Marketing strategy evaluation method and device based on flow label, equipment and medium

Abstract

The invention provides a marketing strategy evaluation method, device and equipment based on flow labels and a medium, and the method comprises the steps: obtaining a plurality of pieces of target flow generated based on a target marketing strategy, the target flow carrying a plurality of marketing labels used for indicating strategy statistics dimensions; inputting the marketing labels and the analysis demand information recording a plurality of candidate levels into an NLP model to obtain a level set corresponding to each marketing label, wherein the level set comprises target traffic corresponding to each candidate level; determining a target grade of each marketing label based on the grading set and generating a traffic label of the target traffic; and determining an evaluation result of the target marketing strategy based on the traffic quantity of the target traffic corresponding to each traffic label. According to the technical scheme of the embodiment of the invention, the grading standard can be dynamically identified according to the NLP model, the traffic label carrying the target grade is added to the target traffic, and the evaluation result of the target marketing strategy is automatically generated by using the traffic label.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a marketing strategy evaluation method, device, equipment and medium based on traffic labels. Background Art

[0002] Currently, various sales application systems often use various marketing campaigns to increase sales, such as setting up different membership tiers to provide different benefit packages or distributing various coupons to users. After the marketing campaign, managers need to analyze the marketing strategy based on the data generated to determine its effectiveness.

[0003] In related technologies, managers can already obtain user traffic generated by specific marketing strategies from databases, export the various data carried by this traffic into relevant tables, and then manually analyze it to determine the marketing strategy evaluation results. However, as the number of conditions that can be set for marketing strategies increases and the number of dimensions of traffic data increases, manual analysis of user traffic data becomes extremely inefficient, and the reliability of marketing strategy evaluation results cannot be guaranteed. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a marketing strategy evaluation method, apparatus, device, and medium based on traffic labels, which can automatically label and analyze user traffic data, thereby improving the reliability of marketing strategy evaluation results.

[0005] In a first aspect, an embodiment of the present invention provides a marketing strategy evaluation method based on traffic labels, which is applied to a marketing system. The method includes:

[0006] Acquire multiple target flows generated based on a target marketing strategy, wherein the target flows carry multiple marketing tags, and the marketing tags are used to indicate strategy statistical dimensions;

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

[0008] Based on any of the target traffic, determine a target level for each of the marketing tags based on the classification set, and generate a traffic tag based on each set of the target levels and the corresponding marketing tags;

[0009] Determine the traffic quantity 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 quantities.

[0010] According to some embodiments of the present invention, the marketing system is preset with multiple candidate marketing strategies. The marketing system is in communication with the user terminal to obtain multiple target traffic generated based on the target marketing strategy, including:

[0011] Constructing a strategy traffic entry on the user terminal based on any of the candidate marketing strategies, wherein the strategy traffic entry is used to indicate the candidate marketing strategy;

[0012] Determine the user traffic generated based on any of the strategy traffic entrances as candidate traffic and associate it with the corresponding candidate marketing strategy;

[0013] Constructing a visualization interface to display each of the candidate marketing strategies in the visualization interface;

[0014] In response to a target marketing strategy selected from a 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 strategic traffic entry on the user terminal based on any of the candidate marketing strategies, the method further includes:

[0016] determining the candidate traffic carrying order information as conversion traffic, wherein the order information records the order amount and discount amount;

[0017] Based on any of the strategic traffic entrances, the strategic activity is determined based on the ratio of the conversion traffic and the candidate traffic, and based on the order information of all the conversion traffic, the traffic cost of each candidate traffic and the marketing indicators of the strategic traffic entrance are determined, wherein the marketing indicators are used to indicate the strategic yield and the strategy conversion rate, and the traffic cost, the strategic activity and the marketing indicators are used to determine the evaluation results.

[0018] According to some embodiments of the present invention, after determining the flow quantity of the target flow corresponding to each of the flow labels, the method further includes:

[0019] Generating sub-options of the target marketing strategy in the visual interface, wherein the sub-options include each of the traffic tags;

[0020] Generating a plurality of distribution curves of the target marketing strategy in the visualization interface, wherein each distribution curve corresponds to one of the marketing tags, the ordinate of the distribution curve is the traffic quantity, and the abscissa is the candidate level of each of the marketing tags;

[0021] In response to any of the flow labels in the lower-level options being selected, a curve segment corresponding to the selected flow label is highlighted in the distribution curve.

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

[0023] Identifying, by the NLP model, the level threshold corresponding to each of the candidate levels from the analysis requirement information, and determining the marketing tag associated with each of the candidate levels;

[0024] Based on any of the marketing tags, the candidate level corresponding to each target flow is 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 is composed of a plurality of sentence description information, and identifying the level threshold corresponding to each of the candidate levels from the analysis requirement information by the NLP model includes:

[0026] Determining valid tags and invalid tags from the plurality of marketing tags using the NLP model, 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] Determining the sentence description information having at least one valid tag as first description information, and determining the remaining sentence description information as second description information;

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

[0029] Each candidate grade and the corresponding grade threshold identified from the second description information are applied to the valid label recorded in the associated first description information.

[0030] According to some embodiments of the present invention, generating a traffic tag based on each set of the target level and the corresponding marketing tag includes:

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

[0032] The target level and the marketing tag are concatenated into the traffic tag.

[0033] In the second aspect, an embodiment of the present invention provides a marketing strategy evaluation device based on traffic labels, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the marketing strategy evaluation method based on traffic labels as described in the first aspect above.

[0034] In a third aspect, an embodiment of the present invention provides an electronic device comprising a marketing strategy evaluation device based on traffic labels as described in the second aspect above.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the marketing strategy evaluation method based on traffic labels as described in the first aspect above.

[0036] According to the marketing strategy evaluation method based on traffic labels of the embodiment of the present invention, there are at least the following beneficial effects: obtaining multiple target flows generated based on the target marketing strategy, wherein the target flows carry multiple marketing labels, and the marketing labels are used to indicate the statistical dimensions of the strategy; obtaining analysis requirement information, inputting the marketing labels and the analysis requirement information into a preset NLP model, obtaining the hierarchical set corresponding to each of the marketing labels output by the NLP model, wherein the analysis requirement information records multiple candidate levels, and the hierarchical set includes the target flows corresponding to each candidate level; based on any of the target flows, determining the target level of each of the marketing labels based on the hierarchical set, and generating traffic labels based on each group of the target levels and the corresponding marketing labels; determining the traffic volume of the target flows corresponding to each of the traffic labels, and determining the evaluation result of the target marketing strategy based on each of the traffic volumes. According to the technical solution of the embodiment of the present invention, the NLP model can automatically add traffic labels carrying target levels to the target flows based on the analysis requirement information, automatically generate the evaluation result of the target marketing strategy based on the traffic labels, and realize automatic labeling and automatic analysis of user traffic data. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0040] Figure 4 It is a structural diagram of a marketing strategy evaluation device based on traffic labels provided by another embodiment of the present invention. DETAILED DESCRIPTION

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

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

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

[0044] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0045] The embodiment of the present invention provides a marketing strategy evaluation method, device, equipment and medium based on traffic labels, wherein the marketing strategy evaluation method based on traffic labels includes: obtaining multiple target flows generated based on a target marketing strategy, wherein the target flows carry multiple marketing labels, and the marketing labels are used to indicate the statistical dimensions of the strategy; obtaining analysis requirement information, inputting the marketing labels and the analysis requirement information into a preset NLP model, obtaining a hierarchical set corresponding to each of the marketing labels output by the NLP model, wherein the analysis requirement information records multiple candidate levels, and the hierarchical set includes the target flows corresponding to each candidate level; based on any of the target flows, determining the target level of each marketing label based on the hierarchical set, and generating a traffic label based on each group of the target level and the corresponding marketing label; determining the flow volume of the target flow corresponding to each traffic label, and determining the evaluation result of the target marketing strategy based on each traffic volume. According to the technical solution of the embodiment of the present invention, the NLP model can automatically add a traffic label carrying a target level to the target flow based on the analysis requirement information, automatically generate the evaluation result of the target marketing strategy based on the traffic label, and realize automatic labeling and automatic analysis of user flow data.

[0046] The following is based on the Figure 1 The principle diagram of the marketing system shown in the figure further illustrates the technical solution of the embodiment of the present invention.

[0047] Reference Figure 2 , Figure 2 A flowchart of a marketing strategy evaluation method based on traffic labels provided by an embodiment of the present invention includes but is not limited to the following steps:

[0048] S10, obtaining multiple target flows generated based on the target marketing strategy, wherein the target flows carry multiple marketing tags, and the marketing tags are used to indicate strategy statistical dimensions.

[0049] It should be noted that the target marketing strategy is the candidate marketing strategy that managers need to evaluate, which can be selected through a visual interface. The candidate marketing strategy can be various promotion 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. Another example is the "full-discount promotion," which offers a 20 yuan discount on purchases over 200 yuan to attract users to increase their order amounts. Specific strategies are not limited here.

[0050] It should be noted that after the marketing system deploys each candidate marketing strategy, the candidate marketing strategy can be applied on user terminals such as user apps and sales websites. When users perform relevant operations on the user terminals, user traffic is generated. The marketing system labels the user traffic based on the candidate marketing strategy selected when the user traffic is accessed, thereby selecting the corresponding target traffic based on different target marketing strategies. For example, when a user pays for parking in the app and uses the "2 hours free" payment discount, the generated user traffic is marked as using the target marketing strategy of "parking fee promotion". When the manager selects the corresponding target marketing strategy in the marketing system, the marketing system can obtain the user traffic with the label "parking fee promotion" from the database as the target traffic.

[0051] It is worth noting that the marketing tag of this embodiment is used to indicate the strategy statistical dimension. The strategy statistical dimension is specifically reflected in the indicator used to evaluate the pros and cons of the target marketing strategy. The marketing tag does not carry a specific numerical value, but is used to mark the indicators involved in the target traffic. The marketing tag is automatically added after obtaining the user traffic. For example, the marketing tag may include coupons, user activity, user rights or generated orders and other indicators. If the user traffic uses a coupon, the marketing tag "coupon" is added to the user traffic. For example, the marketing tag "premium member" is added according to the user's membership level. There is no need to mark the specific value and type of the coupon. The marketing system only needs to know which statistical dimensions of the target traffic that can be used for analysis based on the marketing tag.

[0052] S20, obtain analysis demand information, input the marketing tags and analysis demand information into the preset NLP model, and obtain the hierarchical set corresponding to each marketing tag output by the NLP model, wherein the analysis demand 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 analysis demand information is obtained by the administrator entering text in the marketing system. Different administrators can enter different analysis demand information for the same target marketing strategy, thereby realizing analysis of a target marketing strategy in different dimensions. For different administrators, the target traffic data of the target marketing strategy is the same, but different numerical values ​​can be set to analyze the target traffic, so that the target marketing strategy can be evaluated with different standards. This embodiment records multiple candidate levels through analysis demand information, and different candidate levels can be distinguished by numerical ranges, thresholds, or whether they have specific information. The candidate levels of different analysis demand information can be different, or different standards can be used on the basis of the same candidate level. For example, based on the marketing label of promotion cost, different administrators can set different thresholds for high costs and different thresholds for medium costs, thereby quantifying the marketing effect of the target marketing strategy with different standards based on the same target traffic.

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

[0055] For example, Figure 1 As shown in the figure, the target marketing strategy takes the parking fee promotion as an example. The manager can input "strategy analysis of parking fee promotion. Analyze the order amount and divide it into high, medium and low grades according to free and 40 yuan; analyze member activity, and make an active member after every 10 logins and purchases; analyze the proportion of coupons, and a full deduction is a high promotion cost, a proportion below 50% is a medium promotion cost, and a proportion below 20% is a low cost." As analysis demand information, the marketing tags identified by the NLP model include "promotion cost", "order amount" and "member activity", and the levels of "promotion cost" are identified as high, medium and low. The threshold interval corresponding to the high level is [50%, 100], the threshold interval corresponding to the medium level is [20%, 50%), and the threshold interval corresponding to the low level is [0%, 20%). The other tags are deduced in the same way. Based on the threshold interval, the data of each target flow can be screened to determine the number of target flows of high, medium and low levels. The obtained graded set is as follows: Figure 1 shown.

[0056] S30, based on any target traffic, determine the target level of each marketing tag based on the classification set, and generate a traffic tag based on each set of target levels and corresponding marketing tags.

[0057] It should be noted that after determining the classification set, the target level of each marketing tag of each target flow can be determined, and the specific reference is made to the above example. Based on this, this embodiment generates a flow tag for each target flow, and the flow tag is the superposition of the target level and the marketing tag, for example Figure 1 As shown in the figure, by adding the target level "high" before the marketing label "promotion cost", the resulting traffic label is "high promotion cost". The traffic label can be used to characterize the level of the target traffic in each strategy statistical dimension, and the traffic label is used as the data quantification result of the target traffic.

[0058] S40, determining the traffic quantity of the target traffic corresponding to each traffic label, and determining the evaluation result of the target marketing strategy based on each traffic quantity.

[0059] It should be noted that after determining the traffic labels of each target flow, this embodiment performs a simple quantity statistics for each traffic label, thereby determining the traffic quantity of the target flow under the same traffic label. It is worth noting that in the case where the target flow can have multiple marketing labels, the target flow also has multiple traffic labels, so each target flow can be counted multiple times, for example Figure 1 As shown, taking traffic 1 as an example, the traffic labels are high promotion cost, medium order amount and low member activity, and the traffic quantity of the above three traffic labels of traffic 1 is counted respectively.

[0060] It should be noted that after determining the amount of traffic, the proportion of each type of traffic label can be determined according to the distribution of the amount of traffic of each traffic label, thereby generating the evaluation results of the target marketing strategy, for example Figure 1 As shown, the proportion of high promotion costs, high order amounts and high member activity can be determined as X, the proportion of high order amounts and high member activity can be determined as Y, and the proportion of low promotion costs, low order amounts and low member activity can be determined as Z, so as to determine the traffic tag with the largest proportion of the target marketing strategy and whether the traffic tag with the largest proportion meets the marketing needs. Managers can preset the expected proportion of each traffic tag and automatically analyze whether it meets the requirements through the marketing system. When relevant data is available, technical personnel in this field are familiar with how to conduct the evaluation, and the specific analysis method is not limited here.

[0061] In addition, in one embodiment, referring to Figure 3 The marketing system presets multiple candidate marketing strategies. The marketing system is connected to the user terminal for communication. Step S10 specifically includes but is not limited to the following steps:

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

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

[0064] S13, building a visualization interface to display each candidate marketing strategy in the visualization interface;

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

[0066] It should be noted that the strategic traffic entry is determined when generating user traffic. According to the description of the above embodiment, each candidate marketing strategy can be applied to the user end, thereby deploying a strategic traffic entry for each candidate marketing strategy on the user end. As long as the user uses the corresponding candidate marketing strategy, traffic will be generated through the strategic traffic entry, and thus counted as candidate traffic for the corresponding candidate marketing strategy. The strategic traffic entry can be presented on the user end through promotional options, coupons, etc., so that users can be aware of marketing activities on the user end.

[0067] For example, Figure 1 As shown, taking a parking fee promotion as an example, when users pay for parking, two hours of parking fees are automatically waived. The parking fee payment interface is the strategic traffic entry. After the user completes the parking fee payment, the generated user traffic is generated through the strategic traffic entry because the two hours of parking fee exemption is applied. Therefore, the user traffic is generated through the strategic traffic entry 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 is the product selection page. When a user selects two related products on the product selection page, they can select the buy-one-get-one-free option or the user end automatically adjusts the settlement amount. After completing the order, the corresponding user traffic value can be counted as a buy-one-get-one-free promotion. If the user selects one related product on the same product selection page, even if the payment is completed, it will not be counted as candidate traffic because there is no relevant option or the strategic traffic entry is triggered during automatic settlement.

[0068] It should be noted that after obtaining candidate traffic in units of policy traffic entry, an association relationship between candidate traffic and candidate marketing strategies is established. Furthermore, this embodiment displays each candidate marketing strategy on a visual interface, allowing managers to select a target marketing strategy from among them, thereby determining the candidate traffic of teammates as the target traffic, and realizing customized selection of target traffic. For example, Figure 1As shown, each candidate marketing strategy is displayed in the visual interface. When the parking fee promotion activity is selected, the marketing system pulls the parking fee traffic from the database as the target traffic.

[0069] In addition, in one embodiment, referring to Figure 3 After executing step S11, the following steps are also included but not limited to:

[0070] S111, determining candidate traffic carrying order information as conversion traffic, wherein the order information records the order amount and discount amount;

[0071] S112, based on any strategy traffic entrance, determine the strategy activity based on the ratio of the conversion traffic and the candidate traffic, and based on the order information of all conversion traffic, determine the traffic cost of each candidate traffic and the marketing indicators of the strategy traffic entrance, where the marketing indicators are used to indicate the strategy yield and strategy conversion rate, and the traffic cost, strategy activity and marketing indicators are used to determine the evaluation results.

[0072] It should be noted that since this embodiment involves a marketing strategy, traffic that did not convert into orders and traffic that did convert into orders need to be treated separately. After obtaining candidate traffic, this embodiment further identifies traffic that carries order information as converted traffic. Order information also records the order amount and discount amount to facilitate subsequent analysis and processing.

[0073] It is worth noting that the conversion traffic is the candidate traffic that generates orders. Therefore, this embodiment determines the strategy activity based on the ratio of the conversion traffic to the candidate traffic. It can be determined that the value of the strategy activity is less than 1, so it can be expressed in the form of a percentage.

[0074] It should be noted that, given order information, the traffic cost of each candidate traffic can be determined. Although some candidate traffic does not convert into order information, traffic cost statistics are still needed to determine the traffic effect generated by the target marketing strategy. This embodiment determines the discount amount based on the order information of the converted traffic, and evenly distributes the sum of the discount amounts to all candidate traffic to determine the traffic cost.

[0075] It should be noted that the marketing indicators of the strategic traffic entrance are actually the marketing indicators of the candidate marketing strategies. The marketing indicators of this embodiment include the strategic yield rate and the strategic conversion rate. The strategic conversion rate is the ratio of the conversion traffic to the candidate traffic, which is used to characterize the proportion of traffic converted into orders; the strategic yield rate can be determined based on the order amount and discount amount of all converted orders to determine the total revenue, and then the total revenue is determined based on the preset product cost. The strategic yield rate can be determined by dividing the total revenue by the number of candidate traffic. After determining the traffic cost, strategic activity and marketing indicators, relevant analysis can be performed based on the above data when determining the evaluation results, and no further details will be given here.

[0076] In addition, in one embodiment, referring to Figure 3 After executing step S40, the following steps are also included but not limited to:

[0077] S41, generating sub-options of the target marketing strategy in a visual interface, wherein the sub-options include various traffic labels;

[0078] S42, generating multiple distribution curves of the target marketing strategy in a visualization interface, wherein each distribution curve corresponds to a marketing tag, the ordinate of the distribution curve is the amount of traffic, and the abscissa is the candidate level of each marketing tag;

[0079] S43: In response to any flow label in the lower-level options being selected, highlighting the curve segment corresponding to the selected flow label in the distribution curve.

[0080] It should be noted that after determining each traffic tag, this embodiment constructs the target marketing strategy sub-options based on each traffic tag in the visual interface. Managers can select any traffic tag in the sub-options to trigger the display of data of a specific tag. Figure 1 As shown, the lower-level options may include "high promotion cost", "medium promotion cost", "low promotion cost" and "high order amount", etc. After selecting "high promotion cost", the target traffic with the above traffic labels can be displayed in the visual interface.

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

[0082] It's important to note that after clicking a traffic label, since the traffic label is a combination of the target level and the marketing label, the horizontal axis area is determined by the level portion of the traffic label, and the vertical axis area is determined by the traffic volume. This, in turn, allows you to determine the curve segment of the traffic label across the multiple distribution curves involved. Each time you select a traffic label, a curve segment will be displayed across the multiple distribution curves to reflect the trend of that traffic label.

[0083] For example, for the traffic label "high promotion cost, medium order amount, low member activity", the segment with the horizontal coordinate of "high" is determined and highlighted in the distribution curve corresponding to the marketing label "promotion cost", that is, the curve composed of the portion with the horizontal coordinate of 50% to 100% of the curve segment. The same is true for other marketing labels. Finally, the segment with the horizontal coordinate of "high" is determined in the distribution curve corresponding to "promotion cost", the segment with the horizontal coordinate of "medium" is determined in the distribution curve corresponding to "order amount", and the segment with the horizontal coordinate of "low" is determined in the distribution curve corresponding to "member activity". Managers can judge the traffic distribution under the traffic label based on the curve segmentation, which is convenient for subsequent analysis.

[0084] In addition, in one embodiment, referring to Figure 3 In step S20, the following steps are specifically included but not limited to:

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

[0086] S22: Based on any marketing tag, determine the candidate level corresponding to each target flow according to the level threshold and construct a corresponding level set.

[0087] It should be noted that this embodiment can analyze the same batch of target traffic according to different standards. Therefore, the administrator needs 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, and the marketing tags that meet the level threshold are classified into the corresponding candidate level, for example Figure 1 As shown, the level threshold corresponding to high promotion cost is [50%, 100%]. When the value corresponding to the label of promotion cost of target traffic is 60%, it can be determined that the corresponding candidate level is high.

[0088] It should be noted that this embodiment uses marketing tags as units to determine a corresponding candidate level for each target flow, and puts each target flow of the same candidate level into the same grading set to provide a basis for subsequent flow labeling.

[0089] In addition, in one embodiment, referring to Figure 3 The analysis requirement information is composed of multiple clause description information. Step S21 specifically includes but is not limited to the following steps:

[0090] S211, determining valid tags and invalid tags from a plurality of marketing tags using an NLP model, wherein 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, determining the sentence description information having at least one valid tag as the first description information, and determining the remaining sentence description information as the second description information;

[0092] S213, determining the first description information associated with each piece of second description information;

[0093] S214: Identify each candidate level and the corresponding level threshold from the second description information and apply them to the valid label 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 have to analyze all marketing tags when conducting an analysis. Therefore, only a few marketing tags can be recorded in the analysis requirement information. This embodiment determines the recorded tags as valid tags and the unrecorded tags as invalid tags, thereby reducing the processing volume of the NLP model.

[0095] It should be noted that the analysis requirement information is composed of multiple sentence description information. In this embodiment, the valid labels are determined as the first description information, and the rest are determined as the second description information. The description habits of managers may be different, and the positions of the second description information and the first description information can be interchanged, but the arrangement method followed by the same manager in a description is usually the same. Therefore, the association method between the two can be determined by the position of the first first description information and the first second description information. If the first first description information is located before the first second description information, it can be determined that the second description information associated with each first description information is located after it.

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

[0097] For example, Figure 1Taking the analysis requirement information shown as "Strategic analysis of parking fee promotion activities. Analyze the order amount and divide it into high, medium and low grades based on free and 40 yuan; analyze member activity, and active members are those who purchase once every 10 logins; analyze the proportion of coupons, and a full deduction is a high promotion cost, a proportion below 50% is a medium promotion cost, and a proportion below 20% is a low cost" as an example, "Analyze the order amount" is the first description information, and "Divide it into high, medium and low grades based on free and 40 yuan" is the second description information, and the two are interrelated, with the effective label being "order amount". The second description information identifies that the level threshold of the high grade is greater than 40 yuan, the level threshold of the medium grade is 0 to 40 yuan, and the level threshold of the low grade is 0 yuan, thereby determining the level thresholds corresponding to multiple candidate grades, and the same applies to other sentence description information.

[0098] In addition, in one embodiment, referring to Figure 3 Step S30 specifically includes but is not limited to the following steps:

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

[0100] S32, concatenates the target level and the marketing label into a traffic label.

[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 "promotional cost," and do not represent specific levels. Therefore, this embodiment combines the target level and the marketing tag to obtain a traffic tag, so that the traffic tag can reflect the specific target level, thereby achieving automatic classification of target traffic and facilitating subsequent analysis of the target traffic. When the target traffic has multiple marketing tags, the operations of 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 labels provided by an embodiment of the present invention. The present invention also provides a marketing strategy evaluation device based on traffic labels, including:

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

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

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

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

[0107] Bus 405 , which 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 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via a bus 405 .

[0109] An embodiment of the present application also provides an electronic device, including the marketing strategy evaluation device based on traffic labels as described above.

[0110] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned marketing strategy evaluation method based on traffic labels.

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

[0112] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0113] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A marketing strategy evaluation method based on traffic labels, characterized in that: Applied to a marketing system, the method includes: Acquire multiple target flows generated based on a target marketing strategy, wherein the target flows carry multiple marketing tags, and the marketing tags are used to indicate strategy statistical dimensions; Acquire analysis requirement information, input the marketing tag and the analysis requirement information into a preset NLP model, and obtain a hierarchical set corresponding to each of the marketing tags output by the NLP model, wherein the analysis requirement information records a plurality of candidate levels, and the hierarchical set includes the target traffic corresponding to each of the candidate levels; Based on any of the target traffic, determine a target level for each of the marketing tags based on the classification set, and generate a traffic tag based on each set of the target levels and the corresponding marketing tags; Determine the traffic quantity 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 quantities.

2. The marketing strategy evaluation method based on traffic labels according to claim 1 is characterized in that: The marketing system is preset with multiple candidate marketing strategies. The marketing system is in communication with the user terminal to obtain multiple target flows generated based on the target marketing strategies, including: Constructing a strategy traffic entry on the user terminal based on any of the candidate marketing strategies, wherein the strategy traffic entry is used to indicate the candidate marketing strategy; Determine the user traffic generated based on any of the strategy traffic entrances as candidate traffic and associate it with the corresponding candidate marketing strategy; Constructing a visualization interface to display each of the candidate marketing strategies in the visualization interface; In response to a target marketing strategy selected from a plurality of candidate marketing strategies, the candidate traffic corresponding to the target marketing strategy is determined as the target traffic.

3. The marketing strategy evaluation method based on traffic labels according to claim 2 is characterized in that: After constructing a strategic traffic entry on the user terminal based on any of the candidate marketing strategies, the method further includes: Determining the candidate traffic carrying order information as conversion traffic, wherein the order information records the order amount and discount amount; Based on any of the strategic traffic entrances, the strategic activity is determined based on the ratio of the conversion traffic and the candidate traffic, and based on the order information of all the conversion traffic, the traffic cost of each candidate traffic and the marketing indicators of the strategic traffic entrance are determined, wherein the marketing indicators are used to indicate the strategic yield and the strategy conversion rate, and the traffic cost, the strategic activity and the marketing indicators are used to determine the evaluation results.

4. The marketing strategy evaluation method based on traffic labels according to claim 2 is characterized in that: After determining the flow quantity of the target flow corresponding to each of the flow labels, the method further includes: Generating sub-options of the target marketing strategy in the visual interface, wherein the sub-options include each of the traffic tags; Generating a plurality of distribution curves of the target marketing strategy in the visualization interface, wherein each distribution curve corresponds to one of the marketing tags, the ordinate of the distribution curve is the traffic quantity, and the abscissa is the candidate level of each of the marketing tags; In response to any of the flow labels in the lower-level options being selected, a curve segment corresponding to the selected flow label is highlighted in the distribution curve.

5. The marketing strategy evaluation method based on traffic labels according to claim 2 is characterized in that: Obtaining a hierarchical set corresponding to each of the marketing tags output by the NLP model, including: Identifying, by the NLP model, the level threshold corresponding to each of the candidate levels from the analysis requirement information, and determining the marketing tag associated with each of the candidate levels; Based on any of the marketing tags, the candidate level corresponding to each target flow is determined according to the level threshold and the corresponding level set is constructed.

6. The marketing strategy evaluation method based on traffic labels according to claim 5 is characterized in that: The analysis requirement information is composed of a plurality of sentence description information, and the level threshold corresponding to each candidate level is identified from the analysis requirement information by the NLP model, including: Determining valid tags and invalid tags from the plurality of marketing tags using the NLP model, 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; Determining the sentence description information having at least one valid tag as first description information, and determining the remaining sentence description information as second description information; determining the first description information associated with each piece of the second description information; Each candidate grade and the corresponding grade threshold identified from the second description information are applied to the valid label recorded in the associated first description information.

7. The marketing strategy evaluation method based on traffic labels according to claim 1 is characterized in that: Generating a traffic tag based on each set of the target levels and the corresponding marketing tags includes: Determining the target level corresponding to each of the marketing tags; The target level and the marketing tag are concatenated into the traffic tag.

8. A marketing strategy evaluation device based on traffic labels, characterized in that: comprising at least one control processor and a memory for communicatively coupling with the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the marketing strategy evaluation method based on traffic labels as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: Including the marketing strategy evaluation device based on traffic labels as described in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the marketing strategy evaluation method based on traffic labels as described in any one of claims 1 to 7.

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