Marketing strategy evaluation methods, devices, equipment, and media based on traffic tagging

Abstract

This invention proposes a marketing strategy evaluation method, apparatus, device, and medium based on traffic tags. The method includes: acquiring multiple target traffic streams generated based on a target marketing strategy, each target traffic stream carrying multiple marketing tags indicating statistical dimensions of the strategy; inputting the marketing tags and analysis requirement information recording multiple candidate levels into an NLP model to obtain a hierarchical set corresponding to each marketing tag, the hierarchical set including the target traffic corresponding to each candidate level; determining the target level of each marketing tag based on the hierarchical set and generating traffic tags for the target traffic; and determining the evaluation result of the target marketing strategy based on the traffic volume of the target traffic corresponding to each traffic tag. According to the technical solution of this invention, hierarchical standards can be dynamically identified based on an NLP model, traffic tags carrying target levels can be added to the target traffic, and evaluation results of the target marketing strategy can be automatically generated using the traffic tags.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a marketing strategy evaluation method, apparatus, device, and medium based on traffic tags. Background Technology

[0002] Currently, various sales application systems frequently employ different marketing campaigns to boost sales, such as setting up different membership levels to offer different benefit packages, or distributing various coupons to users. After the marketing campaign concludes, managers need to analyze the data generated to determine the effectiveness of the marketing strategy.

[0003] In related technologies, managers can already obtain user traffic generated by specific marketing strategies from databases, export the various data carried by the user traffic to relevant tables, and then perform manual analysis to determine the evaluation results of the marketing strategies. However, as the conditions that can be set for marketing strategies increase and the dimensions of traffic data become more numerous, the efficiency of manually analyzing user traffic data is very low, and the reliability of the evaluation results of marketing strategies cannot be guaranteed. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a marketing strategy evaluation method, apparatus, device, and medium based on traffic tagging, which can realize automatic tagging and analysis of user traffic data, improving the reliability of marketing strategy evaluation results.

[0005] In a first aspect, embodiments of the present invention provide a marketing strategy evaluation method based on traffic tags, applied to a marketing system, the method comprising:

[0006] Acquire multiple target traffic streams generated based on a target marketing strategy, wherein each target traffic stream carries multiple marketing tags, which are used to indicate strategy statistical dimensions;

[0007] Obtain analysis requirement information, input the marketing tags and the analysis requirement information into a preset NLP model, obtain the hierarchical set corresponding to each of the marketing tags output by the NLP model, wherein the analysis requirement information records multiple candidate levels, and the hierarchical set includes the target traffic corresponding to each candidate level;

[0008] Based on any of the target traffic, the target level of each of the marketing tags is determined based on the hierarchical set, and traffic tags are generated based on each group of target levels and the corresponding marketing tags;

[0009] Determine the traffic volume of the target traffic corresponding to each of the traffic tags, and determine the evaluation result of the target marketing strategy based on each of the traffic volumes.

[0010] According to some embodiments of the present invention, the marketing system has multiple candidate marketing strategies preset, and the marketing system communicates with the user terminal to obtain multiple target traffic streams generated based on the target marketing strategy, including:

[0011] A strategy traffic entry point is constructed on the user end based on any of the candidate marketing strategies, wherein the strategy traffic entry point is used to indicate the candidate marketing strategy;

[0012] User traffic generated based on any of the aforementioned strategy traffic entry points is identified as candidate traffic and associated with the corresponding candidate marketing strategy;

[0013] Construct a visual interface to display each of the candidate marketing strategies;

[0014] In response to selecting a target marketing strategy from among the plurality of candidate marketing strategies, the candidate traffic corresponding to the target marketing strategy is determined as the target traffic.

[0015] According to some embodiments of the present invention, after constructing a strategy traffic entry point on the user terminal based on any of the candidate marketing strategies, the method further includes:

[0016] The candidate traffic carrying order information is identified as conversion traffic, wherein the order information contains the order amount and the discount amount;

[0017] Based on any of the strategy traffic entry points, the strategy activity is determined based on the ratio of the number of converted traffic to the number of candidate traffic. Based on the order information of all the converted traffic, the traffic cost of each candidate traffic and the marketing metrics of the strategy traffic entry point are determined, wherein the marketing metrics are used to indicate the strategy return rate and the strategy conversion rate, and the traffic cost, the strategy activity, and the marketing metrics are used to determine the evaluation result.

[0018] According to some embodiments of the present invention, after determining the number of traffic flows corresponding to each of the traffic tags, the method further includes:

[0019] The visualization interface generates sub-options for the target marketing strategy, wherein the sub-options include each of the traffic tags;

[0020] In the visualization interface, multiple distribution curves of the target marketing strategy are generated, wherein each distribution curve corresponds to a marketing tag, the vertical axis of the distribution curve is the traffic volume, and the horizontal axis is the candidate level of each marketing tag;

[0021] In response to the selection of any of the traffic labels in the sub-options, the curve segment corresponding to the selected traffic label is highlighted in the distribution curve.

[0022] According to some embodiments of the present invention, obtaining the hierarchical set corresponding to each of the marketing tags output by the NLP model includes:

[0023] The NLP model is used to identify the level threshold corresponding to each candidate level from the analysis requirements information, and the marketing tag associated with each candidate level is determined.

[0024] Based on any of the marketing tags, the candidate levels corresponding to each of the target traffic are determined according to the level threshold, and the corresponding level set is constructed.

[0025] According to some embodiments of the present invention, the analysis requirement information consists of multiple clause description information, and the NLP model identifies the level threshold corresponding to each of the candidate levels from the analysis requirement information, including:

[0026] The NLP model is used to determine valid and invalid tags from a plurality of marketing tags, wherein the valid tags are the marketing tags recorded in the analysis requirement information, and the invalid tags are the marketing tags not recorded in the analysis requirement information;

[0027] The sentence description information containing at least one of the valid tags is determined as the first description information, and the remaining sentence description information is determined as the second description information;

[0028] Determine the first description information associated with each of the second description information;

[0029] Each candidate level and its corresponding level threshold identified from the second description information will be applied to the valid label recorded in the associated first description information.

[0030] According to some embodiments of the present invention, generating traffic tags based on each group of target levels and the corresponding marketing tags includes:

[0031] Determine the target level corresponding to each of the aforementioned marketing tags;

[0032] The target level is combined with the marketing tag to form the traffic tag.

[0033] Secondly, embodiments of the present invention provide a marketing strategy evaluation device based on traffic tags, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the marketing strategy evaluation method based on traffic tags as described in the first aspect above.

[0034] Thirdly, embodiments of the present invention provide an electronic device including a marketing strategy evaluation device based on traffic tags as described in the second aspect above.

[0035] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for performing the marketing strategy evaluation method based on traffic tags as described in the first aspect above.

[0036] The marketing strategy evaluation method based on traffic tags according to embodiments of the present invention has at least the following beneficial effects: It acquires multiple target traffic streams generated based on a target marketing strategy, wherein the target traffic streams carry multiple marketing tags, and the marketing tags are used to indicate strategy statistical dimensions; it acquires analysis requirement information, inputs the marketing tags and the analysis requirement information into a preset NLP model, acquires the hierarchical set corresponding to each of the marketing tags output by the NLP model, wherein the analysis requirement information records multiple candidate levels, and the hierarchical set includes the target traffic streams corresponding to each candidate level; based on any target traffic stream, it determines the target level of each marketing tag based on the hierarchical set, and generates traffic tags based on each set of target levels and corresponding marketing tags; it determines the traffic quantity of the target traffic streams corresponding to each traffic tag, and determines the evaluation result of the target marketing strategy based on the traffic quantity. According to the technical solution of the present invention, the NLP model can automatically add traffic tags carrying target levels to target traffic based on the analysis requirement information, and automatically generate the evaluation result of the target marketing strategy based on the traffic tags, thereby realizing automatic labeling and automatic analysis of user traffic data. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the marketing system provided in one embodiment of the present invention;

[0038] Figure 2 This is a flowchart of a marketing strategy evaluation method based on traffic tags provided in another embodiment of the present invention;

[0039] Figure 3This is a complete flowchart of a marketing strategy evaluation method based on traffic tags provided in another embodiment of the present invention;

[0040] Figure 4 This is a structural diagram of a marketing strategy evaluation device based on traffic tags provided in another embodiment of the present invention. Detailed Implementation

[0041] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0042] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0043] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0044] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0045] This invention provides a marketing strategy evaluation method, apparatus, device, and medium based on traffic tags. The method includes: acquiring multiple target traffic streams generated based on a target marketing strategy, wherein each target traffic stream carries multiple marketing tags, which indicate strategy statistical dimensions; acquiring analysis requirement information, inputting the marketing tags and the analysis requirement information into a preset NLP model, and acquiring a hierarchical set corresponding to each marketing tag output by the NLP model, wherein the analysis requirement information records multiple candidate levels, and the hierarchical set includes the target traffic corresponding to each candidate level; determining the target level of each marketing tag based on any target traffic stream and the hierarchical set, and generating traffic tags based on each target level and its corresponding marketing tag; determining the traffic quantity of the target traffic corresponding to each traffic tag, and determining the evaluation result of the target marketing strategy based on the traffic quantity. According to the technical solution of this invention, the NLP model can automatically add traffic tags carrying target levels to target traffic based on the analysis requirement information, and automatically generate the evaluation result of the target marketing strategy based on the traffic tags, thus realizing automatic labeling and analysis of user traffic data.

[0046] The following is based on the appendix Figure 1 The schematic diagram of the marketing system shown further illustrates the technical solution of this embodiment of the invention.

[0047] Reference Figure 2 , Figure 2 The flowchart illustrates a marketing strategy evaluation method based on traffic tags, as provided in this embodiment of the invention. This method includes, but is not limited to, the following steps:

[0048] S10: Obtain multiple target traffic streams generated based on the target marketing strategy. These target traffic streams carry multiple marketing tags, which are used to indicate the strategy's statistical dimensions.

[0049] It should be noted that the target marketing strategy refers to the candidate marketing strategies that managers need to evaluate. These can be selected through a visual interface, and the candidate marketing strategies can be various promotional methods, such as... Figure 1 The "parking fee promotion" shown attracts users to use the parking lot by offering a certain amount of free time. The "spend and save" promotion, such as a payment discount of 20 yuan off for every 200 yuan spent, is designed to attract users to increase their order amount. Specific strategies will not be limited here.

[0050] It's important to note that after deploying various candidate marketing strategies in the marketing system, these strategies can be applied to user terminals such as user apps and sales websites. When users perform related actions on these terminals, user traffic is generated. The marketing system labels this user traffic based on the candidate marketing strategy selected during access, allowing it to select the corresponding target traffic for different target marketing strategies. For example, when a user pays parking fees in the app using the "2 hours free" payment offer, the resulting user traffic is tagged with the "parking fee promotion" target marketing strategy. When administrators select the corresponding target marketing strategy in the marketing system, the system retrieves the user traffic tagged with "parking fee promotion" from the database as the target traffic.

[0051] It is worth noting that the marketing tags in this embodiment are used to indicate strategy statistical dimensions. These statistical dimensions are specifically indicators used to evaluate the effectiveness of the target marketing strategy. The marketing tags do not carry specific numerical values, but are used to mark the indicators involved in the target traffic. Marketing tags are automatically added after user traffic is obtained. For example, marketing tags may include indicators such as coupons, user activity, user benefits, or generated orders. If the user traffic uses coupons, the marketing tag "coupon" is added to the user traffic. Similarly, the marketing tag "premium member" is added based on the user's membership level. It is not necessary to mark the specific value and type of the coupon. The marketing system can know which statistical dimensions the target traffic has that can be used for analysis based on the marketing tags.

[0052] S20, Obtain analysis requirement information. Input the marketing tags and analysis requirement information into the preset NLP model, and obtain the hierarchical set corresponding to each marketing tag output by the NLP model. The analysis requirement information records multiple candidate levels, and the hierarchical set includes the target traffic corresponding to each candidate level.

[0053] It should be noted that the analytical requirements information is obtained by managers inputting text into the marketing system. Different managers can input different analytical requirements information for the same target marketing strategy, thereby enabling the analysis of a target marketing strategy from different dimensions. For different managers, the target traffic data of the target marketing strategy is the same, but different values ​​can be set to analyze the target traffic, allowing the target marketing strategy to be evaluated according to different standards. In this embodiment, multiple candidate levels are recorded through analytical requirements information. Different candidate levels can be distinguished by numerical ranges, thresholds, or the presence of specific information. The candidate levels of different analytical requirements information can be different, or different standards can be used on the same candidate level. For example, based on the marketing tag of promotion cost, different managers can set different thresholds for high costs and different thresholds for medium costs, thereby quantifying the marketing effect of the target marketing strategy according to different standards on the same target traffic.

[0054] It should be noted that the NLP model can perform natural language analysis and processing. The analysis demand information can carry all marketing tags or only some marketing tags. In this embodiment, all marketing tags are used as the classification basis of the NLP model, so that the NLP model can identify the marketing tags carried in the analysis demand information, extract the corresponding data from the target traffic based on the recorded marketing tags, classify the target traffic based on the candidate level, and directly output the hierarchical set corresponding to each candidate level through the NLP model.

[0055] For example, such as Figure 1 As shown, taking a parking fee promotion as an example, the target marketing strategy can be analyzed by the administrator using the following input: "Analyze the strategy of the parking fee promotion. Analyze order amount, categorizing it into high, medium, and low tiers based on free and 40 yuan; analyze member activity, defining an active member as one purchase every 10 logins; analyze the percentage of coupons, with full discounts representing high promotion costs, less than 50% representing medium promotion costs, and less than 20% representing low promotion costs." The NLP model identifies marketing tags including "promotion cost," "order amount," and "member activity," and identifies three levels for "promotion cost": high, medium, and low. The threshold range for high level is [50%, 100], for medium level it is [20%, 50%), and for low level it is [0%, 20%), and so on for other tags. Based on these threshold ranges, the model can filter the data for each target traffic level to determine the quantity of target traffic for each level (high, medium, and low), resulting in a tiered set as shown below. Figure 1 As shown.

[0056] S30: Based on any target traffic, determine the target level of each marketing tag based on the hierarchical set, and generate traffic tags based on each target level and the corresponding marketing tag.

[0057] It should be noted that after determining the hierarchical set, the target level for each marketing tag of each target traffic can be determined, as shown in the example above. This embodiment uses this as a basis to generate traffic tags for each target traffic. The traffic tags are a combination of the target level and the marketing tag, for example... Figure 1 As shown, adding the target level "high" before the marketing tag "promotion cost" results in the traffic tag "high promotion cost". The traffic tag can characterize the level of the target traffic in each strategy statistical dimension, and the traffic tag is used as the data quantification result of the target traffic.

[0058] S40: Determine the traffic volume of the target traffic corresponding to each traffic tag, and determine the evaluation result of the target marketing strategy based on the traffic volume.

[0059] It should be noted that after determining the traffic tags for each target traffic item, this embodiment performs a simple quantity count for each traffic tag to determine the traffic volume of the target traffic under the same traffic tag. It is worth noting that when a target traffic item can have multiple marketing tags, it also has multiple traffic tags; therefore, each target traffic item can be counted multiple times. Figure 1 As shown, taking traffic 1 as an example, the traffic tags are high promotion cost, medium order amount and low member activity. The traffic volume of traffic 1 under the above three traffic tags is counted respectively.

[0060] It should be noted that after determining the traffic volume, the proportion of each traffic tag can be determined based on the distribution of traffic volume for each traffic tag, thereby generating an evaluation result for the target marketing strategy. For example... Figure 1 As shown, we can determine the percentages of high promotion costs, high order amounts, and high member activity as X, the percentages of high order amounts and high member activity as Y, and the percentages of low promotion costs, low order amounts, and low member activity as Z. This allows us to determine the traffic tags with the highest percentages for the target marketing strategy and whether these traffic tags meet the marketing requirements. Managers can preset the expected percentages for each traffic tag, and the marketing system can automatically analyze whether the targets are met. With relevant data, those skilled in the art are familiar with how to conduct the evaluation, and specific analysis methods will not be limited here.

[0061] Additionally, in one embodiment, reference is made to Figure 3 The marketing system has multiple pre-set candidate marketing strategies. The marketing system communicates with the user terminal. Step S10 specifically includes, but is not limited to, the following steps:

[0062] S11, construct a strategy traffic entry point on the user end based on any candidate marketing strategy, wherein the strategy traffic entry point is used to indicate the candidate marketing strategy;

[0063] S12, identify user traffic generated based on any strategy traffic entry point as candidate traffic and associate it with the corresponding candidate marketing strategy;

[0064] S13, Build a visual interface to display various candidate marketing strategies;

[0065] S14, in response to selecting one target marketing strategy from a plurality of candidate marketing strategies, identifies the candidate traffic corresponding to the target marketing strategy as the target traffic.

[0066] It should be noted that the strategy traffic entry point is determined when user traffic is generated. As described in the above embodiments, each candidate marketing strategy can be applied to the user's end, thereby deploying the strategy traffic entry point for each candidate marketing strategy on the user's end. Whenever a user uses the corresponding candidate marketing strategy, traffic will be generated through the strategy traffic entry point, and thus counted as candidate traffic for the corresponding candidate marketing strategy. The strategy traffic entry point on the user's end can be displayed through promotional options, coupons, etc., ensuring that users are aware of the marketing activity on their end.

[0067] For example, such as Figure 1 As shown, taking a parking fee promotion as an example, users automatically receive a 2-hour parking fee waiver when paying for parking. The parking fee payment interface serves as the strategic traffic entry point. After a user completes the payment, the resulting user traffic, due to the 2-hour parking fee waiver, is generated through this strategic traffic entry point and is counted as candidate traffic for the parking fee promotion. Similarly, taking a buy-one-get-one-free promotion as an example, the strategic traffic entry point is the product selection page. When a user selects two related products on the product selection page, they can either select the buy-one-get-one-free option or have the settlement amount automatically adjusted on the user's end. After completing the order, the corresponding user traffic will be counted as part of the buy-one-get-one-free promotion. If a user selects only one related product on the same product selection page, even if payment is completed, it will not be counted as candidate traffic because there is no relevant option or the strategic traffic entry point is not triggered during automatic settlement.

[0068] It should be noted that after acquiring candidate traffic at each strategic traffic entry point, a correlation is established between the candidate traffic and the candidate marketing strategies. Furthermore, this embodiment displays each candidate marketing strategy on a visual interface, allowing managers to select a target marketing strategy, thereby identifying the candidate traffic of their team members as the target traffic and achieving customized selection of target traffic. For example, as shown... Figure 1As shown, the visualization interface displays various candidate marketing strategies. When a parking fee promotion is selected, the marketing system retrieves parking fee traffic from the database as the target traffic.

[0069] Additionally, in one embodiment, reference is made to Figure 3 After step S11 is completed, the following steps are included, but are not limited to:

[0070] S111, the candidate traffic carrying order information is identified as conversion traffic, wherein the order information contains the order amount and the discount amount;

[0071] S112, based on any strategy traffic entry point, determine strategy activity based on the ratio of converted traffic to candidate traffic, and determine the traffic cost of each candidate traffic and the marketing metrics of the strategy traffic entry point based on the order information of all converted traffic. The marketing metrics are used to indicate the strategy return rate and strategy conversion rate, and the traffic cost, strategy activity, and marketing metrics are used to determine the evaluation results.

[0072] It should be noted that, since this embodiment involves a marketing strategy, traffic that does not convert to orders and traffic that does convert to orders need to be processed separately. After obtaining candidate traffic, this embodiment further identifies traffic carrying order information as conversion traffic. The order information records both the order amount and the discount amount, which facilitates subsequent analysis and processing.

[0073] It is worth noting that the converted traffic is the candidate traffic that has generated orders. Therefore, in this embodiment, the strategy activity is determined based on the ratio of the number of converted traffic to the number of candidate traffic. Since the value of the strategy activity is less than 1, it can be expressed as a percentage.

[0074] It should be noted that, with order information available, the traffic cost for each candidate traffic can be determined. Although some candidate traffic may not generate order information, traffic cost still needs to be calculated to determine the traffic effect of the target marketing strategy. This embodiment determines the discount amount based on the order information of converted traffic, and the sum of the discount amounts is evenly distributed among all candidate traffic to determine the traffic cost.

[0075] It should be noted that the marketing metrics for the strategy traffic entry point are actually the marketing metrics for candidate marketing strategies. In this embodiment, the marketing metrics include strategy return rate and strategy conversion rate. The strategy conversion rate is the ratio of converted traffic to candidate traffic, used to characterize the proportion of traffic that converts into orders. The strategy return rate can be determined by calculating the total revenue based on the total order amount and discount amount of all converted orders, then determining the total profit based on the preset product cost, and finally dividing the total profit by the number of candidate traffic. After determining the traffic cost, strategy activity, and marketing metrics, relevant analysis can be performed based on the above data when determining the evaluation results, which will not be elaborated further here.

[0076] Additionally, in one embodiment, reference is made to Figure 3 After step S40 is completed, the following steps are included, but are not limited to:

[0077] S41, Generate sub-options for the target marketing strategy in the visual interface, where the sub-options include various traffic tags;

[0078] S42 generates multiple distribution curves for the target marketing strategy in the visualization interface. Each distribution curve corresponds to a marketing tag. The vertical axis of the distribution curve is the traffic volume, and the horizontal axis is the candidate level of each marketing tag.

[0079] S43, in response to the selection of any traffic label in the sub-options, highlights the curve segment corresponding to the selected traffic label in the distribution curve.

[0080] It should be noted that after determining each traffic tag, this embodiment constructs sub-options for the target marketing strategy based on each traffic tag in the visualization interface. Administrators can select any traffic tag from these sub-options to trigger the display of data for that specific tag. For example... Figure 1 As shown, the sub-options can include "high promotion cost", "medium promotion cost", "low promotion cost" and "high order amount". After selecting "high promotion cost", the target traffic with the above traffic tags can be displayed on the visualization interface.

[0081] It should be noted that after determining each traffic tag, this embodiment generates multiple distribution curves in the visualization interface. Each distribution curve corresponds to a marketing tag. The vertical axis of the distribution curve is the traffic quantity, and the horizontal axis is the distribution of different candidate levels. For example, the marketing tag takes "promotion cost" as an example. The horizontal axis is 0%-100%. It can be divided into 10% increments. The distribution curve is constructed by taking the traffic quantity of the target traffic with a promotion cost of 10% as a coordinate point.

[0082] It's important to note that after clicking on a traffic tag, since the traffic tag is a combination of the target level and the marketing tag, the horizontal axis area can be determined based on the traffic tag's level portion, and the vertical axis area can be determined based on the traffic volume. This allows you to identify the segment of the curve where the traffic tag falls within the multiple distribution curves. In other words, each selected traffic tag will display a curve segment across multiple distribution curves, reflecting the trend of that traffic tag.

[0083] For example, for a traffic label like "high promotion cost, medium order amount, low member activity," the segment with a "high" horizontal axis is identified and highlighted in the distribution curve corresponding to the marketing label "promotion cost." This segment represents the portion of the curve where the horizontal axis is between 50% and 100%. This process is repeated for other marketing labels. Ultimately, segments with a "high" horizontal axis are identified in the distribution curve corresponding to "promotion cost," segments with a "medium" horizontal axis are identified in the distribution curve corresponding to "order amount," and segments with a "low" horizontal axis are identified in the distribution curve corresponding to "member activity." Administrators can use these curve segments to determine the traffic distribution under each traffic label, facilitating subsequent analysis.

[0084] Additionally, in one embodiment, reference is made to Figure 3 Step S20 specifically includes, but is not limited to, the following steps:

[0085] S21, using an NLP model to identify the level threshold corresponding to each candidate level from the analyzed demand information, and determine the marketing tag associated with each candidate level;

[0086] S22, based on any marketing tag, determine the candidate level corresponding to each target traffic according to the level threshold and construct the corresponding level set.

[0087] It should be noted that this embodiment can analyze the same batch of target traffic according to different standards. Therefore, managers need to record the level threshold corresponding to each candidate level in the analysis requirement information, so that the NLP model can determine the level threshold corresponding to each candidate level through simple semantic analysis. The level threshold can be in the form of an interval. Marketing tags that meet the level threshold are categorized into the corresponding candidate level, for example... Figure 1 As shown, the threshold for the level corresponding to high promotion cost is [50%, 100%]. When the value corresponding to the tag of promotion cost of target traffic is 60%, it can be determined as the corresponding candidate level as high level.

[0088] It should be noted that this embodiment uses marketing tags as the unit, determines the corresponding candidate level for each target traffic, and puts all target traffic of the same candidate level into the same hierarchical set to provide a basis for subsequent traffic labeling.

[0089] Additionally, in one embodiment, reference is made to Figure 3 The analysis of the demand information consists of multiple descriptive clauses. Step S21 specifically includes, but is not limited to, the following steps:

[0090] S211, use an NLP model to determine valid and invalid tags from multiple marketing tags, where valid tags are marketing tags recorded in the analysis demand information, and invalid tags are marketing tags not recorded in the analysis demand information;

[0091] S212, the clause description information containing at least one valid tag is determined as the first description information, and the remaining clause description information is determined as the second description information;

[0092] S213, determine the first description information associated with each second description information;

[0093] S214, each candidate level and its corresponding level threshold identified from the second description information are applied to the valid labels recorded in the associated first description information.

[0094] It should be noted that since the analysis requirement information can be set according to actual needs, managers do not necessarily need to analyze all marketing tags when performing an analysis. Therefore, the analysis requirement information can record only a few marketing tags. In this embodiment, the recorded tags are determined as valid tags, and the unrecorded tags are determined as invalid tags, thereby reducing the processing load of the NLP model.

[0095] It should be noted that the analysis requirement information consists of multiple descriptive sentences. In this embodiment, the sentences containing valid tags are identified as the first descriptive information, and the rest are identified as the second descriptive information. Managers may have different descriptive habits, and the positions of the second descriptive information and the first descriptive information may be interchanged. However, the arrangement followed by the same manager in a single description is usually the same. Therefore, the association between the two can be determined by the positions of the first first descriptive information and the first second descriptive information. If the first first descriptive information is located before the first second descriptive information, then the second descriptive information associated with each first descriptive information is located after it.

[0096] It should be noted that after determining the second descriptive information, the candidate level and the corresponding threshold can be determined through simple semantic recognition, and then associated with the valid labels recorded in the first descriptive information.

[0097] For example, such as Figure 1The analysis requirement information shown is "Strategy analysis of parking fee promotion activities. Analyze order amount, categorize into high, medium, and low tiers based on free and 40 yuan; analyze member activity, define an active member as one purchase every 10 logins; analyze the proportion of coupons, full discount is high promotion cost, less than 50% is medium promotion cost, and less than 20% is low cost." Taking this example, "Analyze order amount" is the first descriptive information, and "categorize into high, medium, and low tiers based on free and 40 yuan" is the second descriptive information. These two are interconnected, with "order amount" as the effective tag. The second descriptive information identifies a high tier threshold greater than 40 yuan, a medium tier threshold between 0 and 40 yuan, and a low tier threshold of 0 yuan, thus determining the tier thresholds for multiple candidate tiers. Other descriptive information follows the same principle.

[0098] Additionally, in one embodiment, reference is made to Figure 3 Step S30 specifically includes, but is not limited to, the following steps:

[0099] S31, determine the target level corresponding to each marketing tag;

[0100] S32 combines the target level with the marketing tag to form a traffic tag.

[0101] It should be noted that the marketing tags in this embodiment are only used to represent statistical dimensions, such as "member activity," "order amount," and "promotion cost," and do not represent specific levels. Therefore, this embodiment concatenates the target level and marketing tags to obtain traffic tags, enabling the traffic tags to reflect specific target levels. This achieves automatic classification of target traffic, facilitating subsequent analysis of the target traffic. When there are multiple marketing tags for the target traffic, the operations in this embodiment can be performed one by one.

[0102] like Figure 4 As shown, Figure 4 This is a structural diagram of a marketing strategy evaluation device based on traffic tags according to an embodiment of the present invention. The present invention also provides a marketing strategy evaluation device based on traffic tags, comprising:

[0103] The processor 401 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0104] The memory 402 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and called by the processor 401 to execute the marketing strategy evaluation method based on traffic tags according to the embodiments of this application.

[0105] Input / output interface 403 is used to implement information input and output;

[0106] The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0107] Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404);

[0108] The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0109] This application also provides an electronic device, including the marketing strategy evaluation device based on traffic tags as described above.

[0110] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described marketing strategy evaluation method based on traffic tags.

[0111] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0113] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A marketing strategy evaluation method based on traffic tags, characterized in that, The method is applied to a marketing system, which has multiple candidate marketing strategies preset, and the marketing system communicates with a user terminal. A strategy traffic entry point is constructed on the user end based on any of the candidate marketing strategies, wherein the strategy traffic entry point is used to indicate the candidate marketing strategy; User traffic generated based on any of the aforementioned strategy traffic entry points is identified as candidate traffic and associated with the corresponding candidate marketing strategy; Construct a visual interface to display each of the candidate marketing strategies; In response to selecting a target marketing strategy from a plurality of candidate marketing strategies, the candidate traffic corresponding to the target marketing strategy is determined as target traffic, wherein the target traffic carries a plurality of marketing tags, the marketing tags being used to indicate strategy statistical dimensions; The analysis requirement information is obtained, and the marketing tags and the analysis requirement information are input into a preset NLP model. The NLP model identifies the level threshold corresponding to each candidate level from the analysis requirement information, determines the marketing tag associated with each candidate level, and, based on any marketing tag, determines the candidate level corresponding to each target traffic according to the level threshold and constructs a corresponding hierarchical set. The analysis requirement information records multiple candidate levels, and the hierarchical set includes the target traffic corresponding to each candidate level. Based on any of the target traffic, the target level of each of the marketing tags is determined based on the hierarchical set, and traffic tags are generated based on each group of target levels and the corresponding marketing tags; Determine the traffic volume of the target traffic corresponding to each of the traffic tags, and determine the evaluation result of the target marketing strategy based on each of the traffic volumes.

2. The marketing strategy evaluation method based on traffic tags according to claim 1, characterized in that, After constructing a strategy traffic entry point on the user's end based on any of the candidate marketing strategies, the method further includes: The candidate traffic carrying order information is identified as conversion traffic, wherein the order information contains the order amount and the discount amount; Based on any of the strategy traffic entry points, the strategy activity is determined based on the ratio of the number of converted traffic to the number of candidate traffic. Based on the order information of all the converted traffic, the traffic cost of each candidate traffic and the marketing metrics of the strategy traffic entry point are determined, wherein the marketing metrics are used to indicate the strategy return rate and the strategy conversion rate, and the traffic cost, the strategy activity, and the marketing metrics are used to determine the evaluation result.

3. The marketing strategy evaluation method based on traffic tags according to claim 1, characterized in that, After determining the number of traffic items corresponding to each of the traffic tags for the target traffic, the method further includes: The visualization interface generates sub-options for the target marketing strategy, wherein the sub-options include each of the traffic tags; In the visualization interface, multiple distribution curves of the target marketing strategy are generated, wherein each distribution curve corresponds to a marketing tag, the vertical axis of the distribution curve is the traffic volume, and the horizontal axis is the candidate level of each marketing tag; In response to the selection of any of the traffic labels in the sub-options, the curve segment corresponding to the selected traffic label is highlighted in the distribution curve.

4. The marketing strategy evaluation method based on traffic tags according to claim 1, characterized in that, The analysis requirement information consists of multiple descriptive clauses. The NLP model identifies the corresponding level thresholds for each candidate level from the analysis requirement information, including: The NLP model is used to determine valid and invalid tags from a plurality of marketing tags, wherein the valid tags are the marketing tags recorded in the analysis requirement information, and the invalid tags are the marketing tags not recorded in the analysis requirement information; The sentence description information containing at least one of the valid tags is determined as the first description information, and the remaining sentence description information is determined as the second description information; Determine the first description information associated with each of the second description information; Each candidate level and its corresponding level threshold identified from the second description information will be applied to the valid label recorded in the associated first description information.

5. The marketing strategy evaluation method based on traffic tags according to claim 1, characterized in that, Traffic tags are generated based on each target level and the corresponding marketing tag, including: Determine the target level corresponding to each of the aforementioned marketing tags; The target level is combined with the marketing tag to form the traffic tag.

6. A marketing strategy evaluation device based on traffic tags, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; The memory stores instructions that can be executed by the at least one control processor to enable the at least one control processor to perform the marketing strategy evaluation method based on traffic tags as described in any one of claims 1 to 5.

7. An electronic device, characterized in that, It includes the marketing strategy evaluation device based on traffic tags as described in claim 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the marketing strategy evaluation method based on traffic tags as described in any one of claims 1 to 5.

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