A real-time monitoring system for marketing activities of a stand reduction gold

Abstract

The application provides a real-time monitoring system for the effect of a marketing activity with a discount, which collects user behavior data in the marketing activity with a discount, divides and processes the user behavior data according to multi-dimensional indexes, obtains target behavior data corresponding to each dimension, analyzes the target behavior data based on a preset effect monitoring mode of each dimension, obtains an index value, analyzes the collected and processed data in a timely manner through the preset effect monitoring mode, obtains the index value, realizes quantitative monitoring of the effect of the marketing activity, constructs an effect monitoring graph based on the correlation between the multi-dimensional indexes and in combination with the index value, marks key nodes in the effect monitoring graph, establishes an automatic alarm threshold mechanism for the key nodes based on an expected effect of the activity, realizes timely discovery of the trend of the marketing activity with a discount, facilitates subsequent optimization based on the effect of the marketing activity, realizes accurate delivery of the discount, and improves the return on investment of the merchant.

Description

Technical Field

[0001] The present invention relates to the technical field of instant discounts, and in particular to a real-time monitoring system for the effects of instant discount marketing activities. Background Art

[0002] Various industries, such as e-commerce and financial payment, are facing fierce homogeneous competition. They need to stimulate user decisions through instant discounts to grab users. Consumers are more inclined to purchase in a cost-effective manner and are easily impressed by price-sensitive decisions. Therefore, instant discounts play an important role in brand exposure and dissemination, stimulating instant conversions, and improving user stickiness.

[0003] In the instant discount marketing activities, monitoring the effectiveness of the instant discount marketing activities helps to quantify the degree of achievement of the activity goals, optimize the efficiency of resource allocation, and gain insights into user behavior and preferences, so as to better achieve the precise allocation of instant discounts and improve the merchant's activity return rate.

[0004] Therefore, how to conduct real-time monitoring of the effectiveness of instant discount marketing activities is an important part of the instant discount marketing activities. Summary of the Invention

[0005] The present invention provides a real-time monitoring system for the effect of an instant discount marketing activity, so as to solve the problems raised in the background technology.

[0006] A real-time monitoring system for the effect of an instant discount marketing activity, comprising:

[0007] A data processing module is used to collect user behavior data from instant discount marketing activities, and to divide and process the user behavior data according to multi-dimensional indicators to obtain target behavior data corresponding to each dimensional indicator;

[0008] The indicator determination module is used to analyze the effect of the target behavior data based on the preset effect monitoring method of each dimension indicator to obtain the indicator value;

[0009] A graph building module is used to build an effect monitoring graph based on the correlation between multi-dimensional indicators and the indicator values, and mark key nodes in the effect monitoring graph;

[0010] The overall monitoring module is used to establish an automatic alarm threshold mechanism for the key nodes based on the expected effects of the activities.

[0011] Preferably, the data processing module includes:

[0012] a determining unit, configured to determine a user operation event based on the instant discount marketing campaign, and determine a trigger code corresponding to the user operation event based on an internal code of the instant discount marketing campaign;

[0013] an associating unit, configured to associate the user operation event with the trigger code to obtain associated information;

[0014] A collection unit, configured to collect user behavior data in the instant discount marketing activity based on the associated information;

[0015] A partitioning unit, configured to determine relevant keywords under each dimensional indicator based on the attributes of the multi-dimensional indicators, and partition the user behavior data based on the relevant keywords to obtain initial behavior data corresponding to each dimensional indicator;

[0016] The processing unit is used to process the initial behavior data based on the indicator characteristics of each dimensional indicator and the data characteristics of the initial behavior data to obtain target behavior data.

[0017] Preferably, the processing unit includes:

[0018] An analysis unit, configured to perform attribute analysis on each dimension indicator to obtain multiple indicator features, and perform attribute analysis on the initial behavior data to obtain multiple data features;

[0019] A hierarchy determination unit is configured to establish an attribute relationship distribution diagram based on the specific application of the attribute in the dimensional indicator, and hierarchically arrange the multiple indicator features according to the attribute relationship distribution diagram to obtain an indicator hierarchy;

[0020] A data stratification unit is used to match the multiple indicator features with the data features based on semantic association to obtain a matching result, and to stratify the initial behavior data in combination with the indicator hierarchy to obtain a behavior data hierarchy;

[0021] A layer marking unit, configured to obtain data content associations and user associations between data layers, perform content marking on the behavior data layer based on the data content associations, and perform user marking on the behavior data layer based on the user associations;

[0022] a tag analysis unit, configured to perform a behavior path analysis on the behavior data hierarchy based on content tags to obtain path sequence data, and to perform a cluster analysis on the behavior data hierarchy based on user tags to obtain user grouping data;

[0023] A hierarchy optimization unit, configured to optimize the behavior data hierarchy using the path sequence data and user grouping data to obtain a target data hierarchy;

[0024] The data processing unit is used to process the initial behavior data according to the target data level to obtain target behavior data.

[0025] Preferably, the hierarchical optimization unit includes:

[0026] a path analysis unit, configured to perform secondary association on adjacent level data of the behavior data level based on the path sequence data, thereby deepening the association between adjacent levels;

[0027] The user analysis unit is used to perform user association on all levels of the behavior data layer based on the user grouping data, and to increase user association at the behavior data layer.

[0028] Preferably, the indicator determination module includes:

[0029] A channel establishment unit is used to establish an association feature between each dimensional indicator and a corresponding preset effect monitoring method, and to establish a data transmission channel based on the association feature;

[0030] An effect analysis unit, configured to transmit the target behavior data to a corresponding preset effect monitoring method based on a data transmission channel for effect analysis;

[0031] The display unit is used to display the indicator value of each dimension indicator based on the effect analysis result.

[0032] Preferably, the map creation module includes:

[0033] An element determination unit is used to define relationships based on multi-dimensional indicators, define nodes based on indicator values, and determine graph elements based on relationship definitions and node definitions;

[0034] An indicator analysis unit is used to obtain the correlation relationship between the corresponding indicator attributes under the multi-dimensional indicators, determine the correlation degree between the multi-dimensional indicators based on the correlation relationship, and determine the criticality of each dimensional indicator based on the number of indicators and the frequency of occurrence of indicators under each dimensional indicator;

[0035] A position determination unit, configured to determine the overall importance of each dimensional indicator based on the criticality and relevance, and determine the atlas position of the atlas element determined by each dimensional indicator based on the overall importance of all dimensional indicators;

[0036] a structure determination unit, configured to perform a preliminary comparison between the index value and a preset index value to obtain an index value difference, determine the importance of each index value based on the index value difference, and determine the degree of local structure refinement of the atlas element determined by each index based on the importance;

[0037] A map establishment unit, configured to establish an initial effect map based on the map elements, combined with the map position and the degree of local structure refinement;

[0038] A horizontal optimization unit is used to determine a horizontal comparison index from the initial effect map, determine effect difference information under the horizontal comparison index, and optimize the initial effect map based on the horizontal comparison index and its corresponding effect difference information to obtain a final effect monitoring map;

[0039] The marking unit is used to determine and mark key nodes from the effect monitoring map based on the frequency of indicator occurrence and the size of the indicator value.

[0040] Preferably, the lateral optimization unit includes:

[0041] A dynamic analysis unit, configured to set a horizontal dynamic indicator based on effect difference information under the horizontal comparison indicator, and determine the dynamic effect under the horizontal dynamic indicator;

[0042] The optimization unit is used to dynamically optimize the initial effect map based on the horizontal dynamic indicators and dynamic effects to obtain an effect monitoring map.

[0043] Preferably, the marking unit comprises:

[0044] The weight unit is used to set the frequency weight of the indicator's occurrence frequency based on the effect requirements, and the value weight based on the size of the indicator value;

[0045] The node determination unit is used to determine the node evaluation value based on the product of the frequency weight and the indicator occurrence frequency, and the product of the indicator value and the value weight, and select the node with a node evaluation value greater than the preset evaluation value as the key node.

[0046] Preferably, the overall monitoring module includes:

[0047] A threshold determination unit is used to determine the alarm threshold at the key node based on the expected effect of the activity and the attributes of the aggregated nodes;

[0048] The mechanism establishing unit is used to establish an automatic alarm threshold mechanism based on the alarm threshold at the key node.

[0049] Preferably, the mechanism establishing unit includes:

[0050] A method determination unit, configured to establish a correspondence between node characteristics of key nodes and alarm thresholds, and to establish an automatic alarm threshold determination method based on the correspondence;

[0051] The embedding unit embeds the automatic alarm threshold judgment method into a preset alarm template to obtain an automatic alarm threshold mechanism.

[0052] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0053] By collecting user behavior data from the instant discount marketing activity and dividing and processing the user behavior data according to multi-dimensional indicators, target behavior data corresponding to each dimension is obtained, and accurate division and processing of behavior data based on dimensional indicators is achieved. From the perspective of data quality, the real-time and accuracy of marketing activity effect monitoring are improved, which facilitates subsequent optimization based on the marketing activity effect. The target behavior data is analyzed based on the preset effect monitoring method for each dimension to obtain indicator values. The collected and processed data is timely analyzed using the preset effect monitoring method to obtain indicator values, thereby achieving quantitative monitoring of the marketing activity effect. Based on the correlation between the multi-dimensional indicators and the indicator values, an effect monitoring map is constructed, and key nodes are marked in the effect monitoring map. By constructing the effect monitoring map, an overall display and analysis of the effect of the instant discount marketing activity is achieved. Based on the expected effect of the activity, an automatic alarm threshold mechanism is established for the key nodes, so that the trend of the instant discount marketing activity can be discovered in a timely manner, which facilitates subsequent optimization based on the marketing activity effect. Ultimately, the precise delivery of instant discounts is better achieved, and the merchant's activity return rate is improved.

[0054] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 This is a structural diagram of a real-time monitoring system for the effect of an instant discount marketing activity in an embodiment of the present invention;

[0058] Figure 2 This is a structural diagram of the indicator determination module described in an embodiment of the present invention;

[0059] Figure 3 2 is a structural diagram of the overall detection module in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] Example 1:

[0062] The embodiment of the present invention provides a real-time monitoring system for the effect of instant discount marketing activities, such as Figure 1 Shown, including:

[0063] A data processing module is used to collect user behavior data from instant discount marketing activities, and to divide and process the user behavior data according to multi-dimensional indicators to obtain target behavior data corresponding to each dimensional indicator;

[0064] The indicator determination module is used to analyze the effect of the target behavior data based on the preset effect monitoring method of each dimension indicator to obtain the indicator value;

[0065] A graph building module is used to build an effect monitoring graph based on the correlation between multi-dimensional indicators and the indicator values, and mark key nodes in the effect monitoring graph;

[0066] The overall monitoring module is used to establish an automatic alarm threshold mechanism for the key nodes based on the expected effects of the activities.

[0067] In this embodiment, the user behavior data includes operation data in the instant discount marketing activity, such as receiving the instant discount, selecting the instant discount, payment operations, etc.

[0068] In this embodiment, the multi-dimensional indicators include the collection and verification of instant discounts, traffic conversion, cost investment and profit return, and the proportion of payment failures caused by users staying on the page, for example, the user exits the payment when selecting the payment method, resulting in a failed transaction.

[0069] In this embodiment, the indicator values ​​include, for example, the number of instant discounts received, the instant discount redemption rate, the browsing traffic of the instant discount activity, the profit trend, etc.

[0070] In this embodiment, the preset effect monitoring method for each dimension is, for example, tracking user paths, overall analysis of multi-platform channel data, etc.

[0071] In this embodiment, the correlation between the multi-dimensional indicators is, for example, the relationship between the user's page pause resulting in payment failure affecting the verification data, the relationship between browsing traffic and profits of the deduction activity, etc.

[0072] In this embodiment, user behavior data in the instant discount marketing activity is collected, and these data are obtained with the user's authorization.

[0073] The beneficial effects of the above design scheme are as follows: by collecting user behavior data in the instant discount marketing activity and dividing and processing the user behavior data according to multi-dimensional indicators, the target behavior data corresponding to each dimension is obtained, and the accurate division and processing of the behavior data based on the dimensional indicators is achieved, thereby improving the real-time and accuracy of the monitoring of the marketing activity effect from the perspective of data quality, facilitating subsequent optimization based on the marketing activity effect, performing effect analysis on the target behavior data based on the preset effect monitoring method for each dimension to obtain the indicator value, and timely analyzing the collected and processed data through the preset effect monitoring method to obtain the indicator value, thereby achieving quantitative monitoring of the marketing activity effect, based on the correlation between the multi-dimensional indicators and the indicator values, constructing an effect monitoring map, and marking key nodes in the effect monitoring map, and realizing the overall display and analysis of the effect of the instant discount marketing activity by constructing the effect monitoring map, and establishing an automatic alarm threshold mechanism for the key nodes based on the expected effect of the activity, thereby realizing timely discovery of the trend of the instant discount marketing activity, facilitating subsequent optimization based on the marketing activity effect, and ultimately better achieving the precise delivery of instant discounts and improving the activity return rate of merchants.

[0074] Example 2:

[0075] Based on Example 1, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the data processing module includes:

[0076] a determining unit, configured to determine a user operation event based on the instant discount marketing campaign, and determine a trigger code corresponding to the user operation event based on an internal code of the instant discount marketing campaign;

[0077] an associating unit, configured to associate the user operation event with the trigger code to obtain associated information;

[0078] A collection unit, configured to collect user behavior data in the instant discount marketing activity based on the associated information;

[0079] A partitioning unit, configured to determine relevant keywords under each dimensional indicator based on the attributes of the multi-dimensional indicators, and partition the user behavior data based on the relevant keywords to obtain initial behavior data corresponding to each dimensional indicator;

[0080] The processing unit is used to process the initial behavior data based on the indicator characteristics of each dimensional indicator and the data characteristics of the initial behavior data to obtain target behavior data.

[0081] In this embodiment, the relevant keywords under each dimensional indicator, for example, the relevant keywords for the collection and cancellation of instant discounts are collection status, cancellation status, etc., and the traffic conversion status is the browsing status of the instant discount marketing activities, the activity participation status, etc.

[0082] The beneficial effects of the above design scheme are: by determining the user operation event based on the instant discount marketing activity, and determining the trigger code corresponding to the user operation event based on the internal code of the instant discount marketing activity, the user operation event is associated with the trigger code to obtain associated information, which provides a basis for determining and obtaining user behavior data; based on the attributes of multi-dimensional indicators, the relevant keywords under each dimensional indicator are determined; based on the relevant keywords, the user behavior data is divided to obtain the initial behavior data corresponding to each dimensional indicator, and the data division is realized to provide a basis for effective data processing; based on the indicator characteristics of each dimensional indicator and the data characteristics of the initial behavior data, the initial behavior data is processed to obtain the target behavior data, and accurate division and processing of behavior data based on dimensional indicators is realized, which improves the real-time and accuracy of marketing activity effect monitoring from the aspect of data quality, and facilitates subsequent optimization based on the effect of marketing activities.

[0083] Example 3:

[0084] Based on Example 2, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the processing unit includes:

[0085] An analysis unit, configured to perform attribute analysis on each dimension indicator to obtain multiple indicator features, and perform attribute analysis on the initial behavior data to obtain multiple data features;

[0086] A hierarchy determination unit is configured to establish an attribute relationship distribution diagram based on the specific application of the attribute in the dimensional indicator, and hierarchically arrange the multiple indicator features according to the attribute relationship distribution diagram to obtain an indicator hierarchy;

[0087] A data stratification unit is used to match the multiple indicator features with the data features based on semantic association to obtain a matching result, and to stratify the initial behavior data in combination with the indicator hierarchy to obtain a behavior data hierarchy;

[0088] A layer marking unit, configured to obtain data content associations and user associations between data layers, perform content marking on the behavior data layer based on the data content associations, and perform user marking on the behavior data layer based on the user associations;

[0089] a tag analysis unit, configured to perform a behavior path analysis on the behavior data hierarchy based on content tags to obtain path sequence data, and to perform a cluster analysis on the behavior data hierarchy based on user tags to obtain user grouping data;

[0090] A hierarchy optimization unit, configured to optimize the behavior data hierarchy using the path sequence data and user grouping data to obtain a target data hierarchy;

[0091] The data processing unit is used to process the initial behavior data according to the target data level to obtain target behavior data.

[0092] In this embodiment, the attributes of the indicator include user attributes, activity attributes, environmental attributes, etc., and the corresponding user behavior data include age, gender, region, etc., as well as the type and denomination of instant discounts, as well as the access platform, access time, etc.

[0093] In this embodiment, the specific application of attributes in the dimensional indicators, such as user attributes as the basic application at the bottom layer and activity attributes as the key application, occupy multiple levels.

[0094] In this embodiment, the indicator hierarchy determines the correlation between various indicators under the dimensional indicators, which is used to clarify the overall idea under the dimensional indicators.

[0095] In this embodiment, the behavior data hierarchy realizes the logical division of the initial behavior data, making the data more organized.

[0096] In this embodiment, the behavioral path analysis of the behavioral data level based on the content tag clarifies the user's behavioral sequence in the entire activity, which facilitates the analysis of subsequent effects.

[0097] In this embodiment, cluster analysis of the behavioral data hierarchy based on user tags clarifies the overall trajectory of users, enables user grouping, and provides a basis for precision marketing.

[0098] The beneficial effects of the above design scheme are: multiple indicator features are obtained by performing attribute analysis on each dimensional indicator, multiple data features are obtained by performing attribute analysis on the initial behavior data, matching and stratification are performed based on the indicator features and data features, and the data content association and user association between data levels are considered in the stratification process to finally obtain the target data level, and the initial behavior data is processed according to the target data level to obtain the target behavior data, thereby ensuring the logic and orderliness of the obtained target behavior data, ensuring the quality of the target behavior data, and providing a basis for effect analysis.

[0099] Example 4:

[0100] Based on Example 3, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the hierarchical optimization unit includes:

[0101] a path analysis unit, configured to perform secondary association on adjacent level data of the behavior data level based on the path sequence data, thereby deepening the association between adjacent levels;

[0102] The user analysis unit is used to perform user association on all levels of the behavior data layer based on the user grouping data, and to increase user association at the behavior data layer.

[0103] In this embodiment, after deepening the association between adjacent levels and increasing the user association of the behavior data level, the target data level is obtained.

[0104] The beneficial effects of the above design scheme are: by performing secondary association on the adjacent level data of the behavior data level based on the path sequence data, the association between adjacent levels is deepened, and user association is performed on all level data of the behavior data level based on the user grouping data, thereby increasing the user association of the behavior data level, ensuring the accuracy of the target data level, realizing effective logical analysis and processing of the behavior data, and providing a basis for effect analysis.

[0105] Example 5:

[0106] Based on Example 1, the present invention provides a real-time monitoring system for the effect of instant discount marketing activities, such as Figure 2 As shown, the indicator determination module includes:

[0107] A channel establishment unit is used to establish an association feature between each dimensional indicator and a corresponding preset effect monitoring method, and to establish a data transmission channel based on the association feature;

[0108] An effect analysis unit, configured to transmit the target behavior data to a corresponding preset effect monitoring method based on a data transmission channel for effect analysis;

[0109] The display unit is used to display the indicator value of each dimension indicator based on the effect analysis result.

[0110] The beneficial effects of the above design scheme are: by establishing the association characteristics between each dimensional indicator and the corresponding preset effect monitoring method, and establishing a data transmission channel based on the said association characteristics, the accurate transmission of target behavior data is achieved, and the target behavior data is transmitted to the corresponding preset effect monitoring method based on the data transmission channel for effect analysis. Based on the effect analysis results, the indicator value of each dimensional indicator is displayed to achieve quantitative monitoring of the effect of marketing activities.

[0111] Example 6:

[0112] Based on Example 1, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the graph building module includes:

[0113] An element determination unit is used to define relationships based on multi-dimensional indicators, define nodes based on indicator values, and determine graph elements based on relationship definitions and node definitions;

[0114] An indicator analysis unit is used to obtain the correlation relationship between the corresponding indicator attributes under the multi-dimensional indicators, determine the correlation degree between the multi-dimensional indicators based on the correlation relationship, and determine the criticality of each dimensional indicator based on the number of indicators and the frequency of occurrence of indicators under each dimensional indicator;

[0115] A position determination unit, configured to determine the overall importance of each dimensional indicator based on the criticality and relevance, and determine the atlas position of the atlas element determined by each dimensional indicator based on the overall importance of all dimensional indicators;

[0116] a structure determination unit, configured to perform a preliminary comparison between the index value and a preset index value to obtain an index value difference, determine the importance of each index value based on the index value difference, and determine the degree of local structure refinement of the atlas element determined by each index based on the importance;

[0117] A map establishment unit, configured to establish an initial effect map based on the map elements, combined with the map position and the degree of local structure refinement;

[0118] A horizontal optimization unit is used to determine a horizontal comparison index from the initial effect map, determine effect difference information under the horizontal comparison index, and optimize the initial effect map based on the horizontal comparison index and its corresponding effect difference information to obtain a final effect monitoring map;

[0119] The marking unit is used to determine and mark key nodes from the effect monitoring map based on the frequency of indicator occurrence and the size of the indicator value.

[0120] In this embodiment, the relationships of the multi-dimensional indicators are defined as edges of the graph, and the nodes of the indicators are defined as nodes of the graph.

[0121] In this embodiment, the greater the number of indicators and the frequency of occurrence of the indicators, the greater the corresponding criticality, and the closer the position in the graph is to the center.

[0122] In this embodiment, the specific value of the local structure refinement degree of the atlas element is used to refine the indicator, for example, the refinement of the write-off rate for different time periods. The higher the importance, the greater the corresponding refinement degree.

[0123] In this embodiment, the horizontal comparison indicators include, for example, the effect of the corresponding redemption rate under different denominations of instant discounts, the effect of the corresponding collection rate under different full-discount rules, and the effect of setting instant discounts and redemption with different validity periods.

[0124] The beneficial effects of the above design scheme are: by defining relationships based on multi-dimensional indicators, defining nodes based on indicator values, determining map elements based on relationship definitions and node definitions, considering the characteristics of the elements themselves and between elements to construct an initial effect map, and optimizing the initial effect map based on horizontal comparison indicators and their corresponding effect difference information to obtain the final effect monitoring map, determining key nodes from the effect monitoring map based on the frequency of indicator occurrence and the size of the indicator value and marking them, and realizing the overall display and analysis of the effects of the opposing gold reduction marketing activities by constructing an effect monitoring map, and ensuring the focus of the effect display to facilitate the review of relevant personnel.

[0125] Example 7:

[0126] Based on Example 6, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the horizontal optimization unit includes:

[0127] A dynamic analysis unit, configured to set a horizontal dynamic indicator based on effect difference information under the horizontal comparison indicator, and determine the dynamic effect under the horizontal dynamic indicator;

[0128] The optimization unit is used to dynamically optimize the initial effect map based on the horizontal dynamic indicators and dynamic effects to obtain an effect monitoring map.

[0129] The beneficial effects of the above design scheme are: by setting horizontal dynamic indicators based on the effect difference information under the horizontal comparison indicators, and determining the dynamic effects under the horizontal dynamic indicators, the initial effect map is dynamically optimized based on the horizontal dynamic indicators and dynamic effects, and an effect monitoring map is obtained to realize the dynamic nature of the map, facilitate the display of map information, and achieve a multi-faceted display of marketing effects.

[0130] Example 8:

[0131] Based on Example 6, this embodiment of the present invention provides a real-time monitoring system for the effectiveness of an instant discount marketing activity, wherein the marking unit includes:

[0132] The weight unit is used to set the frequency weight of the indicator's occurrence frequency based on the effect requirements, and the value weight based on the size of the indicator value;

[0133] The node determination unit is used to determine the node evaluation value based on the product of the frequency weight and the indicator occurrence frequency, and the product of the indicator value and the value weight, and select the node with a node evaluation value greater than the preset evaluation value as the key node.

[0134] The beneficial effects of the above design scheme are: based on the effect demand, the frequency weight of the index appearance frequency is set, the value weight based on the index value size is set, the node evaluation value is determined based on the product of the frequency weight and the index appearance frequency and the product of the index value size and the value weight, the node with the node evaluation value greater than the preset evaluation value is selected as the key node, and the basis for timely early warning of effect monitoring is provided.

[0135] Embodiment 9:

[0136] Based on the basis of embodiment 1, the embodiment of the application provides a real-time monitoring system for marketing activity effect of a reduction in gold, as shown in Figure 3 The whole monitoring module comprises:

[0137] The threshold determination unit is configured to determine the alarm threshold at the key node based on the expected effect of the activity and the node attributes.

[0138] The mechanism establishment unit is configured to establish an automatic alarm threshold mechanism based on the alarm threshold at the key node.

[0139] The beneficial effects of the above design scheme are: based on the expected effect of the activity and the node attributes, the alarm threshold at the key node is determined, the automatic alarm threshold mechanism is established based on the alarm threshold at the key node, the trend of the marketing activity of the reduction in gold is discovered in time, the subsequent optimization based on the marketing activity effect is facilitated, and finally, the accurate delivery of the reduction in gold is better achieved, and the activity return rate of the merchant is improved.

[0140] Embodiment 10:

[0141] Based on the basis of embodiment 9, the embodiment of the application provides a real-time monitoring system for marketing activity effect of a reduction in gold, and the mechanism establishment unit comprises:

[0142] The method determination unit is configured to establish a corresponding relationship between the node characteristics of the key node and the alarm threshold, and establish an automatic alarm threshold judgment method based on the corresponding relationship.

[0143] The embedding unit is configured to embed the automatic alarm threshold judgment method into a preset alarm template to obtain an automatic alarm threshold mechanism.

[0144] The beneficial effects of the above design scheme are: by establishing a corresponding relationship between the node characteristics of the key node and the alarm threshold, and establishing an automatic alarm threshold judgment method based on the corresponding relationship, the automatic alarm threshold judgment method is embedded into a preset alarm template to obtain an automatic alarm threshold mechanism, and the establishment of the mechanism is achieved, thereby providing a basis for timely discovery of the marketing activity effect.

[0145] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of this application document and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A real-time monitoring system for the effect of instant discount marketing activities, characterized in that: include: The data processing module is used to collect user behavior data from the instant discount marketing activities, and divide and process the user behavior data according to multi-dimensional indicators to obtain the target behavior data corresponding to each dimensional indicator, including: A partitioning unit, configured to determine relevant keywords under each dimensional indicator based on the attributes of the multi-dimensional indicators, and partition the user behavior data based on the relevant keywords to obtain initial behavior data corresponding to each dimensional indicator; The processing unit is used to process the initial behavior data based on the indicator characteristics of each dimension indicator and the data characteristics of the initial behavior data to obtain target behavior data, including: An analysis unit, configured to perform attribute analysis on each dimension indicator to obtain multiple indicator features, and perform attribute analysis on the initial behavior data to obtain multiple data features; A hierarchy determination unit is configured to establish an attribute relationship distribution diagram based on the specific application of the attribute in the dimensional indicator, and hierarchically arrange the multiple indicator features according to the attribute relationship distribution diagram to obtain an indicator hierarchy; A data stratification unit is used to match the multiple indicator features with the data features based on semantic association to obtain a matching result, and to stratify the initial behavior data in combination with the indicator hierarchy to obtain a behavior data hierarchy; A layer marking unit, configured to obtain data content associations and user associations between data layers, perform content marking on the behavior data layer based on the data content associations, and perform user marking on the behavior data layer based on the user associations; a tag analysis unit, configured to perform a behavior path analysis on the behavior data hierarchy based on content tags to obtain path sequence data, and to perform a cluster analysis on the behavior data hierarchy based on user tags to obtain user grouping data; A hierarchy optimization unit, configured to optimize the behavior data hierarchy using the path sequence data and user grouping data to obtain a target data hierarchy; a data processing unit, configured to process the initial behavior data according to the target data hierarchy to obtain target behavior data; The indicator determination module is used to analyze the effect of the target behavior data based on the preset effect monitoring method of each dimension indicator to obtain the indicator value; A graph building module is used to build an effect monitoring graph based on the correlation between multi-dimensional indicators and the indicator values, and mark key nodes in the effect monitoring graph; The overall monitoring module is used to establish an automatic alarm threshold mechanism for the key nodes based on the expected effects of the activities.

2. A real-time monitoring system for the effect of instant discount marketing activities according to claim 1, characterized in that: The data processing module further includes: a determining unit, configured to determine a user operation event based on the instant discount marketing campaign, and determine a trigger code corresponding to the user operation event based on an internal code of the instant discount marketing campaign; an associating unit, configured to associate the user operation event with the trigger code to obtain associated information; The collection unit is used to collect user behavior data in the instant discount marketing activity based on the associated information.

3. The real-time monitoring system for the effect of instant discount marketing activities according to claim 1 is characterized in that: The hierarchical optimization unit includes: a path analysis unit, configured to perform secondary association on adjacent level data of the behavior data level based on the path sequence data, thereby deepening the association between adjacent levels; The user analysis unit is used to perform user association on all levels of the behavior data layer based on the user grouping data, and to increase user association at the behavior data layer.

4. The real-time monitoring system for the effect of instant discount marketing activities according to claim 1 is characterized in that: The indicator determination module includes: A channel establishment unit is used to establish an association feature between each dimensional indicator and a corresponding preset effect monitoring method, and to establish a data transmission channel based on the association feature; An effect analysis unit, configured to transmit the target behavior data to a corresponding preset effect monitoring method based on a data transmission channel for effect analysis; The display unit is used to display the indicator value of each dimension indicator based on the effect analysis result.

5. The real-time monitoring system for the effect of instant discount marketing activities according to claim 1 is characterized in that: The map creation module includes: An element determination unit is used to define relationships based on multi-dimensional indicators, define nodes based on indicator values, and determine graph elements based on relationship definitions and node definitions; An indicator analysis unit is used to obtain the correlation relationship between the corresponding indicator attributes under the multi-dimensional indicators, determine the correlation degree between the multi-dimensional indicators based on the correlation relationship, and determine the criticality of each dimensional indicator based on the number of indicators and the frequency of occurrence of indicators under each dimensional indicator; A position determination unit, configured to determine the overall importance of each dimensional indicator based on the criticality and relevance, and determine the atlas position of the atlas element determined by each dimensional indicator based on the overall importance of all dimensional indicators; a structure determination unit, configured to perform a preliminary comparison between the index value and a preset index value to obtain an index value difference, determine the importance of each index value based on the index value difference, and determine the degree of local structure refinement of the atlas element determined by each index based on the importance; A map establishment unit, configured to establish an initial effect map based on the map elements, combined with the map position and the degree of local structure refinement; A horizontal optimization unit is used to determine a horizontal comparison index from the initial effect map, determine effect difference information under the horizontal comparison index, and optimize the initial effect map based on the horizontal comparison index and its corresponding effect difference information to obtain a final effect monitoring map; The marking unit is used to determine and mark key nodes from the effect monitoring map based on the frequency of indicator occurrence and the size of the indicator value.

6. A real-time monitoring system for the effect of instant discount marketing activities according to claim 5, characterized in that: The lateral optimization unit comprises: A dynamic analysis unit, configured to set a horizontal dynamic indicator based on effect difference information under the horizontal comparison indicator, and determine the dynamic effect under the horizontal dynamic indicator; The optimization unit is used to dynamically optimize the initial effect map based on the horizontal dynamic indicators and dynamic effects to obtain an effect monitoring map.

7. The real-time monitoring system for the effect of instant discount marketing activities according to claim 5 is characterized in that: The marking unit comprises: The weight unit is used to set the frequency weight of the indicator's occurrence frequency based on the effect requirements, and the value weight based on the size of the indicator value; The node determination unit is used to determine the node evaluation value based on the product of the frequency weight and the indicator occurrence frequency, and the product of the indicator value and the value weight, and select the node with a node evaluation value greater than the preset evaluation value as the key node.

8. The real-time monitoring system for the effect of instant discount marketing activities according to claim 1 is characterized in that: The overall monitoring module includes: A threshold determination unit is used to determine the alarm threshold at the key node based on the expected effect of the activity and the attributes of the aggregated nodes; The mechanism establishing unit is used to establish an automatic alarm threshold mechanism based on the alarm threshold at the key node.

9. The real-time monitoring system for the effect of instant discount marketing activities according to claim 8 is characterized in that: The mechanism establishment unit includes: A method determination unit, configured to establish a correspondence between node characteristics of key nodes and alarm thresholds, and to establish an automatic alarm threshold determination method based on the correspondence; The embedding unit embeds the automatic alarm threshold judgment method into a preset alarm template to obtain an automatic alarm threshold mechanism.

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