Accurate flow casting method and system based on user behavior analysis

Abstract

The invention discloses a precise flow casting method and system based on user behavior analysis, and belongs to the technical field of information processing, and the method comprises the following steps: S1, data collection and integration; s2, analyzing user behaviors; s3, constructing a user portrait; s4, accurate matching and putting; s5, effect evaluation and user feedback; and S6, putting optimization and system upgrading. According to the method, the data processing process is optimized by adopting weighting, dimensionality reduction and feature selection technologies, the density and quality of the data are improved, the consistency and comparability of the data are ensured through processing such as standardization and normalization, meanwhile, the user behavior data are collected through means such as websites, APPs and social media, the data source is rich, and the user experience is improved. The problem that advertisement putting is not accurate enough due to sparse user behavior data is solved, the privacy protection principle is strictly followed in the data collection, storage and processing process, the user personal information is protected by adopting an encryption or anonymization technical means, and privacy protection is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a precise flow casting method and system based on user behavior analysis. Background Art

[0002] With the rapid development of Internet technology, the accumulation of user behavior data has reached an unprecedented scale. These data not only record the online activity trajectory of users, but also contain important information such as users' interests, preferences and needs, which can be used for user behavior analysis.

[0003] User behavior analysis is a key data analysis technology that focuses on studying the interaction between users and products, services or systems. By collecting and analyzing user behavior data, companies can gain in-depth insights into user needs, preferences and usage habits, thereby optimizing product design, improving user experience and increasing user satisfaction. This analysis usually involves tracking the user's behavior path on a website, application or physical store, including key indicators such as click-through rate, page viewing time, and purchase conversion rate. The results of user behavior analysis can help companies identify market trends, predict user behavior, develop personalized marketing strategies, and improve overall business performance. In addition, through user behavior analysis, companies can identify potential problems and improvement points, such as navigation barriers or process bottlenecks, and then carry out targeted optimization. In short, user behavior analysis is a bridge connecting user needs and corporate decision-making, which is of great significance for improving competitiveness and achieving sustainable development.

[0004] Therefore, precise advertising methods based on user behavior analysis have emerged. By deeply mining these data and understanding the real needs of users, precise advertising can be achieved, improving advertising effects and user experience.

[0005] The existing technology has the following problems:

[0006] Traditional user behavior data is usually very sparse, that is, users do not have clear behavioral data for most items or services, which will lead to inaccurate advertising. When new users or new items join the system, it is difficult to provide accurate recommendations due to the lack of historical behavior data. In some application scenarios, such as online shopping, social media, etc., user behavior is highly real-time. However, existing advertising systems often find it difficult to capture changes in user behavior in real time and adjust advertising strategies accordingly.

[0007] Therefore, a precise traffic flow method and system based on user behavior analysis is proposed to solve or alleviate the above problems. Summary of the invention

[0008] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a precise flow casting method and system based on user behavior analysis.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A precise flow method based on user behavior analysis includes the following steps:

[0011] S1: Data collection and integration;

[0012] S2: User behavior analysis;

[0013] S3: Build user profiles;

[0014] S4: Accurate matching and delivery;

[0015] S5: Effect evaluation and user feedback;

[0016] S6: Delivery optimization and system upgrade.

[0017] Preferably, the S1: data collection and integration, comprises the following steps:

[0018] The S1 includes: collecting the user's historical behavior data, including browsing history, click behavior, purchase history, and dwell time, and tracking the user's online behavior in real time to obtain the latest user behavior data.

[0019] Preferably, the S2: user behavior analysis comprises the following steps:

[0020] Use machine learning algorithms and data mining algorithms to conduct in-depth analysis of user behavior data to identify users' interests, hobbies, consumption habits, and behavior patterns, including behavior sequence analysis, preference analysis, and time series analysis;

[0021] The behavior sequence analysis includes identifying user access paths and behavior patterns;

[0022] The preference analysis includes analyzing the user's preferences for goods, content, and activities based on the user's historical behavior;

[0023] The time series analysis includes capturing the changing trends of user behaviors over time.

[0024] Preferably, the S3: constructing a user portrait comprises the following steps:

[0025] Build a multi-dimensional and three-dimensional user portrait based on the results of user behavior analysis;

[0026] The user profile includes basic attributes, interests and hobbies, consumption habits, and potential needs;

[0027] The basic attributes include age, gender, occupation, and region;

[0028] The interests and hobbies include reading preferences, entertainment preferences, and sports preferences;

[0029] The consumption habits include consumption capacity, consumption frequency, and brand preference;

[0030] The potential needs include purchase intentions or possible future demands.

[0031] Preferably, the S4: accurate matching and delivery includes the following steps:

[0032] Accurately match user profiles with delivery targets, customize personalized delivery strategies for different user groups, and push advertisements or content to target users. The delivery strategies include content customization, time planning, channel selection, and budget allocation;

[0033] The content customization includes customizing the advertising content that attracts the user's attention according to the user's interest preferences;

[0034] The time planning includes delivering the content during the time when the user activity is high;

[0035] The channel selection includes selecting an advertisement display channel according to the user's active platform;

[0036] The budget allocation includes reasonably allocating the advertising budget based on the estimated advertising effect.

[0037] Preferably, said S5: effect evaluation and user feedback, comprises the following steps:

[0038] Monitor advertising effectiveness in real time and set key performance indicators, including click-through rate, conversion rate and ROI.

[0039] Preferably, the S6: delivery optimization and system upgrade includes the following steps:

[0040] Based on effect evaluation and user feedback, the system is iteratively upgraded. According to the evaluation and analysis results, the A / B testing and machine learning model methods are used to optimize and adjust the delivery strategy, including optimizing advertising materials, adjusting delivery time and region, and improving user portrait construction.

[0041] The present invention also provides a precise flow-casting system based on user behavior analysis, which performs the precise flow-casting method based on user behavior analysis as described above, including:

[0042] Data collection and processing module, used to collect user behavior data from various data sources and process the collected data, where the data sources include but are not limited to websites, apps, and social media;

[0043] User behavior analysis module, which conducts in-depth analysis of user behavior data based on machine learning algorithms and data mining algorithms to identify user characteristics and behavior patterns;

[0044] User portrait building module, used to build detailed user portraits based on user behavior analysis results;

[0045] The precise matching and delivery module is used to accurately match user portraits and delivery targets, and select appropriate delivery channels and timings for delivery;

[0046] The delivery effect evaluation module is used to evaluate the delivery effect of advertisements and collect user feedback;

[0047] The optimization module is used to optimize and adjust the delivery strategy and system iterative upgrades.

[0048] The present invention has the following beneficial effects:

[0049] 1. The present invention provides a precise flow delivery method and system based on user behavior analysis, which optimizes the data processing process by adopting weighting, dimensionality reduction and feature selection techniques, improves the density and quality of data, ensures the consistency and comparability of data by standardization and normalization, and collects user behavior data through websites, apps, social media and other means. The data sources are rich, which solves the problem of inaccurate advertising delivery due to sparse user behavior data. In the process of data collection, storage and processing, the privacy protection principle is strictly observed, and encryption or anonymization technology is used to protect user personal information and strengthen privacy protection.

[0050] 2. The present invention provides a precise traffic delivery method and system based on user behavior analysis. By utilizing other attribute information of new users or new items and adopting a personalized sorting algorithm to push advertisements or content to target users, accurate recommendations are provided for new users or new items, thereby improving the accuracy of traffic delivery and user satisfaction.

[0051] 3. The present invention provides a precise flow delivery method and system based on user behavior analysis. By real-time tracking of the user's online behavior and obtaining the user's latest behavior data, the changes in user behavior can be captured in real time. The delivery strategy can be optimized and adjusted through the optimization module. At the same time, the user's social relationship and geographic location can be integrated to more comprehensively understand the user's needs. It can quickly provide personalized recommendations to users, and realize real-time precise flow delivery. At the same time, the optimization module is also used for iterative upgrades of the system, which can continuously optimize the system algorithm, improve the system's data processing capabilities, enrich the user portrait dimensions, and improve the delivery strategy library.

[0052] 4. The present invention provides a precise flow delivery method and system based on user behavior analysis. Through comprehensive data collection and integration, in-depth user behavior analysis, precise portrait construction, scientific delivery strategy formulation and optimization, effective effect evaluation and feedback, and continuous system iteration and upgrading, it provides strong support for enterprises to achieve precise marketing. The implementation of this method and system will help enterprises improve the accuracy and efficiency of advertising delivery, reduce marketing costs, and ultimately enhance the market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 This is a flow chart of the precise traffic flow method based on user behavior analysis of the present invention;

[0055] Figure 2 It is a schematic diagram of the framework of the precise flow casting system based on user behavior analysis of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0057] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0059] In the description of the present invention, it should be understood that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the product of the invention is conventionally placed when in use, or are the orientations or positional relationships conventionally understood by those skilled in the art. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0060] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0061] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] A precise traffic delivery method based on user behavior analysis includes the following steps:

[0063] S1: Data collection and integration;

[0064] S2: User behavior analysis;

[0065] S3: Build user profiles;

[0066] S4: Accurate matching and delivery;

[0067] S5: Effect evaluation and user feedback;

[0068] S6: Delivery optimization and system upgrade.

[0069] Through comprehensive data collection and integration, in-depth user behavior analysis, accurate portrait construction, scientific delivery strategy formulation and optimization, effective effect evaluation and feedback, and continuous system iteration and upgrade, it provides strong support for enterprises to achieve precision marketing. The implementation of this method and system will help enterprises improve the accuracy and efficiency of advertising, reduce marketing costs, and ultimately enhance the market competitiveness of enterprises.

[0070] S1 includes: collecting historical behavior data of users, including but not limited to browsing history, click behavior, purchase history, and dwell time, and tracking users' online behavior in real time, obtaining the latest user behavior data, and using weighting, dimensionality reduction, and feature selection techniques to process the collected data, including but not limited to cleaning, deduplication, standardization, and normalization, and then storing the processed data in a data warehouse. In the process of data collection, storage, and processing, privacy protection principles are strictly observed, and encryption or anonymization techniques are used to protect user personal information.

[0071] Weighting, dimensionality reduction and feature selection techniques are used to optimize the data processing process, improve data density and quality, and standardize and normalize user behavior data to ensure data consistency and comparability. At the same time, user behavior data is collected through websites, apps, social media and other means. The data sources are rich, which solves the problem of inaccurate advertising due to sparse user behavior data. In the process of data collection, storage and processing, the privacy protection principles are strictly observed, and encryption or anonymization technology is used to protect user personal information and strengthen privacy protection. By tracking users' online behavior in real time and obtaining their latest behavior data, changes in user behavior can be captured in real time.

[0072] S2 includes: using machine learning algorithms and data mining algorithms to conduct in-depth analysis of user behavior data, integrating users’ social relationships and geographic locations, and identifying users’ interests, consumption habits, and behavior patterns, including but not limited to:

[0073] Behavior sequence analysis: identifying user access paths and behavior patterns;

[0074] Preference analysis: Analyze users’ preferences for products, content, and activities based on their historical behaviors;

[0075] Time series analysis: Capture the changing trends of user behavior over time.

[0076] By integrating users’ social relationships and geographic locations to gain a more comprehensive understanding of user needs, we can quickly provide users with personalized recommendations and achieve real-time, accurate traffic delivery.

[0077] S3 includes: Building a multi-dimensional and three-dimensional user portrait based on the results of user behavior analysis.

[0078] User portraits include:

[0079] Basic attributes: age, gender, occupation, region;

[0080] Hobbies and interests: reading preferences, entertainment preferences, sports preferences;

[0081] Consumption habits: spending power, consumption frequency, brand preference;

[0082] Potential needs: purchase intention or possible future demand.

[0083] S4 includes: accurate matching based on user portraits and delivery targets, customized delivery strategies for different user groups, and introduction of personalized sorting algorithms to push advertisements or content to target users. Delivery strategies include:

[0084] Content customization: Customize advertising content to attract users’ attention based on their interests and preferences;

[0085] Time planning: Deliver during periods of high user activity;

[0086] Channel selection: select advertising display channels based on user active platforms;

[0087] Budget allocation: Allocate advertising budget reasonably based on the estimated advertising effect.

[0088] By utilizing other attribute information of new users or new items and using personalized sorting algorithms to push advertisements or content to target users, accurate recommendations can be provided for new users or new items, thereby improving the accuracy of traffic delivery and user satisfaction.

[0089] S5 includes: real-time monitoring of advertising effectiveness, setting key performance indicators, including but not limited to click-through rate, conversion rate and ROI, comprehensive evaluation of advertising effectiveness, and collection of user feedback.

[0090] S6 includes: iteratively upgrading the system based on effect evaluation and user feedback, optimizing and adjusting the delivery strategy based on the evaluation and analysis results using A / B testing and machine learning model methods to continuously improve delivery effects, including but not limited to optimizing advertising materials, adjusting delivery time periods and regions, and improving user portrait construction methods.

[0091] Iterative upgrades to the system can continuously optimize system algorithms, enhance the system's data processing capabilities, enrich user portrait dimensions, and improve the delivery strategy library.

[0092] like Figure 2 As shown, the present invention also provides a precise streaming system based on user behavior analysis, the system comprising

[0093] Data collection and processing module: used to collect user behavior data from various data sources and process the collected data, where the data sources include but are not limited to websites, apps, and social media, to provide high-quality data for subsequent user behavior analysis;

[0094] User behavior analysis module: conducts in-depth analysis of user behavior data based on machine learning algorithms and data mining algorithms to identify user characteristics and behavior patterns;

[0095] User portrait building module: used to build detailed user portraits based on user behavior analysis results, providing a basis for accurate matching;

[0096] Precision matching and delivery module: used to accurately match user profiles and delivery targets, and select appropriate delivery channels and timings for delivery;

[0097] Delivery effect evaluation module: used to evaluate the delivery effect of advertisements and collect user feedback;

[0098] Optimization module: used to optimize and adjust delivery strategies and system iteration upgrades.

[0099] In summary, by collecting historical behavior data of users, including but not limited to browsing history, click behavior, purchase history, and dwell time, and tracking users' online behavior in real time and obtaining the latest user behavior data, changes in user behavior can be captured in real time. User behavior data comes from data sources such as websites, apps, and social media. User behavior data is rich. Using weighting, dimensionality reduction, and feature selection techniques to process the collected data can optimize the data processing process, improve data density and quality, and solve the problem of inaccurate advertising due to sparse user behavior data. Processing includes but is not limited to cleaning, deduplication, standardization, and normalization to ensure data consistency and comparability. The processed data is then stored in a data warehouse. During data collection, storage and processing, we strictly abide by the privacy protection principle, use encryption or anonymization technology to protect user personal information, use machine learning algorithms and data mining algorithms to conduct in-depth analysis of user behavior data, and integrate users' social relationships and geographic locations to more comprehensively understand user needs, identify users' interests, hobbies, consumption habits and behavior patterns, and build multi-dimensional and three-dimensional user portraits based on user behavior analysis results. We accurately match user portraits with delivery targets, customize personalized delivery strategies for different user groups, and introduce personalized sorting algorithms to push advertisements or content to target users, achieving real-time and accurate delivery, and setting key performance indicators, including but not limited to click-through rate, conversion rate and RO I. Comprehensively evaluate the effectiveness of advertising delivery, monitor the effectiveness of advertising delivery in real time, and collect user feedback. Based on the effect evaluation and user feedback, the system is iteratively upgraded. According to the evaluation results and analysis results, the delivery strategy is optimized and adjusted through the optimization module. The delivery strategy is optimized and adjusted using the A / B test and machine learning model methods, which can quickly provide personalized recommendations for users. At the same time, the optimization module is also used for the iterative upgrade of the system, which can continuously optimize the system algorithm, improve the system's data processing capabilities, enrich the user portrait dimensions, and improve the delivery strategy library. In addition, other attribute information of new users or new items is used to provide accurate recommendations for new users or new items, improve the accuracy of delivery and user satisfaction. The above steps provide strong support for enterprises to achieve precision marketing. The implementation of this method and system will help enterprises improve the accuracy and efficiency of advertising delivery, reduce marketing costs, and ultimately enhance the market competitiveness of enterprises.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A precise traffic flow method based on user behavior analysis, characterized in that: The following steps are included: S1: Data collection and integration; S2: User behavior analysis; S3: Build user profiles; S4: Accurate matching and delivery; S5: Effect evaluation and user feedback; S6: Delivery optimization and system upgrade.

2. According to claim 1, a precise flow casting method based on user behavior analysis is characterized in that: S1: Data collection and integration, including the following steps: The S1 includes: collecting the user's historical behavior data, including browsing history, click behavior, purchase history, and dwell time, and tracking the user's online behavior in real time to obtain the latest user behavior data.

3. According to the method of claim 1, the precise flow casting method based on user behavior analysis is characterized in that: S2: User behavior analysis includes the following steps: Use machine learning algorithms and data mining algorithms to conduct in-depth analysis of user behavior data to identify users' interests, hobbies, consumption habits, and behavior patterns, including behavior sequence analysis, preference analysis, and time series analysis; The behavior sequence analysis includes identifying user access paths and behavior patterns; The preference analysis includes analyzing the user's preferences for goods, content, and activities based on the user's historical behavior; The time series analysis includes capturing the changing trends of user behaviors over time.

4. According to claim 1, a precise flow-casting method based on user behavior analysis is characterized in that: S3: Building a user portrait includes the following steps: Build a multi-dimensional and three-dimensional user portrait based on the results of user behavior analysis; The user profile includes basic attributes, interests and hobbies, consumption habits, and potential needs; The basic attributes include age, gender, occupation, and region; The interests and hobbies include reading preferences, entertainment preferences, and sports preferences; The consumption habits include consumption capacity, consumption frequency, and brand preference; The potential needs include purchase intentions or possible future demands.

5. According to claim 1, a precise flow casting method based on user behavior analysis is characterized in that: S4: accurate matching and delivery, includes the following steps: Accurately match user profiles with delivery targets, customize personalized delivery strategies for different user groups, and push advertisements or content to target users. The delivery strategies include content customization, time planning, channel selection, and budget allocation; The content customization includes customizing the advertising content that attracts the user's attention according to the user's interest preferences; The time planning includes delivering the content during the time when the user activity is high; The channel selection includes selecting an advertisement display channel according to the user's active platform; The budget allocation includes reasonably allocating the advertising budget based on the estimated advertising effect.

6. According to claim 1, a precise flow casting method based on user behavior analysis is characterized in that: S5: effect evaluation and user feedback, including the following steps: Monitor advertising effectiveness in real time and set key performance indicators, including click-through rate, conversion rate and ROI.

7. According to claim 1, a precise flow casting method based on user behavior analysis is characterized in that: The S6: delivery optimization and system upgrade includes the following steps: Based on effect evaluation and user feedback, the system is iteratively upgraded. According to the evaluation and analysis results, the A / B testing and machine learning model methods are used to optimize and adjust the delivery strategy, including optimizing advertising materials, adjusting delivery time and region, and improving user portrait construction.

8. A precise flow-casting system based on user behavior analysis, performing a precise flow-casting method based on user behavior analysis as described in any one of claims 1 to 7, characterized in that: include Data collection and processing module, used to collect user behavior data from various data sources and process the collected data, where the data sources include but are not limited to websites, apps, and social media; User behavior analysis module, which conducts in-depth analysis of user behavior data based on machine learning algorithms and data mining algorithms to identify user characteristics and behavior patterns; User portrait building module, used to build detailed user portraits based on user behavior analysis results; The precise matching and delivery module is used to accurately match user portraits and delivery targets, and select appropriate delivery channels and timings for delivery; The delivery effect evaluation module is used to evaluate the delivery effect of advertisements and collect user feedback; The optimization module is used to optimize and adjust the delivery strategy and system iterative upgrades.

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