AI marketing method and system based on data driving

Abstract

The invention provides an AI marketing method and system based on data driving. Comprising the following steps: receiving attribute information of a to-be-marketed target, and obtaining a plurality of efficient flow casting cases according to the attribute information; extracting flow throwing operation information from each efficient flow throwing case, wherein the flow throwing operation information comprises a plurality of flow throwing targets, and the flow throwing sequence and flow throwing intensity of each flow throwing target; other flow casting information of each flow casting target in the recent period is obtained, the flow casting operation information is optimized and adjusted according to the other flow casting information, and target flow casting operation information is obtained; and carrying out marketing operation on the to-be-marketing target according to the target flow investment operation information. According to the method, the efficient flow casting case is obtained by utilizing the attribute information of the to-be-marketing target, successful experience can be quickly referred to, and effective reference is provided for marketing; meanwhile, the success experience is adaptively adjusted on the basis of the recent flow casting actual condition of each network platform to be subjected to flow casting, so that the probability of obtaining a better flow casting effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of marketing management, and in particular, to a data-driven AI marketing method and system. Background Art

[0002] With the prosperity of the Internet ecosystem, the online behavior paths of consumers have become increasingly complex and diverse, and traffic investment has become an important marketing means. However, traditional traffic investment models have exposed many problems when dealing with complex consumer behaviors and precise placement requirements. Traditional traffic investment is mostly based on extensive audience targeting methods, such as advertising placement based on basic information such as age, gender, and region. This method cannot accurately target potential customers with real needs, and a large amount of advertising budget is wasted on ineffective exposure.

[0003] Therefore, how to improve marketing efficiency is a technical problem that needs to be solved urgently at present. Summary of the Invention

[0004] To solve the technical problems existing in the above background art, the present invention provides a data-driven AI marketing method, system, electronic device, computer storage medium, and computer program product.

[0005] The present invention provides a data-driven AI marketing method, which is applied to a marketing agent, and the method includes the following steps: Receiving the attribute information of the target to be marketed, and obtaining a number of efficient traffic investment cases according to the attribute information; Extracting traffic investment operation information from each of the efficient traffic investment cases, where the traffic investment operation information includes a number of traffic investment targets, and the traffic investment order and intensity of each traffic investment target; wherein, the traffic investment target refers to a network platform; Obtaining other traffic investment information of each of the traffic investment targets in a recent period, and optimizing and adjusting the traffic investment operation information according to the other traffic investment information to obtain target traffic investment operation information; Performing a marketing operation on the target to be marketed according to the target traffic investment operation information.

[0006] Optionally, the extracting traffic investment operation information from each of the efficient traffic investment cases includes: Identifying each network platform in each of the efficient traffic investment cases, and performing time clustering on the traffic investment data of each network platform; Determining the first traffic investment order for each network platform according to the earliest time of the time clustering, and evaluating the first traffic investment intensity of each network platform based on the traffic investment data; Taking each network platform in the efficient traffic investment case with the best traffic investment effect as the traffic investment target, and taking its corresponding first traffic investment order as the traffic investment order; Comprehensively analyze the first investment promotion intensity of the same investment promotion target in each of the efficient investment promotion cases to obtain an investment promotion intensity adjustment value, and finely adjust the first investment promotion intensity based on the investment promotion intensity adjustment value to obtain a second investment promotion intensity; Use the second investment promotion intensity as the investment promotion intensity of the investment promotion target.

[0007] Optionally, the comprehensively analyzing the first investment promotion intensity of the same investment promotion target in each of the efficient investment promotion cases to obtain an investment promotion intensity adjustment value includes: Determine the deviation quantity of the type of network platform included in each of the efficient investment promotion cases from the type of network platform included in the efficient investment promotion case with the optimal investment promotion effect, and match the corresponding weighting coefficient according to the deviation quantity; Use the weighting coefficient to perform weighted summation on the first investment promotion intensity of the same investment promotion target in each of the efficient investment promotion cases, calculate the average value of the weighted summation result, and use this average value as the investment promotion intensity adjustment value.

[0008] Optionally, the obtaining other investment promotion information of each of the investment promotion targets in the recent period and optimizing and adjusting the investment promotion operation information according to the other investment promotion information to obtain target investment promotion operation information includes: Obtain the other investment promotion information having the same or similar attributes as the target to be marketed in the recent period, evaluate the second investment promotion intensity of each of the other investment promotion information, and calculate the variance of each of the second investment promotion intensities; Determine an investment promotion intensity adjustment coefficient according to the variance, multiply the investment promotion intensity adjustment coefficient by the corresponding investment promotion intensity to obtain a target investment promotion intensity, and replace the investment promotion intensity in the investment promotion operation information with the target investment promotion intensity to obtain the target investment promotion operation information.

[0009] Optionally, before performing the marketing operation on the target to be marketed according to the target investment promotion operation information, the method further includes: Output the target investment promotion operation information and the investment promotion operation information to a marketer at the same time, and after receiving the confirmation information from the marketer, determine to execute the target investment promotion operation information; Or, after receiving the modification information from the marketer, execute the modified target investment promotion operation information corresponding to the modification information.

[0010] The present invention also provides a data-driven AI marketing system applied to a marketing agent, including a processor and a memory, and the processor calls and executes a computer program in the memory to implement the following steps: Receive the attribute information of the target to be marketed, and obtain a number of efficient investment promotion cases according to the attribute information; Extract the investment operation information from each of the efficient investment cases. The investment operation information includes a number of investment targets, as well as the investment order and investment intensity of each investment target; wherein, the investment target refers to a network platform. Obtain other investment information of each of the investment targets in the recent period, and optimize and adjust the investment operation information according to the other investment information to obtain the target investment operation information. Conduct marketing operations on the target to be marketed according to the target investment operation information.

[0011] The present invention also provides an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory and executes the method described in any one of the preceding items.

[0012] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the method described in any one of the preceding items.

[0013] The present invention also provides a computer program product, including a computer program stored on a non-transitory computer-readable medium, and the computer program is executed by a processor to perform the method described in any one of the preceding items.

[0014] The beneficial effects of the present invention are as follows: By using the attribute information of the target to be marketed to obtain efficient investment cases, the present invention can quickly draw on successful experiences and provide effective references for marketing; at the same time, the present invention also analyzes the recent investment situation of each network platform to be invested, so as to make adaptive adjustments to the successful experiences, which can increase the probability of obtaining better investment effects, and thus can significantly improve the accuracy and effectiveness of investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of an AI marketing method based on data driving disclosed in an embodiment of the present invention.

[0017] Figure 2 It is a schematic diagram of an investment operation disclosed in an embodiment of the present invention.

[0018] Figure 3It is a schematic structural diagram of an AI marketing system based on data driving disclosed in an embodiment of the present invention. Detailed implementation manners

[0019] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0022] In the description of the present invention, it should be noted that if terms such as "upper", "lower", "inner", "outer", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0023] It should be noted that, without conflict, the features in the embodiments of the present invention can be combined with each other.

[0024] As Figure 1 shown, an embodiment of the present invention discloses an AI marketing method based on data driving, which is applied to a marketing agent. The method includes the following steps: S100, receiving attribute information of a target to be marketed, and obtaining a number of highly efficient investment and promotion cases according to the attribute information.

[0025] The attribute information of the target to be marketed covers many aspects. For example, if the target to be marketed is a fashion clothing brand for young women, the attribute information may include the age range of the target audience (such as 18-30 years old), gender (female), brand style (such as simple and casual style), product price range (such as 100-500 yuan), etc. The above attribute information of the target to be marketed can be manually input by marketers.

[0026] Based on these attribute information, filter in the massive investment promotion case database. For example, the database stores investment promotion cases of various industries and different product types. By setting filtering conditions, such as targeting young women, fashion categories, similar price ranges, etc., several highly efficient investment promotion cases that have achieved good marketing effects are obtained from it.

[0027] S200, extract investment promotion operation information from each of the above-mentioned highly efficient investment promotion cases. The investment promotion operation information includes several investment promotion targets, as well as the investment promotion order and intensity of each investment promotion target; among them, the investment promotion target refers to the network platform.

[0028] Conduct further in-depth analysis on the above-mentioned multiple selected highly efficient investment promotion cases, and extract the investment promotion operation information of these cases, including investment promotion targets, as well as the investment promotion order and intensity of each investment promotion target.

[0029] Taking the above-mentioned multiple highly efficient investment promotion cases of fashion clothing brands as an example, the investment promotion targets extracted from the first case are Xiaohongshu, Douyin, and Weibo. In terms of the investment promotion order, first conduct grass-roots promotion on Xiaohongshu, then release creative short videos on Douyin, and finally carry out topic discussions on Weibo. The investment promotion intensity is reflected by the advertising budget invested, the frequency of released content, etc. For example, 40% of the total budget is invested on Xiaohongshu, and 10 grass-roots notes are released every week; 35% of the budget is invested on Douyin, and 5 short videos are released every week; 25% of the budget is invested on Weibo, and 2 topic discussions are carried out every week. Similar investment promotion operation information is also extracted from other cases, including different network platforms as investment promotion targets and the corresponding investment promotion order and intensity arrangements. The investment promotion principle can be referred to Figure 2 as shown.

[0030] Integrate and analyze the above-mentioned multiple groups of investment promotion targets, the investment promotion order and intensity of each investment promotion target, and the final investment promotion operation information is obtained.

[0031] S300, obtain other investment promotion information of each of the above-mentioned investment promotion targets in the recent period, and optimize and adjust the investment promotion operation information according to the other investment promotion information to obtain the target investment promotion operation information; For the above-mentioned multiple determined investment promotion targets, respectively obtain their investment promotion situations in the recent period, such as in the recent month. Analyze the other investment promotion information of each investment promotion target in the recent period to adapt and adjust the previously obtained investment promotion operation information for reference to adapt to the current actual situation, and then obtain the adjusted target investment promotion operation information.

[0032] For example, for the investment promotion target of Xiaohongshu, other investment promotion information in the recent period shows that the advertising investment in the fashion category on Xiaohongshu has increased significantly in the past month. Based on this information, it is necessary to adjust the investment promotion intensity on Xiaohongshu. For example, the budget proportion is reduced from 30% to 40%.

[0033] S400, perform marketing operations on the target to be marketed according to the target investment promotion operation information.

[0034] Based on the obtained target investment promotion operation information above, gradually carry out investment promotion operations on each network platform. For example, first carry out grass-roots promotion on Xiaohongshu according to the adjusted budget and content release frequency, and publish a series of notes highlighting the brand style and suitable for the wearing scenarios of the target audience; then publish short videos of life scenarios on Douyin according to the new budget investment and content direction; finally, on Weibo, organize hot topic discussions related to the brand according to the set budget and topics. By carrying out these marketing operations methodically on each network platform, it is expected to achieve the purpose of attracting the target audience, increasing brand awareness and product sales.

[0035] By using the attribute information of the target to be marketed to obtain efficient investment promotion cases, the present invention can quickly draw on successful experiences and provide effective references for marketing; at the same time, the present invention also analyzes the recent investment promotion situation of each network platform to be invested, so as to make adaptive adjustments to the successful experiences, which can increase the probability of obtaining better investment promotion effects, and thus can significantly improve the accuracy and effectiveness of investment promotion.

[0036] Optionally, extracting the investment promotion operation information from each of the efficient investment promotion cases includes: Identify each network platform in each of the efficient investment promotion cases, and perform time clustering on the investment promotion data of each network platform; Determine the first investment promotion order for each network platform according to the earliest time of time clustering, and evaluate and obtain the first investment promotion intensity of each network platform based on the investment promotion data; Use the network platforms in the efficient investment promotion case with the best investment promotion effect as the investment promotion target, and use the corresponding first investment promotion order as the investment promotion order; Comprehensively analyze the first investment promotion intensity of the same investment promotion target in each of the efficient investment promotion cases to obtain an investment promotion intensity adjustment value, and fine-tune the first investment promotion intensity based on the investment promotion intensity adjustment value to obtain a second investment promotion intensity; Use the second investment promotion intensity as the investment promotion intensity of the investment promotion target.

[0037] In this embodiment, the investment promotion data in the selected efficient investment promotion cases includes the time of advertising placement on each platform, the number of ad exposures, the number of user clicks, the number of user interactions (likes, comments, shares), etc.

[0038] First, the online platforms for investment promotion can be directly obtained from the above investment promotion data, such as Xiaohongshu, Douyin, Weibo, etc. These online platforms are the investment promotion targets selected for the corresponding efficient investment promotion cases.

[0039] Then, by performing time clustering on the investment promotion data of each online platform, the time period of this investment promotion can be determined. The earliest time in this period is used as the start time of investment promotion for this online platform, and the order of investment promotion for each online platform is determined according to the earliest time. For example, through time clustering, it is found that in a certain efficient investment promotion case, the earliest concentrated placement time on Xiaohongshu is at the beginning of the month, on Douyin is in the middle of the month, and on Weibo is at the end of the month. Then, according to this earliest time order, the first investment promotion order is determined as Xiaohongshu, Douyin, Weibo.

[0040] Next, the investment promotion data also includes data such as the proportion of advertising budget, the frequency of published content, the exposure volume and interaction volume brought by each piece of content on average. The above content is evaluated using a preset evaluation method to obtain the corresponding first investment promotion intensity. For example, on Xiaohongshu, by calculating data such as the proportion of advertising budget, the frequency of published content, the exposure volume and interaction volume brought by each piece of content on average during the entire investment promotion period, the first investment promotion intensity is comprehensively evaluated. The first investment promotion intensities of Douyin and Weibo are obtained in the same way. It should be noted that the present invention does not limit the specific calculation formula of the investment promotion intensity, but there is a positive correlation between the above-mentioned related parameters and the investment promotion intensity.

[0041] Then, one case with the best investment promotion effect is determined from each efficient investment promotion case through manual designation or other means. The online platforms involved in this case are used as the investment promotion targets, and the corresponding first investment promotion order is used as the investment promotion order. For the investment promotion intensity, based on the first investment promotion intensity corresponding to each online platform of the case with the best investment promotion effect, and comprehensively analyzing the first investment promotion intensity corresponding to the same investment promotion target of all cases, multiple investment promotion intensity adjustment values are obtained; then, the investment promotion intensity adjustment values are integrated with the first investment promotion intensity of the corresponding optimal case, so that multiple second investment promotion intensities are obtained correspondingly.

[0042] So far, the investment promotion operation information including several investment promotion targets, the investment promotion order and investment promotion intensity of each investment promotion target is obtained. This investment promotion operation information is the reference benchmark for the investment promotion operation of the target to be promoted this time.

[0043] Optionally, the comprehensive analysis of the first investment promotion intensity corresponding to the same investment promotion target in each of the efficient investment promotion cases to obtain the investment promotion intensity adjustment value includes: Determine the deviation quantity between each of the high-efficiency investment promotion cases and the types of network platforms included in the high-efficiency investment promotion case with the optimal investment promotion effect, and match the corresponding weighting coefficient according to the deviation quantity; Use the weighting coefficient to perform weighted summation on the first investment promotion intensity of the same investment promotion target in each of the high-efficiency investment promotion cases, and calculate the average value of the weighted summation result, and use this average value as the investment promotion intensity adjustment value.

[0044] In this embodiment, still based on the high-efficiency investment promotion case with the optimal investment promotion effect, analyze the deviation quantity between other high-efficiency investment promotion cases and the types of network platforms included in this optimal case (this deviation value is the absolute value of the positive deviation and the negative deviation). For example, a certain other high-efficiency investment promotion case lacks network platform A in the optimal case, and the corresponding deviation quantity is 1; a certain other high-efficiency investment promotion case has additionally increased network platforms B and C compared to the optimal case, and the corresponding deviation quantity is 2. The magnitude of this deviation quantity reflects the degree of difference between other high-efficiency investment promotion cases and the optimal case in the selected investment promotion target. Based on this degree of difference, that is, the above deviation value, the corresponding weighting coefficient is matched. This matching is obtained based on a preset association relationship, and the weighting coefficient is negatively correlated with the deviation quantity.

[0045] That is to say, the greater the difference between other high-efficiency investment promotion cases and the optimal case in the selection of the investment promotion target, the worse its referenceability, and the smaller the corresponding weighting coefficient; on the contrary, the better its referenceability, and the larger the corresponding weighting coefficient. The weighting coefficient of the optimal case is 1.

[0046] It should be noted that each weighting coefficient is applicable to all investment promotion targets in each high-efficiency investment promotion case. The average value of the weighted summation result can be: the weighted summation result / N, or the weighted summation result / nN, where N is the number of all high-efficiency investment promotion cases, and n = 2, 3..., and the use of n can set the investment promotion intensity adjustment value smaller to achieve the goal of fine-tuning, and preferably n = 3.

[0047] Optionally, the obtaining other investment promotion information of each of the investment promotion targets in the recent period, and optimizing and adjusting the investment promotion operation information according to the other investment promotion information to obtain target investment promotion operation information includes: Obtain the other investment promotion information that has the same or similar attributes as the target to be marketed in the recent period, evaluate the second investment promotion intensity of each of the other investment promotion information, and calculate the variance of each of the second investment promotion intensities; Determine the investment promotion intensity adjustment coefficient according to the variance, use the investment promotion intensity adjustment coefficient to multiply the corresponding investment promotion intensity to obtain the target investment promotion intensity, and replace the investment promotion intensity in the investment promotion operation information with the target investment promotion intensity to obtain the target investment promotion operation information.

[0048] In this embodiment, the investment promotion operation information based on the reference cases can be obtained through the foregoing method, that is, which network platforms are required for investment promotion, and what are the investment promotion order and intensity on these network platforms. At the same time, the present invention further considers the marketing investment promotion situations of similar products on the selected network platforms in the recent period, and accordingly optimizes and adjusts the investment promotion intensity in the investment promotion operation information. Specifically: Obtain the recent investment promotion information of products with the same or similar attributes as the target to be marketed on this network platform, evaluate the second investment promotion intensity of each other investment promotion information in the same manner as described above, and then calculate the variance between these second investment promotion intensities. This variance represents the gap situation of the investment promotion intensities of the same or similar products on the same network platform. The larger the gap, the relatively smaller the marketing competition intensity of the marketers of these same or similar products on this network platform, and the smaller the gap, the greater the marketing competition intensity of the marketers of these same or similar products on this network platform (especially when the second investment promotion intensities are all very high).

[0049] Based on the corresponding relationship between the above variance and the competition intensity, the present invention sets an investment promotion intensity adjustment coefficient determined according to the variance. There is also a negative correlation between the investment promotion intensity adjustment coefficient and the variance, that is, the larger the variance, the smaller the corresponding investment promotion intensity adjustment coefficient, such as 1.0, 1.1. At this time, there is no need to excessively increase the investment promotion intensity, and the probability of obtaining good marketing effects can basically be ensured; while the smaller the variance, the larger the corresponding investment promotion intensity adjustment coefficient, such as 1.3, 1.5. At this time, it is necessary to increase the investment promotion intensity to increase the probability of obtaining good marketing effects.

[0050] Finally, multiply the obtained investment promotion intensity adjustment coefficient by the corresponding investment promotion intensity to obtain the target investment promotion intensity, and replace the investment promotion intensity in the investment promotion operation information with the target investment promotion intensity, that is, the above optimization adjustment is completed, and the target investment promotion operation information is obtained.

[0051] Optionally, before marketing the target to be marketed according to the target investment promotion operation information, the method further includes: Output the target investment promotion operation information and the investment promotion operation information to the marketing personnel at the same time, and after receiving the confirmation information from the marketing personnel, determine to execute the target investment promotion operation information; Or, after receiving the modification information from the marketing personnel, execute the modified target investment promotion operation information corresponding to the modification information.

[0052] In this embodiment, to ensure the accuracy of the marketing effect, before performing marketing operations on the target to be marketed according to the target investment operation information, it is also necessary to output the determined target investment operation information to the marketing personnel for confirmation, and at the same time, output the investment operation information obtained based on typical cases to the marketing personnel for their comparison and analysis.

[0053] If the marketing personnel recognize the target investment operation information, they directly feedback the confirmation information, and the target investment operation information will be executed subsequently. If the marketing personnel recognize the target investment operation information, they will modify the target investment operation information. For example, adjust the investment intensity for each investment target, and then feedback the adjusted target investment operation information, and the adjusted target investment operation information will be executed subsequently.

[0054] As Figure 3 shown, an embodiment of the present invention also discloses a data-driven AI marketing system applied to a marketing agent, including a processor and a memory. The processor calls and executes the computer program in the memory to implement the following steps: Receive the attribute information of the target to be marketed, and obtain a number of efficient investment cases according to the attribute information; Extract the investment operation information from each of the efficient investment cases. The investment operation information includes a number of investment targets, as well as the investment order and investment intensity of each investment target; wherein, the investment target refers to a network platform; Obtain other investment information of each of the investment targets in the recent period, and optimize and adjust the investment operation information according to the other investment information to obtain the target investment operation information; Perform marketing operations on the target to be marketed according to the target investment operation information.

[0055] For the technical principle and technical effect of the system of the present invention, it is the same as the foregoing method, and will not be elaborated here.

[0056] An embodiment of the present invention also discloses an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory and executes the method as described in the foregoing embodiment.

[0057] An embodiment of the present invention also discloses a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the method as described in the foregoing embodiment.

[0058] An embodiment of the present invention also discloses a computer program product, including a computer program stored on a non-transitory computer-readable medium, and the computer program is executed by a processor to perform the method as described in any one of the foregoing.

[0059] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0060] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0061] In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0062] It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and no limitation is made herein.

[0063] The above specific embodiments do not constitute a limitation on the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A data-driven AI marketing method, applied to a marketing agent, characterized in that: The method comprises the following steps: Receive attribute information of a target to be marketed, and obtain several high-efficiency traffic investment cases according to the attribute information; Extracting flow-casting operation information from each of the efficient flow-casting cases, the flow-casting operation information includes a number of flow-casting targets, and a flow-casting order and flow-casting intensity of each flow-casting target; wherein the flow-casting target refers to a network platform; Acquire other flow-casting information of each of the flow-casting targets in a recent period, optimize and adjust the flow-casting operation information according to the other flow-casting information, and obtain target flow-casting operation information; Perform marketing operations on the target to be marketed according to the target flow operation information.

2. A data-driven AI marketing method according to claim 1, characterized in that: The extracting of flow-casting operation information from each of the efficient flow-casting cases includes: Identify each network platform in each of the efficient traffic casting cases, and perform time clustering on the traffic casting data of each network platform; Determine the first flow order for each network platform according to the earliest time of time clustering, and evaluate the first flow intensity of each network platform based on the flow data; Taking each network platform in the efficient traffic casting case with the best traffic casting effect as the traffic casting target, and taking the first traffic casting order corresponding to the network platform as the traffic casting order; Comprehensively analyzing the first flow injection intensity of the same flow injection target in each of the efficient flow injection cases to obtain a flow injection intensity adjustment value, and fine-tuning the first flow injection intensity based on the flow injection intensity adjustment value to obtain a second flow injection intensity; The second flow injection intensity is used as the flow injection intensity of the flow injection target.

3. A data-driven AI marketing method according to claim 2, characterized in that: The step of comprehensively analyzing the first flow injection intensity of the same flow injection target in each of the efficient flow injection cases to obtain a flow injection intensity adjustment value includes: Determine the number of deviations between each of the efficient traffic casting cases and the types of network platforms included in the efficient traffic casting case with the best traffic casting effect, and obtain corresponding weighting coefficients according to the number of deviations; The weighted coefficient is used to perform weighted summation on the first flow injection intensity of the same flow injection target in each of the high-efficiency flow injection cases, and an average value of the weighted summation results is calculated, and the average value is used as the flow injection intensity adjustment value.

4. A data-driven AI marketing method according to claim 3, characterized in that: The acquiring other flow-casting information of each flow-casting target in a recent period, optimizing and adjusting the flow-casting operation information according to the other flow-casting information, and obtaining the target flow-casting operation information includes: Acquire the other flow-casting information having the same or similar attributes as the target to be marketed in a recent period, evaluate and obtain the second flow-casting intensity of each of the other flow-casting information, and calculate and obtain the variance of each of the second flow-casting intensities; A flow intensity adjustment coefficient is determined according to the variance, a target flow intensity is obtained by multiplying the flow intensity adjustment coefficient with the corresponding flow intensity, and the target flow intensity is used to replace the flow intensity in the flow operation information to obtain the target flow operation information.

5. The data-driven AI marketing method according to claim 1, characterized in that: Before performing a marketing operation on the target to be marketed according to the target flow casting operation information, the method further includes: Outputting the target flow-casting operation information and the flow-casting operation information to the marketing personnel at the same time, and after receiving the confirmation information from the marketing personnel, determining to execute the target flow-casting operation information; Alternatively, after receiving the modification information from the marketing personnel, the modified target flow operation information corresponding to the modification information is executed.

6. A data-driven AI marketing system, applied to a marketing agent, comprising a processor and a memory, characterized in that: The processor calls and executes the computer program in the memory to implement the following steps: Receive attribute information of a target to be marketed, and obtain several high-efficiency traffic investment cases according to the attribute information; Extracting flow-casting operation information from each of the efficient flow-casting cases, the flow-casting operation information includes a number of flow-casting targets, and a flow-casting order and flow-casting intensity of each flow-casting target; wherein the flow-casting target refers to a network platform; Acquire other flow-casting information of each of the flow-casting targets in a recent period, optimize and adjust the flow-casting operation information according to the other flow-casting information, and obtain target flow-casting operation information; Perform marketing operations on the target to be marketed according to the target flow operation information.

7. A data-driven AI marketing system according to claim 6, characterized in that: The extracting of flow-casting operation information from each of the efficient flow-casting cases includes: Identify each network platform in each of the efficient traffic casting cases, and perform time clustering on the traffic casting data of each network platform; Determine the first flow order for each network platform according to the earliest time of time clustering, and evaluate the first flow intensity of each network platform based on the flow data; Taking each network platform in the efficient traffic casting case with the best traffic casting effect as the traffic casting target, and taking the first traffic casting order corresponding to the network platform as the traffic casting order; Comprehensively analyzing the first flow injection intensity of the same flow injection target in each of the efficient flow injection cases to obtain a flow injection intensity adjustment value, and fine-tuning the first flow injection intensity based on the flow injection intensity adjustment value to obtain a second flow injection intensity; The second flow injection intensity is used as the flow injection intensity of the flow injection target.

8. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-5.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is executed.

10. A computer program product comprising a computer program stored on a non-transitory computer readable medium, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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