical marketing data according to the received environmental parameters, and determines the corresponding historical marketing methods and corresponding data trend values. Among them, a similarity threshold can be preset, such as the number of identical environmental parameters or the range of the degree of influence of each environmental parameter on the marketing method. Combined with the analysis of multiple data trend values, the sales trends of multiple marketing platforms are generated. The multiple data trend values ​​are averaged according to the weights, and the weights of the environmental parameters with higher similarity are greater, so as to obtain a data trend value of the data involved in the final marketing method, that is, the sales trend of multiple marketing platforms. The adjustment priority of multiple marketing platforms is determined according to the degree of influence of the sales trends of multiple marketing platforms on the marketing goals. For example, when the sales trend of product A on marketing platform H is to reduce the unit price by 5%, which has the greatest impact on the increase in overall sales, the unit price of product A on marketing platform H is lowered first.

[0067] Based on the above Figure 2 It can be seen from the implementation method that the embodiment of the present invention calculates the historical marketing target value and the historical marketing data through the first historical data, and trains the marketing strategy verification model based on this, so as to obtain a marketing strategy verification model that is more in line with the actual historical trend. When the marketing prediction value obtained by using the marketing strategy verification model does not match the marketing expectation value, it means that the target marketing means cannot achieve the marketing expectation value. The marketing strategy verification model is used again to analyze the sales trend suitable for the current market environment, and the target marketing strategy is adjusted based on the sales trend to obtain a predicted marketing strategy that meets the marketing expectation value, and the predicted marketing strategy is configured to multiple marketing platforms. The correction of the target marketing strategy is achieved, and a more accurate and more realistic predicted marketing strategy is obtained, and the predicted marketing strategy can be directly configured to multiple marketing platforms, which greatly improves the configuration efficiency.

[0068] Furthermore, as a response to the above Figure 1 , Figure 2The implementation of the embodiment shown in the method, the embodiment of the present invention provides a strategy configuration device based on multi-platform data, the device is used to verify the target marketing strategy using the marketing strategy verification model, and improve the efficiency of strategy configuration. The embodiment of the device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 3 As shown, the device comprises:

[0069] An acquisition unit 31 is used to acquire historical marketing data of multiple marketing platforms according to preset rules and generate a historical marketing data table, wherein the historical marketing data table at least includes historical marketing means and corresponding data trend values;

[0070] A training unit 32, configured to train a preset model using the historical marketing data table generated by the acquisition unit 31 to obtain a marketing strategy verification model;

[0071] A receiving unit 33 is used to receive a target marketing strategy and environmental parameters, wherein the target marketing strategy includes a target marketing means and a marketing expectation;

[0072] The processing unit 34 is used to process the target marketing means and environmental parameters received by the receiving unit 33 using the marketing strategy verification model to obtain a marketing prediction value;

[0073] The configuration unit 35 is configured to configure target marketing strategies to multiple marketing platforms if the marketing prediction value obtained by the processing unit 34 matches the marketing expectation value.

[0074] Further, such as Figure 4 As shown, the acquisition unit 31 includes:

[0075] An acquisition module 311 is used to acquire first historical data and marketing targets of multiple marketing platforms and introduce them into a preset data lake;

[0076] The analysis module 312 is used to perform data analysis on the first historical data acquired by the acquisition module 311 based on the marketing target acquired by the acquisition module 311, obtain historical marketing data and generate a historical marketing data table.

[0077] Further, such as Figure 4 As shown, the analysis module 312 includes:

[0078] An analysis submodule 3121 is used to perform a correlation analysis on the first historical data based on the marketing goal to obtain relevant marketing features;

[0079] A calculation submodule 3122, configured to calculate the first historical data according to the marketing target, and obtain the historical marketing data corresponding to the relevant marketing features obtained by the analysis submodule 3121;

[0080] The determination submodule 3123 is used to analyze the historical marketing data obtained by the calculation submodule 3122, determine the historical marketing means and the corresponding data trend values, and generate a historical marketing data table.

[0081] Further, such as Figure 4 As shown, the training unit 32 includes:

[0082] A calculation module 321 is used to perform calculations based on historical marketing data to obtain historical marketing target values ​​corresponding to historical marketing means;

[0083] The training module 322 is used to take the historical marketing means and data trend values ​​as input and the historical marketing target values ​​obtained by the calculation module 321 as output, train the preset model, and obtain the marketing strategy verification model.

[0084] Further, such as Figure 4 As shown, the device also includes:

[0085] The prediction unit 36 ​​determines the predicted marketing strategy corresponding to the marketing expected value according to the marketing strategy verification model if the marketing predicted value does not match the marketing expected value, wherein the predicted marketing strategy includes predicted marketing means and marketing expected value;

[0086] The configuration unit 35 is further used to configure the predicted marketing strategy obtained by the prediction unit 36 ​​to multiple marketing platforms.

[0087] Further, such as Figure 4 As shown, the prediction unit 36 ​​includes:

[0088] A first determination module 361, for determining sales trends of multiple marketing platforms according to environmental parameters;

[0089] A second determination module 362, configured to determine adjustment priorities of multiple marketing platforms based on the sales trend determined by the first determination module 361;

[0090] The adjustment module 363 is used to adjust the target marketing strategy according to the adjustment priority determined by the second determination module 362 and generate a predicted marketing strategy.

[0091] Further, such as Figure 4 As shown, the first determining module 361 includes:

[0092] A determination submodule 3611 is used to determine a plurality of historical marketing means and corresponding data trend values ​​related to environmental parameters based on preset conditions;

[0093] The analysis submodule 3612 is used to generate sales trends of multiple marketing platforms by combining the multiple data trend values ​​determined by the analysis determination submodule 3611.

[0094] Furthermore, an embodiment of the present invention further provides a computing device, the computing device comprising: at least one processor, and a memory, wherein the memory stores instructions executable by the processor, the instructions are executed by the processor, so that the processor can perform the above Figure 1-2 The strategy configuration method based on multi-platform data described in.

[0095] Furthermore, an embodiment of the present invention further provides a readable storage medium, wherein the readable storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-2 The strategy configuration method based on multi-platform data described in.

[0096] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0100] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0106] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0107] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0108] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0109] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0110] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A strategy configuration method based on multi-platform data, characterized in that: The method comprises: Acquire historical marketing data of multiple marketing platforms according to preset rules, and generate a historical marketing data table, wherein the historical marketing data table at least includes historical marketing means and corresponding data trend values; Using the historical marketing data table to train a preset model to obtain a marketing strategy verification model; Receiving a target marketing strategy and environmental parameters, wherein the target marketing strategy includes target marketing means and marketing expectations; Using the marketing strategy verification model to process the target marketing means and environmental parameters to obtain a marketing prediction value; If the marketing prediction value matches the marketing expectation value, the target marketing strategy is configured to the multiple marketing platforms.

2. The method according to claim 1, characterized in that The method of obtaining historical marketing data of multiple marketing platforms according to preset rules and generating a historical marketing data table includes: Acquire the first historical data and marketing targets of the multiple marketing platforms and introduce them into a preset data lake; The first historical data is analyzed based on the marketing goal to obtain the historical marketing data and generate the historical marketing data table.

3. The method according to claim 2, characterized in that Performing data analysis on the first historical data based on the marketing goal, obtaining the historical marketing data and generating the historical marketing data table, includes: Performing a correlation analysis on the first historical data based on the marketing goal to obtain relevant marketing features; Calculate the first historical data according to the marketing target to obtain the historical marketing data corresponding to the relevant marketing features; The historical marketing data is analyzed to determine the historical marketing means and corresponding data trend values, and the historical marketing data table is generated.

4. The method according to claim 3, characterized in that The preset model is trained using the historical marketing data table to obtain a marketing strategy verification model, including: Calculating based on the historical marketing data to obtain a historical marketing target value corresponding to the historical marketing means; The historical marketing means and the data trend value are used as input, and the historical marketing target value is used as output, to train the preset model and obtain the marketing strategy verification model.

5. The method according to claim 1, characterized in that The method further comprises: If the marketing prediction value does not match the marketing expected value, determining the predicted marketing strategy corresponding to the marketing expected value according to the marketing strategy verification model, wherein the predicted marketing strategy includes predicted marketing means and the marketing expected value; The predictive marketing strategy is deployed to the plurality of marketing platforms.

6. The method according to claim 5, characterized in that Determining the predicted marketing strategy corresponding to the marketing expectation value according to the marketing strategy verification model includes: determining sales trends of the plurality of marketing platforms according to the environmental parameters; Determining adjustment priorities of the plurality of marketing platforms based on the sales trend; The target marketing strategy is adjusted according to the adjustment priority, and the predicted marketing strategy is generated.

7. The method according to claim 6, characterized in that Determining sales trends of the multiple marketing platforms according to the environmental parameters includes: Determine multiple historical marketing methods and corresponding data trend values ​​related to the environmental parameters based on preset conditions; The multiple data trend values ​​are combined and analyzed to generate sales trends of the multiple marketing platforms.

8. A policy configuration device based on multi-platform data, characterized in that: The device comprises: An acquisition unit, configured to acquire historical marketing data of multiple marketing platforms according to preset rules, and generate a historical marketing data table, wherein the historical marketing data table at least includes historical marketing means and corresponding data trend values; A training unit, used to train a preset model using the historical marketing data table generated by the acquisition unit to obtain a marketing strategy verification model; A receiving unit, configured to receive a target marketing strategy and environmental parameters, wherein the target marketing strategy includes target marketing means and marketing expectations; A processing unit, which processes the target marketing means and environmental parameters received by the receiving unit using the marketing strategy verification model to obtain a marketing prediction value; A configuration unit is configured to configure the target marketing strategy to the multiple marketing platforms if the marketing prediction value obtained by the processing unit matches the marketing expectation value.

9. A computing device, characterized in that The computing device includes: at least one processor, and a memory, wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor, so that the processor can execute the policy configuration method based on multi-platform data as described in any one of claims 1-7.

10. A readable storage medium, characterized in that: The readable storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the policy configuration method based on multi-platform data according to any one of claims 1 to 7.

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