Marketing activity management method and device

Abstract

The invention discloses a marketing activity management method and device, relates to the technical field of big data and can also be used in the financial field, and the method comprises the steps: collecting the use behavior data of a user in a mobile banking APP; according to the use behavior data of each user, determining a hot word tag of the user and related marketing activities; determining the matching degree between each hot word tag and each marketing activity according to the hot word tag of each user and the involved marketing activity, and for each hot word tag, counting the number of the users with the hot word tag; determining the weight of each marketing activity according to the matching degree of each marketing activity and the hot word tag and the number of the users with the hot word tag; according to the weight of each marketing activity and the historical marketing effect data of each marketing activity, determining a comprehensive score of each marketing activity; and according to the comprehensive score of each marketing activity, carrying out on-shelf processing on the at least one marketing activity.

Description

Technical Field

[0001] The present invention relates to the field of big data technology and can also be used in the financial field, and in particular to a marketing activity management method and device. Background Art

[0002] With the development of Internet technology and the popularization of e-commerce and mobile payments, online promotional activities have become the mainstream form, and the promotion of online activities conforms to the development of the times.

[0003] The current mobile banking activity calendar uses an advertising system to publish marketing activity data, which is synchronized to the activity calendar management platform. Business operation and maintenance personnel use the activity calendar management platform to screen target activities and publish them to the activity calendar. The mobile banking activity calendar function obtains published and reviewed activities and displays the activity content for customers to use.

[0004] However, this method of screening and reviewing by business operation and maintenance personnel is inefficient and may also result in errors or omissions, leading to poor marketing campaign management results.

[0005] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No description herein is admitted to be prior art by inclusion in this section. Summary of the invention

[0006] An embodiment of the present invention provides a marketing activity management method for improving the management efficiency and management effect of marketing activities. The method includes:

[0007] Collect user behavior data within the mobile banking app;

[0008] Determine the user's hot word tags and the marketing activities involved based on each user's usage behavior data;

[0009] According to the hot word tags of each user and the marketing activities involved, determine the matching degree between each hot word tag and each marketing activity, and, for each hot word tag, count the number of users having the hot word tag;

[0010] Determine the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag;

[0011] Determine the comprehensive score of each marketing campaign based on the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign;

[0012] According to the comprehensive score of each marketing campaign, at least one of the marketing campaigns is put on the shelves.

[0013] In some embodiments, the collecting of the user's usage behavior data within the mobile banking APP includes:

[0014] The user usage behavior to be tracked is defined using the configuration interface or configuration file provided by the tracking tool integrated in the front-end code of the mobile banking APP. The user usage behavior includes at least one of the following: click behavior, page browsing behavior, search behavior, and transaction behavior;

[0015] The user's usage behavior data within the mobile banking APP is collected through the tracking tool.

[0016] In some embodiments, determining the hot word tag of each user according to the usage behavior data of the user includes:

[0017] For each user, extract text information from the user's usage behavior data;

[0018] Perform word segmentation on the extracted text information to obtain an initial word set;

[0019] Eliminate stop words from the initial word set to obtain a target word set;

[0020] Searching for each target word in the target word set in a preset keyword library;

[0021] If the target word exists in the keyword library, the frequency of occurrence of the target word in the target word set is counted;

[0022] According to the frequency of occurrence of each target word in the target word set, the hot word tag of the user is determined, wherein the user has at least one hot word tag.

[0023] In some embodiments, determining the marketing activities involved in each user based on the usage behavior data of the user includes:

[0024] Extracting data related to marketing activities from the usage behavior data of each user, wherein the data related to marketing activities include marketing activities of clicks, browsing, searches and / or transactions;

[0025] The marketing activities in which the user is involved are determined based on the extracted data related to the marketing activities.

[0026] In some embodiments, determining the weight of each marketing activity according to each user's hot word tag and the marketing activity involved includes:

[0027] According to the hot word tags of each user and the marketing activities involved, determine the matching degree between each hot word tag and each marketing activity; for each hot word tag, count the number of users with the hot word tag;

[0028] The weight of each marketing activity is determined according to the matching degree between each marketing activity and the hot word tag and the number of users having the hot word tag.

[0029] In some embodiments, determining the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved includes:

[0030] For each of the marketing activities, count the hot word tags of users involved in the marketing activity;

[0031] For each of the hot word tags, count the number of users who have the hot word tag among the users involved in the marketing activity;

[0032] According to the number of users corresponding to each hot word tag, the matching degree between the hot word tag and the marketing activity is determined.

[0033] In some embodiments, determining the comprehensive score of each marketing campaign according to the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign includes:

[0034] For each marketing campaign, obtaining historical marketing effectiveness data of the marketing campaign, wherein the historical marketing effectiveness data includes at least one of the following: click-through rate, conversion rate, and sales volume of the marketing campaign;

[0035] Calculate the historical marketing effectiveness score of the marketing activity based on the historical marketing effectiveness data;

[0036] Determine the comprehensive score of the marketing campaign based on the historical marketing effectiveness score of the marketing campaign and the weight of the marketing campaign.

[0037] In some embodiments, the step of putting at least one marketing activity on the shelves according to the comprehensive score of each marketing activity includes:

[0038] Rank the marketing activities in descending order of comprehensive scores;

[0039] The sorted marketing activities are put on the shelves so that the mobile banking APP displays the marketing activities in order according to the sorting.

[0040] In some embodiments, after at least one marketing campaign is put on the shelf according to the comprehensive score of each marketing campaign, the method further includes:

[0041] Collect data on users’ participation in the marketing activities within the mobile banking app;

[0042] Re-determine the comprehensive score of each marketing campaign based on the weight of each marketing campaign and the data of users’ participation in the marketing campaign within the mobile banking app;

[0043] According to the comprehensive score of each marketing campaign, at least one of the marketing campaigns is removed from the shelves.

[0044] An embodiment of the present invention provides a marketing activity management device for improving the management efficiency and management effect of marketing activities. The device includes:

[0045] The collection module is used to collect the user's usage behavior data within the mobile banking APP;

[0046] The first determination module is used to determine the hot word tags of each user and the marketing activities involved according to the usage behavior data of the user;

[0047] A second determination module is used to determine the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved, and, for each hot word tag, count the number of users having the hot word tag;

[0048] A third determination module is used to determine the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag;

[0049] A fourth determination module is used to determine a comprehensive score for each marketing campaign based on the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign;

[0050] The listing module is used to list at least one marketing activity according to the comprehensive score of each marketing activity.

[0051] In some embodiments, the acquisition module is specifically used for:

[0052] The user usage behavior to be tracked is defined using the configuration interface or configuration file provided by the tracking tool integrated in the front-end code of the mobile banking APP. The user usage behavior includes at least one of the following: click behavior, page browsing behavior, search behavior, and transaction behavior;

[0053] The user's usage behavior data within the mobile banking APP is collected through the tracking tool.

[0054] In some embodiments, the first determining module is specifically configured to:

[0055] For each user, extract text information from the user's usage behavior data;

[0056] Perform word segmentation on the extracted text information to obtain an initial word set;

[0057] Eliminate stop words from the initial word set to obtain a target word set;

[0058] Searching for each target word in the target word set in a preset keyword library;

[0059] If the target word exists in the keyword library, the frequency of occurrence of the target word in the target word set is counted;

[0060] According to the frequency of occurrence of each target word in the target word set, the hot word tag of the user is determined, wherein the user has at least one hot word tag.

[0061] In some embodiments, the first determining module is specifically configured to:

[0062] Extracting data related to marketing activities from the usage behavior data of each user, wherein the data related to marketing activities include marketing activities of clicks, browsing, searches and / or transactions;

[0063] The marketing activities in which the user is involved are determined based on the extracted data related to the marketing activities.

[0064] In some embodiments, the third determination module determines the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved, including:

[0065] For each of the marketing activities, count the hot word tags of users involved in the marketing activity;

[0066] For each of the hot word tags, count the number of users who have the hot word tag among the users involved in the marketing activity;

[0067] According to the number of users corresponding to each hot word tag, the matching degree between the hot word tag and the marketing activity is determined.

[0068] In some embodiments, the fourth determination module is specifically configured to:

[0069] For each marketing campaign, obtaining historical marketing effectiveness data of the marketing campaign, wherein the historical marketing effectiveness data includes at least one of the following: click-through rate, conversion rate, and sales volume of the marketing campaign;

[0070] Calculate the historical marketing effectiveness score of the marketing activity based on the historical marketing effectiveness data;

[0071] Determine the comprehensive score of the marketing campaign based on the historical marketing effectiveness score of the marketing campaign and the weight of the marketing campaign.

[0072] In some embodiments, the racking module is specifically used for:

[0073] Rank the marketing activities in descending order of comprehensive scores;

[0074] The sorted marketing activities are put on the shelves so that the mobile banking APP displays the marketing activities in order according to the sorting.

[0075] In some embodiments, the collection module is further used to: collect data of users participating in the marketing activities within the mobile banking APP;

[0076] The fourth determination module is further used to: re-determine the comprehensive score of each marketing activity according to the weight of each marketing activity and the data of the user's participation in the marketing activity in the mobile banking APP;

[0077] The device further comprises: a delisting module, which is used to delist at least one marketing activity according to the comprehensive score of each marketing activity.

[0078] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the marketing activity management method described in any of the above embodiments when executing the computer program.

[0079] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the marketing activity management method described in any of the above embodiments is implemented.

[0080] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the marketing activity management method described in any of the above embodiments is implemented.

[0081] The marketing activity management method and device provided by the embodiment of the present invention collects the usage behavior data of users in the mobile banking APP; determines the hot word tags of the user and the marketing activities involved according to the usage behavior data of each user; determines the matching degree between each hot word tag and each marketing activity according to the hot word tags of each user and the marketing activities involved, and, for each hot word tag, counts the number of users with the hot word tag; determines the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag; determines the comprehensive score of each marketing activity according to the weight of each marketing activity and the historical marketing effectiveness data of each marketing activity; and puts at least one marketing activity on the shelf according to the comprehensive score of each marketing activity. In this way, the user usage behavior data is automatically collected, processed and analyzed by computer technology, and the correlation between the user usage behavior data and the marketing activities is utilized to automatically manage the marketing activities, which greatly improves the management efficiency of the marketing activities, and can greatly reduce the occurrence of errors or omissions compared to manual management of marketing activities, thereby improving the management effect of marketing activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0083] Figure 1 A flowchart of a marketing activity management method provided in an embodiment of the present application.

[0084] Figure 2 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0085] Figure 3 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0086] Figure 4 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0087] Figure 5 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0088] Figure 6 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0089] Figure 7 A partial flow chart of a marketing activity management method provided in an embodiment of the present application.

[0090] Figure 8 A schematic diagram of the structure of a marketing activity management device provided in an embodiment of the present application.

[0091] Fig. 9 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0092] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0093] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0094] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0095] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0096] In order to solve at least one of the above problems existing in the prior art, in a first aspect, the present application provides a marketing activity management method, such as Figure 1 As shown, the present application provides a marketing activity management method, comprising:

[0097] S101. Collecting the user's usage behavior data within the mobile banking APP;

[0098] In step S101, data collection points can be predefined to determine which user behavior data needs to be collected, such as page visits, button clicks, transaction types, search queries, timestamps, etc. Then, the front-end tracking technology can be used to embed tracking code in the APP front end to capture user behavior events and send user behavior events to the back-end server. All collected data can also be encrypted to comply with data protection regulations and user privacy policies.

[0099] For the collected data, data cleaning and preprocessing can also be performed. For example, the collected raw data can be cleaned to remove invalid or incorrect data, and then formatted and standardized. After data cleaning and preprocessing, a suitable database or data warehouse can be selected to store the user usage behavior data to ensure data accessibility and scalability. If the data comes from different sources, data integration is required to ensure data consistency and integrity.

[0100] To protect user privacy, the user usage behavior data can also be anonymized before being saved. To ensure data accuracy and reliability, the performance of the data collection system and data quality can also be monitored regularly. In addition, a user feedback channel can be provided to collect users' opinions and suggestions on data collection and usage.

[0101] Through the above steps, the usage behavior data of users within the mobile banking APP can be effectively collected, providing a basis for subsequent user behavior analysis and marketing campaign management.

[0102] S102. Determine the hot word tags of the user and the marketing campaigns involved according to the usage behavior data of each user;

[0103] In step S102, data mining and natural language processing (NLP) techniques can be used to determine the hot word tags of the user from the user's usage behavior data. Specifically, the text data in the user's usage behavior data can be cleaned first, including removing stop words (such as common words like "of", "and", etc.), punctuation marks, and performing stemming or lemmatization, etc.; then keyword extraction can be carried out. For example, statistical methods such as TF-IDF (term frequency-inverse document frequency) can be used to identify important keywords in the text, or NLP techniques such as named entity recognition (NER) can be used to identify information such as proper nouns, locations, organizations, etc. in the text. Finally, the hot word tags of the user are determined according to the extracted keywords, and the hot word tags of the user are used to describe the user's interests and behavior characteristics.

[0104] In addition, methods such as topic modeling, sentiment analysis, clustering analysis, and / or machine learning models can also be used to determine the hot word tags of the user according to the user's usage behavior data.

[0105] Topic modeling: Use topic modeling techniques such as LDA (Latent Dirichlet Allocation) to discover potential topics and patterns from user behavior data.

[0106] Sentiment analysis: Perform sentiment analysis on the user's comments or feedback to determine the user's sentiment tendency towards certain topics or products.

[0107] Cluster analysis: Use clustering algorithms (such as K-means) to group user behaviors to identify groups of users with similar behavior patterns.

[0108] Machine Learning Models: Train machine learning models, such as classifiers or predictive models, to predict user interests and preferences based on their behavior.

[0109] It should be understood that user interests and behavior characteristics change over time, so users' hot word tags can be regularly evaluated and optimized to ensure that they accurately reflect user behavior. In addition, users can also be allowed to provide feedback on hot word tags to further adjust and improve the tag system.

[0110] Through these steps, hot word tags that reflect users' interests and preferences can be effectively extracted from their usage behavior data to provide support for subsequent marketing campaign management.

[0111] In addition, data related to marketing activities can be extracted from the usage behavior data of each user, wherein the data related to marketing activities can include marketing activities of clicks, browsing, searching and / or generating transactions; based on the extracted data related to marketing activities, the marketing activities involved by the user are determined. Specifically,

[0112] Define campaign-related data: First, you need to identify which user behaviors are relevant to campaigns. Based on search results, this data can include clicks, views, searches, and / or campaigns that result in transactions.

[0113] Data collection: Use client SDK, server SDK or tool import to collect user behavior data.

[0114] Data extraction: Extract data related to marketing activities from collected user behavior data to obtain the marketing activities in which the user is involved.

[0115] Through these steps, information related to marketing activities can be effectively extracted from user behavior data, and the specific marketing activities involved in the user can be determined accordingly, thereby optimizing the marketing effect.

[0116] S103, determining the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved, and, for each hot word tag, counting the number of users having the hot word tag;

[0117] In step S103, the internal association between marketing activities and hot word tags can be explored based on the hot word tags of each user and the marketing activities involved, and then the weight of each marketing activity can be determined based on the hot word tags of each user. In this way, each marketing activity can be assigned a weight that reflects its matching degree with the user's interests, thereby helping to manage marketing activities more effectively in the future.

[0118] S104, determining the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users having the hot word tag;

[0119] In step S104, for example, for each hot word tag, the number of users with the hot word tag is counted. The more users with the hot word tag, the larger the user group interested in the hot word tag. Each marketing campaign has at least one matching hot word tag. The marketing campaign and the matching hot word tags can be pre-set. The matching degree between the marketing campaign and each hot word tag can also be set. Then, the matching degree between the marketing campaign and the hot word tag is multiplied by the number of users with the hot word tag to obtain a product. By analogy, the product between the matching degree of other hot word tags and the marketing campaign and the number of users with the other hot word tags is calculated. All the products are added up to obtain the weight of the marketing campaign.

[0120] S105. Determine a comprehensive score for each marketing activity based on the weight of each marketing activity and the historical marketing effectiveness data of each marketing activity;

[0121] In step S105, it is necessary to collect historical data of each marketing campaign, including but not limited to the number of participants, conversion rate, revenue, cost, etc. Then, the weight of each indicator can be determined according to the business objectives and marketing strategies. For example, if the conversion rate is the most important indicator, it may be given a higher weight. In addition, since different indicators may have different magnitudes and units, the data needs to be standardized so that they can be compared on the same scale, such as using Z-score standardization or minimum-maximum standardization method. Then, calculate the weighted score: multiply the standardized value of each indicator by its weight, and then sum all indicators to obtain the weighted score of each marketing campaign. The weighted score can be used as the historical marketing effectiveness score of each marketing campaign, reflecting its overall historical performance.

[0122] The historical marketing effectiveness score of each marketing campaign and the weight of the marketing campaign can be used to further determine the comprehensive score of the marketing campaign, so that the comprehensive score can be used to analyze which marketing campaigns perform well and which need improvement.

[0123] S106. According to the comprehensive score of each marketing activity, at least one of the marketing activities is put on the shelves.

[0124] In step S106, all marketing activities can be ranked according to the comprehensive scores obtained in the previous step. This will help determine which activities should be prioritized for listing. In addition, the resources available for marketing activities can be pre-evaluated, including budget, manpower, time, etc., which will affect the number of marketing activities that can be listed at the same time.

[0125] You can also set a scoring threshold, and only marketing campaigns that meet or exceed this threshold are eligible to be listed. This threshold can be determined based on resource conditions and business goals.

[0126] Based on the comprehensive score and resource evaluation, a detailed launch plan can be developed. This plan can include the launch time, expected duration, expected goals, etc. of each marketing campaign. According to the launch plan, resources are reasonably allocated to ensure that each marketing campaign can get the necessary support.

[0127] Next, the marketing activities can be put on the corresponding platform or channel according to the listing plan, such as social media, email marketing, advertising, etc. In this embodiment, it can be a mobile banking APP.

[0128] The marketing activity management method provided in the embodiment of the present application collects the usage behavior data of users in the mobile banking APP; determines the user's hot word tags and the marketing activities involved according to each user's usage behavior data; determines the weight of each marketing activity according to the hot word tags of each user and the marketing activities involved; determines the comprehensive score of each marketing activity according to the weight of each marketing activity and the historical marketing effectiveness data of each marketing activity; and puts at least one of the marketing activities on the shelves according to the comprehensive score of each marketing activity. In this way, the user's usage behavior data is automatically collected, processed and analyzed through computer technology, and the correlation between the user's usage behavior data and the marketing activities is used to automatically manage the marketing activities, which greatly improves the management efficiency of the marketing activities, and can greatly reduce the occurrence of errors or omissions compared to manual management of marketing activities, thereby improving the management effect of marketing activities.

[0129] like Figure 2 As shown, in some embodiments, the above step S101 may include:

[0130] S1011. Use the configuration interface or configuration file provided by the tracking tool integrated in the front-end code of the mobile banking APP to define the user usage behavior to be tracked, where the user usage behavior includes at least one of the following: click behavior, page browsing behavior, search behavior, and transaction behavior;

[0131] S1012. Collect the user's usage behavior data within the mobile banking APP through the tracking tool.

[0132] Specifically, first, you need to determine the user behaviors that need to be tracked, including click behaviors, page browsing behaviors, search behaviors, and transaction behaviors. Then, choose the appropriate tracking method: Tracking can be divided into code tracking, visual tracking, and full tracking. Code tracking can set the custom attributes of the event in detail; visual tracking is simple to operate, but the amount of data reported is small; full tracking can capture all user behaviors, but may generate a large amount of data. Choose the appropriate tracking strategy, or combine multiple tracking methods.

[0133] Key-Value parameters can be defined for each user behavior event. For example, when clicking a payment button, in addition to recording the click event, information such as payment method and payment amount can also be attached. In addition, a tracking point document can be created to record information such as application ID, page name, tracking point location, tracking point ID and parameters, and the online status and change history of the tracking point can be maintained in the document to facilitate tracking and management of the tracking point life cycle.

[0134] When implementing data collection, data can be sent asynchronously when the page is unloaded or closed, ensuring data integrity and accuracy without affecting the user experience.

[0135] If you are faced with massive amounts of data and multi-dimensional analysis, you can use powerful computing engines and unified data models to support quick decision-making and refined operations.

[0136] Through these steps, the tracking tool can be effectively integrated into the front-end code of the mobile banking app to track and analyze user behavior.

[0137] like Figure 3 As shown, in some embodiments, the above step S102 may include:

[0138] S1021. For each user, extract text information from the user's usage behavior data;

[0139] S1022, performing word segmentation processing on the extracted text information to obtain an initial word set;

[0140] S1023, eliminating stop words in the initial word set to obtain a target word set;

[0141] S1024, searching for each target word in the target word set in a preset keyword library;

[0142] S1025. If the target word exists in the keyword library, count the frequency of the target word appearing in the target word set;

[0143] S1026. Determine the hot word tags of the user according to the frequency of each said target word in the set of target words, where the user has at least one hot word tag.

[0144] Specifically, first, it is necessary to extract text information from the user's usage behavior data. This may include the user's search records, page access content, clicked content, transaction objects, etc. on the platform. Then, the extracted text information is segmented, that is, the continuous text is split into individual words or phrases to form an initial set of words. In the obtained initial set of words, stop words are removed. Stop words refer to words that frequently appear in the text but have little significance for text analysis, such as "de", "shi", "zai", etc. Removing these words can improve the accuracy and efficiency of subsequent analysis. The set of target words after removing stop words is matched with a preset keyword library. The keyword library is a predefined set of words related to the user's interests or behaviors. For the target words found in the keyword library, count their frequencies of occurrence in the set of target words. This step helps to identify the user's preferences for certain topics or words. According to the frequency of occurrence of each target word, determine the user's hot word tags. The hot word tags can reflect the user's interest points or behavioral characteristics, and ensure that each user has at least one hot word tag, which helps to classify users or provide personalized recommendations.

[0145] Such as Figure 4 As shown, in some embodiments, the above step S103 may include:

[0146] S1031. For each said marketing activity, count the hot word tags of the users involved in the marketing activity;

[0147] S1032. For each said hot word tag, count the number of users among the users involved in the marketing activity who have the hot word tag;

[0148] S1033. Determine the matching degree between the hot word tag and the marketing activity according to the number of users corresponding to each said hot word tag.

[0149] Specifically, for each marketing activity, first find all the users involved in the marketing activity, and then count the hot word tags of these users; then for each hot word tag of these users, count the number of users among these users who have the hot word tag, that is, how many users have the hot word tag. Finally, calculate the matching degree according to the number of users corresponding to each hot word tag. The matching degree can be calculated by the following formula: Matching degree = the number of users who have the hot word tag among the users participating in the marketing activity / the total number of users participating in the marketing activity.

[0150] Based on the matching calculation results, it can be determined which hot word tags are highly relevant to the marketing campaign and which are not. Users corresponding to hot word tags with high matching degrees may have a higher interest or participation in the marketing campaign.

[0151] In addition, user interests and behaviors may change over time, so hot word tags and matching degrees may be updated regularly. For new marketing activities that have never been listed before, the matching degree between the new marketing activity and each hot word tag may be determined by the similarity between the marketing activities that have been listed and the new marketing activity. Specifically, the matching degree between the marketing activity that is most similar to the new marketing activity and the hot word tag may be used as the matching degree between the new marketing activity and the hot word tag, or the similarity may be used as a weighted value to calculate the matching degree between the new marketing activity and the hot word tag.

[0152] like Figure 5 As shown, in some embodiments, the above step S105 may include:

[0153] S1051. For each marketing campaign, obtain historical marketing effectiveness data of the marketing campaign, wherein the historical marketing effectiveness data includes at least one of the following: click-through rate, conversion rate, and sales volume of the marketing campaign;

[0154] S1052. Calculate the historical marketing effectiveness score of the marketing activity according to the historical marketing effectiveness data;

[0155] S1053. Determine a comprehensive score for the marketing campaign based on the historical marketing effectiveness score of the marketing campaign and the weight of the marketing campaign.

[0156] Specifically, historical data of specific marketing activities can be extracted from a database or data warehouse. These data may include, but are not limited to, key indicators such as click-through rate (CTR), conversion rate, and sales. Then, based on the acquired data, different statistical methods or models can be used to calculate the historical marketing effectiveness score of each marketing activity. For example, a weighted average method can be used, in which indicators such as click-through rate, conversion rate, and sales can be given different weights based on their importance to the business. Finally, the historical marketing effectiveness score is combined with the weight of the marketing activity to determine the final comprehensive score of each marketing activity.

[0157] like Figure 6 As shown, in some embodiments, the above step S106 may include:

[0158] S1061. Sort the marketing activities in descending order of comprehensive scores;

[0159] S1062. Put the sorted marketing activities on the shelves, so that the mobile banking APP displays the marketing activities on the shelves in order according to the sorting.

[0160] Specifically, all marketing activities can be sorted from high to low according to the scores. This can be achieved through simple sorting algorithms, such as quick sort, merge sort, etc. After the sorting is completed, these marketing activities need to be put on the shelves according to the sorting results. Before putting them on the shelves, you must first ensure that the display content (such as pictures, descriptions, discount information, etc.) of each marketing activity is ready. Then, in the back-end system of the mobile banking APP, update the status of the marketing activity according to the sorting results and change it to the "on the shelves" status. In the front-end display logic of the APP, ensure that the marketing activities are displayed according to the sorting results.

[0161] like Figure 7 As shown, in some embodiments, after the above step S106, the method may further include:

[0162] S107, collecting data of users participating in the marketing activities within the mobile banking APP;

[0163] S108. Re-determine the comprehensive score of each marketing activity based on the weight of each marketing activity and the data of the user's participation in the marketing activity in the mobile banking APP;

[0164] S109: According to the comprehensive score of each marketing activity, at least one marketing activity is removed from the shelves.

[0165] Specifically, first collect data on user interactions with marketing activities, which may include key behavioral indicators such as user clicks, participation time, and transaction volume. Specifically, data collection can be achieved through user behavior event tracking technology. Then, based on the user's participation data in the mobile banking app and the initial weight of the marketing activity, recalculate the comprehensive score of each marketing activity, where the user's participation data in the mobile banking app reflects the current performance of each marketing activity.

[0166] Based on the recalculated comprehensive score, poorly performing marketing campaigns can be delisted. These poorly performing campaigns can also be removed from the prominent position of the mobile banking app or stopped completely. The delisting operation can also be carried out continuously based on the continuous evaluation of marketing campaigns.

[0167] These steps together form a closed-loop marketing campaign management process, which aims to optimize the allocation of marketing resources and improve the overall effectiveness of marketing activities through continuous monitoring, evaluation and adjustment.

[0168] In order to better understand the present application, the marketing activity management method provided by the present application is described in detail below through a specific embodiment.

[0169] Mobile banking collects data on user behavior within the mobile banking app (collected information includes customer click events and page browsing events), and sends the collected data to the big data center component for storage;

[0170] The data product service component processes the data in the big data center library. Data analysis and processing, data cleaning is to remove the wrong collected data, old versions of collected data, and filter and archive according to the events that meet the collection requirements, and then label the hot words such as automobiles, e-commerce, financial management, credit cards, and transfers that the customer frequently visits, and generate a customer preference flag field (for example: a new customer visits and views credit card activities in the mobile banking app. After data collection, when the customer visits the credit card activities 10 times a day, the customer's preference hot words are activated, and the preference flag is set to 01. At this time, the new customer generates a preference flag for credit card activities).

[0171] The reach advertising component synchronizes the advertising activities released by the mobile banking channel to the data product service component. The data product service component selects and matches highly matched marketing advertisements based on user preferences, and records the matching degree using weight marks (for example, the activity list includes e-commerce activities, transfer activities, credit card activities, etc. The data product service component can select credit card activities that match customer A based on the label hot word credit card. After screening, the weight of the credit card activity is temporarily set to 1. If customer B is also matched with the credit card activity later, the weight of the credit card activity is updated from the original 1 to 2. The more matching customers, the greater the weight of the activity, which means that the activity is more popular).

[0172] The data product service component sorts the matching weights and effectiveness data and synchronizes them in batches to the channel service component, which then puts the data on the shelf and stores it in the warehouse. When customers use the mobile banking activity calendar function, they obtain the channel service activity calendar data and display marketing activities in the order of activity weights (the data product service component calculates the effectiveness data of e-commerce activities, transfer activities, and credit card activities in the activity list, and adds the weights corresponding to each activity to obtain the activity's popularity value. The activity list is then sorted by popularity value. The larger the popularity value, the more customers like it and the more significant the effectiveness).

[0173] Customers participate in the activities displayed on the activity calendar and collect data for the activities (collected data includes activity name, amount of participation, number of activity clicks, and number of activity views) and send it to the big data center component for storage. The data product service component calculates and increases the weight of activities with significant activity results based on the activity operation performance data, and reduces the weight of activities with insufficient activity results. Finally, the activity data is sorted according to the weight, and activities with insufficient results and those that do not meet the user preference standards are filtered out and re-listed. (Data on significant activity results: After collecting activity data, the data is filtered. For example, the activities listed include e-commerce activities, transfer activities, and credit card activities. Among them, the number of visits to credit card activities by customers reaches 1.2 million times / day, and the number of visits to e-commerce activities is only 10,000 / day. The threshold for meeting the activity standard is set to 200,000 / day. It is calculated that the credit card activity meets the standard 1 million times / day, and the weight of the credit card activity can be increased by 100 in proportion. However, the e-commerce activity does not meet the standard, and the effect is negative 190,000 / day. The e-commerce activity can be reduced by 19 in proportion. After re-sorting, the low-heat activities are automatically removed from the shelves according to the weight of the activity list and replaced with other activities that meet the requirements).

[0174] This embodiment uses data analysis to automatically put the content of the activity calendar marketing activities on the shelves, effectively avoiding the input of operation and maintenance personnel and the problem of accurate activity online caused by manual operation and maintenance, reducing operation and maintenance costs, and providing an automated listing method. It also realizes automatic listing and unlisting of the content of the activity calendar marketing activities based on operation effectiveness analysis, and realizes not listing activities that are not customer preferences, and showing marketing activities that customers prefer, making the customer experience more friendly and significantly improving the operation effectiveness of the activities.

[0175] The present invention also provides a marketing activity management device, as described in the following embodiments. Since the principle of solving the problem by the device is similar to that of the marketing activity management method, the implementation of the device can refer to the implementation of the marketing activity management method, and the repeated parts will not be repeated.

[0176] like Figure 8 As shown, the present application provides a marketing activity management device, comprising:

[0177] The collection module 21 is used to collect the user's usage behavior data within the mobile banking APP;

[0178] The first determination module 22 is used to determine the hot word tags of each user and the marketing activities involved according to the usage behavior data of each user;

[0179] The second determination module 23 is used to determine the matching degree between each hot word tag and each marketing activity according to the hot word tag of each user and the marketing activity involved, and, for each hot word tag, count the number of users having the hot word tag;

[0180] The third determination module 24 is used to determine the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag;

[0181] A fourth determination module 25 is used to determine a comprehensive score for each marketing activity according to the weight of each marketing activity and the historical marketing effectiveness data of each marketing activity;

[0182] The listing module 26 is used to list at least one marketing activity according to the comprehensive score of each marketing activity.

[0183] The marketing activity management device provided by the embodiment of the present invention collects the usage behavior data of users in the mobile banking APP; determines the user's hot word tags and the marketing activities involved according to each user's usage behavior data; determines the weight of each marketing activity according to the hot word tags of each user and the marketing activities involved; determines the comprehensive score of each marketing activity according to the weight of each marketing activity and the historical marketing effectiveness data of each marketing activity; and puts at least one of the marketing activities on the shelves according to the comprehensive score of each marketing activity. In this way, the user's usage behavior data is automatically collected, processed and analyzed through computer technology, and the correlation between the user's usage behavior data and the marketing activities is used to automatically manage the marketing activities, which greatly improves the management efficiency of the marketing activities, and can greatly reduce the occurrence of errors or omissions compared to manual management of marketing activities, thereby improving the management effect of marketing activities.

[0184] In some embodiments, the acquisition module is specifically used for:

[0185] The user usage behavior to be tracked is defined using the configuration interface or configuration file provided by the tracking tool integrated in the front-end code of the mobile banking APP. The user usage behavior includes at least one of the following: click behavior, page browsing behavior, search behavior, and transaction behavior;

[0186] The user's usage behavior data within the mobile banking APP is collected through the tracking tool.

[0187] In some embodiments, the first determining module is specifically configured to:

[0188] For each user, extract text information from the user's usage behavior data;

[0189] Perform word segmentation on the extracted text information to obtain an initial word set;

[0190] Eliminate stop words from the initial word set to obtain a target word set;

[0191] Searching for each target word in the target word set in a preset keyword library;

[0192] If the target word exists in the keyword library, the frequency of occurrence of the target word in the target word set is counted;

[0193] According to the frequency of occurrence of each target word in the target word set, the hot word tag of the user is determined, wherein the user has at least one hot word tag.

[0194] In some embodiments, the first determining module is specifically configured to:

[0195] Extracting data related to marketing activities from the usage behavior data of each user, wherein the data related to marketing activities include marketing activities of clicks, browsing, searches and / or transactions;

[0196] The marketing activities in which the user is involved are determined based on the extracted data related to the marketing activities.

[0197] In some embodiments, the third determination module determines the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved, including:

[0198] For each of the marketing activities, count the hot word tags of users involved in the marketing activity;

[0199] For each of the hot word tags, count the number of users who have the hot word tag among the users involved in the marketing activity;

[0200] According to the number of users corresponding to each hot word tag, the matching degree between the hot word tag and the marketing activity is determined.

[0201] In some embodiments, the fourth determination module is specifically configured to:

[0202] For each marketing campaign, obtain historical marketing effectiveness data of the marketing campaign, wherein the historical marketing effectiveness data includes at least one of the following: click-through rate, conversion rate, and sales volume of the marketing campaign;

[0203] Calculate the historical marketing effectiveness score of the marketing activity based on the historical marketing effectiveness data;

[0204] Determine the comprehensive score of the marketing campaign based on the historical marketing effectiveness score of the marketing campaign and the weight of the marketing campaign.

[0205] In some embodiments, the racking module is specifically used for:

[0206] Rank the marketing activities in descending order of comprehensive scores;

[0207] The sorted marketing activities are put on the shelves so that the mobile banking APP displays the marketing activities in order according to the sorting.

[0208] In some embodiments, the collection module is further used to: collect data of users participating in the marketing activities within the mobile banking APP;

[0209] The fourth determination module is further used to: re-determine the comprehensive score of each marketing activity according to the weight of each marketing activity and the data of the user's participation in the marketing activity in the mobile banking APP;

[0210] The device further comprises: a delisting module, which is used to delist at least one marketing activity according to the comprehensive score of each marketing activity.

[0211] It should be noted that the marketing activity management method provided in the embodiment of the present invention can be used in the financial field, and can also be used in any technical field other than the financial field. The embodiment of the present invention does not limit the application field of the virtual reality-based bank training system and its training method.

[0212] Fig. 9 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention, such as Fig. 9 As shown, the electronic device includes: a processor (processor) 301, a memory (memory) 302 and a bus 303.

[0213] The processor 301 and the memory 302 communicate with each other via the bus 303 .

[0214] The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above method embodiments.

[0215] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned marketing activity management method is implemented.

[0216] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned marketing activity management method is implemented.

[0217] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0218] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0219] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0220] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0221] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A marketing activity management method, It is characterized in that include: Collect user behavior data within the mobile banking app; Determine the user's hot word tags and the marketing activities involved based on each user's usage behavior data; According to the hot word tags of each user and the marketing activities involved, determine the matching degree between each hot word tag and each marketing activity, and, for each hot word tag, count the number of users having the hot word tag; Determine the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag; Determine the comprehensive score of each marketing campaign based on the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign; According to the comprehensive score of each marketing campaign, at least one of the marketing campaigns is put on the shelves.

2. The method according to claim 1, It is characterized in that The collected user behavior data within the mobile banking APP includes: The user usage behavior to be tracked is defined using the configuration interface or configuration file provided by the tracking tool integrated in the front-end code of the mobile banking APP. The user usage behavior includes at least one of the following: click behavior, page browsing behavior, search behavior, and transaction behavior; The user's usage behavior data within the mobile banking APP is collected through the tracking tool.

3. The method according to claim 1, It is characterized in that According to each user's usage behavior data, determine the user's hot word tags, including: For each user, extract text information from the user's usage behavior data; Perform word segmentation on the extracted text information to obtain an initial word set; Eliminate stop words from the initial word set to obtain a target word set; Searching for each target word in the target word set in a preset keyword library; If the target word exists in the keyword library, the frequency of occurrence of the target word in the target word set is counted; According to the frequency of occurrence of each target word in the target word set, the hot word tag of the user is determined, wherein the user has at least one hot word tag.

4. The method according to claim 1, It is characterized in that Based on each user’s usage behavior data, determine the marketing activities that the user is involved in, including: Extracting data related to marketing activities from the usage behavior data of each user, wherein the data related to marketing activities include marketing activities of clicks, browsing, searches and / or transactions; The marketing activities in which the user is involved are determined based on the extracted data related to the marketing activities.

5. The method according to claim 1, It is characterized in that Determining the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved includes: For each of the marketing activities, count the hot word tags of users involved in the marketing activity; For each of the hot word tags, count the number of users who have the hot word tag among the users involved in the marketing activity; According to the number of users corresponding to each hot word tag, the matching degree between the hot word tag and the marketing activity is determined.

6. The method according to claim 1, It is characterized in that The comprehensive score of each marketing campaign is determined based on the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign, including: For each marketing campaign, obtain historical marketing effectiveness data of the marketing campaign, wherein the historical marketing effectiveness data includes at least one of the following: click-through rate, conversion rate, and sales volume of the marketing campaign; Calculate the historical marketing effectiveness score of the marketing activity based on the historical marketing effectiveness data; Determine the comprehensive score of the marketing campaign based on the historical marketing effectiveness score of the marketing campaign and the weight of the marketing campaign.

7. The method according to claim 1, It is characterized in that The step of putting at least one marketing activity on the shelves according to the comprehensive score of each marketing activity includes: Rank the marketing activities in descending order of comprehensive scores; The sorted marketing activities are put on the shelves so that the mobile banking APP displays the marketing activities in order according to the sorting.

8. The method according to claim 1, It is characterized in that After putting at least one marketing activity on the shelf according to the comprehensive score of each marketing activity, the method further includes: Collect data on users’ participation in the marketing activities within the mobile banking app; Re-determine the comprehensive score of each marketing campaign based on the weight of each marketing campaign and the data of users’ participation in the marketing campaign within the mobile banking app; According to the comprehensive score of each marketing campaign, at least one of the marketing campaigns is removed from the shelves.

9. A marketing activity management device, It is characterized in that include: The collection module is used to collect the user's usage behavior data within the mobile banking APP; The first determination module is used to determine the hot word tags of each user and the marketing activities involved according to the usage behavior data of the user; A second determination module is used to determine the matching degree between each hot word tag and each marketing activity according to each user's hot word tag and the marketing activity involved, and, for each hot word tag, count the number of users having the hot word tag; A third determination module is used to determine the weight of each marketing activity according to the matching degree between each marketing activity and the hot word tag and the number of users with the hot word tag; A fourth determination module is used to determine a comprehensive score for each marketing campaign based on the weight of each marketing campaign and the historical marketing effectiveness data of each marketing campaign; The listing module is used to list at least one marketing activity according to the comprehensive score of each marketing activity.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

11. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

12. A computer program product, It is characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images