Map operation method and system based on user strategy

Abstract

The invention discloses a user strategy-based map operation method and system. The method comprises the steps of constructing a crowd strategy map tree based on user multi-dimensional data; setting a multi-scene personalized operation strategy according to the crowd strategy map tree; and carrying out operation analysis on user behavior data collected by each key node of the platform according to the multi-scene personalized operation strategy, and carrying out operation optimization according to an analysis result. Precise marketing and personalized service are realized, the conversion rate and the user value are improved, and continuous growth of platform business is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of user operation and data analysis, and particularly to an operation method and system based on a user strategy map. Background Art

[0002] In the digital age, the operation of users on Internet platforms has received much attention. However, the current operation mode faces many challenges. On the one hand, the business strategy logic often starts from the perspective of the crowd, which often conflicts with the characteristic of the algorithm of "one person, one face" without grouping, resulting in the difficulty of accurately implementing the operation strategy. On the other hand, after the new algorithm is developed, it needs to be synchronized to the algorithm platform before it can be used, and the process is cumbersome and the implementation efficiency is low. At the same time, in terms of data verification, the accuracy and stability are insufficient, and once the algorithm is implemented, it is fully covered, and the effect cannot be verified in different versions, making it difficult to evaluate and optimize the strategy. In addition, most of the existing algorithm platforms are limited to the content recommendation service logic, and important factors such as price strategies are often excluded from the framework, making it impossible to achieve comprehensive operation optimization.

[0003] In the existing operation technology, the differences between the business strategy and the algorithm logic make it difficult to coordinate and unify in actual operation, and it is impossible to give full play to the advantages of both, affecting the user experience and operation effect. Moreover, the limitations of the functions of the algorithm platform result in the fact that the operation strategy cannot cover key factors such as price, making it impossible to achieve all-round user operation management.

[0004] Therefore, the existing technology still needs to be improved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that, aiming at the defects of the existing technology, the present invention provides an operation method and system based on a user strategy map to solve the problem that the existing operation management method cannot achieve all-round user operation management.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows: In a first aspect, the present invention provides an operation method based on a user strategy map, including: Constructing a population strategy map tree based on multi-dimensional user data; Setting multi-scenario personalized operation strategies according to the population strategy map tree; Performing operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategy, and performing operation optimization according to the analysis result.

[0007] In one implementation, the constructing a population strategy map tree based on multi-dimensional user data includes: Collecting the multi-dimensional user data, and constructing a user life cycle hierarchical model according to the multi-dimensional user data; Develop detailed segmentation criteria for users in different lifecycle stages, where the users in different lifecycle stages include: potential users, novice users, growing users, mature users, declining users, and churned users. Based on the user lifecycle segmentation model, segment the users in different lifecycle stages according to the segmentation criteria, continuously adjust and optimize the algorithm model parameters, and verify the behavioral feature identifiers corresponding to the node populations through cross-validation methods to obtain the population strategy map tree.

[0008] In one implementation, the collecting of the multi-dimensional user data and the construction of the user lifecycle segmentation model based on the multi-dimensional user data include: Collect the user's registration information, browsing behavior, consumption behavior, and device and environment information to obtain the multi-dimensional user data; Clean, denoise, and perform format conversion processing on the multi-dimensional user data, and use the principal component analysis method and the independent component analysis method to extract key features to construct a user behavior feature vector; Based on the user behavior feature vector, use a combination of clustering analysis algorithms and classification algorithms to divide users into different lifecycle stages to obtain the user lifecycle segmentation model.

[0009] In one implementation, the setting of the multi-scenario personalized operation strategy according to the population strategy map tree includes: Construct a unified operation scenario management platform, comprehensively incorporate various operation scenarios into the management system, and establish a mapping relationship database between scenarios and populations within the platform. According to the population classification in the population strategy map tree, preset default strategies and personalized strategy templates for each scenario; Build an automated recommendation system based on a hybrid recommendation algorithm, and integrate content-based recommendation algorithms, collaborative filtering recommendation algorithms, and deep learning recommendation algorithms in the automated recommendation system; Generate the multi-scenario personalized operation strategy based on the collaboration of the operation scenario management platform and the automated recommendation system.

[0010] In one implementation, the generating of the multi-scenario personalized operation strategy based on the collaboration of the operation scenario management platform and the automated recommendation system includes: Generate the scenario information and strategy guidance required by the automated recommendation system based on the operation scenario management platform, and determine the recommended scenario context information and target population strategies; Based on the automated recommendation system, perform data cleaning and feature engineering on the user behavior data and scenario context information collected in real time, and calculate the interest scores of users for different contents or products using the content recommendation algorithm, the collaborative filtering recommendation algorithm, and the deep learning recommendation algorithm. Screen out the recommended items with higher scores and provide corresponding recommended content for each scenario.

[0011] In one implementation, the operation analysis of the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategy, and the operation optimization according to the analysis results, include: Collect user behavior data, system performance data, business operation data, and external environment data; Establish a monitoring index system and an analysis model based on the user behavior data, the system performance data, the business operation data, and the external environment data; Based on the monitoring index system and the analysis model, monitor multi-dimensional and multi-level monitoring indexes; Based on the monitoring index system and the analysis model, use time series analysis algorithms to predict user behavior trends, use association rules to mine the potential relationships between user behaviors and business indicators, identify abnormal user groups through clustering analysis algorithms, construct a user behavior funnel model, deeply analyze the reasons for user loss at different business process stages, use machine learning algorithms to predict user loss, and generate visual reports and warning information according to the analysis results; Optimize the page layout and interaction design, adjust the recommendation algorithm parameters and strategies, launch personalized marketing activities, and improve product or service functions according to the monitoring and analysis results of the monitoring index system and the analysis model.

[0012] In one implementation, the monitoring of multi-dimensional and multi-level monitoring indexes based on the monitoring index system and the analysis model includes: When abnormal fluctuations of indicators are detected or warning information is triggered, quickly locate the root cause of the problem from multiple dimensions of population, scenario, and time through data backtracking and correlation analysis techniques, and formulate corresponding operation strategy adjustment plans according to the problem location results.

[0013] In a second aspect, the present invention provides an operation system based on a user strategy map, including: A population strategy map tree construction module for constructing a population strategy map tree based on user multi-dimensional data; A multi-scenario personalized operation strategy module for setting multi-scenario personalized operation strategies according to the population strategy map tree; An operation analysis and strategy optimization module is used to perform operation analysis on user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategy, and perform operation optimization according to the analysis results.

[0014] In a third aspect, the present invention provides a terminal, including: a processor and a memory, where the memory stores an operation program based on a user strategy map, and when the operation program based on the user strategy map is executed by the processor, it is used to implement the operations of the operation method based on the user strategy map as described in the first aspect.

[0015] In a fourth aspect, the present invention further provides a medium, which is a computer-readable storage medium, and the medium stores an operation program based on a user strategy map. When the operation program based on the user strategy map is executed by a processor, it is used to implement the operations of the operation method based on the user strategy map as described in the first aspect.

[0016] The present invention adopts the above technical solutions and has the following effects: The present invention constructs a population strategy map tree based on user multi-dimensional data, can set a multi-scenario personalized operation strategy according to the population strategy map tree, and then perform operation analysis on user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategy, and perform operation optimization according to the analysis results. The present invention realizes precise marketing and personalized services, improves the conversion rate and user value, promotes the continuous growth of the platform business, improves the operation efficiency, optimizes the resource allocation, and reduces the operation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the structures shown in these drawings without creative efforts.

[0018] Figure 1 is a flowchart of the operation method based on the user strategy map in the present invention.

[0019] Figure 2 is a framework diagram of the operation based on the user strategy map in the present invention.

[0020] Figure 3 is an adjustment schematic diagram of the multi-scenario personalized operation strategy in the present invention.

[0021] Figure 4 is a functional schematic diagram of the terminal in an implementation manner of the present invention.

[0022] The implementation, functional features, and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer and more explicit, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0024] Exemplary method Problems still existing in existing user operation and data analysis technologies: 1) Difficulties in coordinating operation strategies: The differences between business strategies and algorithmic logics make it difficult to coordinate and integrate in actual operation, unable to fully utilize the advantages of both, and affecting user experience and operation effects.

[0025] 2) Lag in innovation and iteration: The cumbersome algorithm online process makes the application of new strategies and algorithms lag, difficult to respond to market changes and user needs in a timely manner, resulting in a decline in platform competitiveness.

[0026] 3) Limited data-driven decision-making: Insufficient data verification and the way of full-scale coverage make operation decisions lack accurate data support, easily causing waste of resources and strategic mistakes.

[0027] 4) Incomplete operation framework: The limitations of the algorithm platform functions result in operation strategies being unable to cover key factors such as price, and unable to achieve all-round user operation management.

[0028] In view of the above technical problems, an operation method based on a user strategy map is provided in an embodiment of the present invention. This method mainly constructs a population strategy map tree based on multi-dimensional user data, can set multi-scenario personalized operation strategies according to the population strategy map tree, and then perform operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategies, and perform operation optimization according to the analysis results. The embodiment of the present invention can achieve precision marketing and personalized services, improve conversion rates and user value, promote the continuous growth of platform business, improve operation efficiency, optimize resource allocation, and reduce operation costs.

[0029] As Figure 1 shown, an embodiment of the present invention provides an operation method based on a user strategy map, including the following steps: Step S100, constructing a population strategy map tree based on multi-dimensional user data.

[0030] In this embodiment, the user strategy map operation method is a user strategy map operation method based on user life cycle stratification and personalized precise delivery; this method realizes precise collaborative operation, improves the algorithm online efficiency, quickly responds to market and user needs, and promotes the innovation and iteration of operation strategies by establishing a unified user strategy framework and integrating business strategies and algorithm logics; and realizes data-driven refined operation decisions, expands the operation framework, incorporates multi-dimensional strategies such as price, and realizes comprehensive user operation management by constructing a perfect data monitoring and verification system.

[0031] To solve the problem that the traditional user stratification method is too simple to accurately reflect the complex needs and behavior characteristics of users in different stages, resulting in the lack of pertinence of operation strategies, in this embodiment, a population strategy map tree is first constructed to comprehensively and deeply analyze user behavior characteristics and construct an accurate user portrait, providing a basis for subsequent multi-scenario personalized operation strategies.

[0032] Specifically, in an implementation manner of this embodiment, step S100 includes the following steps: Step S101, collect the multi-dimensional data of the user, and construct a user life cycle stratification model according to the multi-dimensional data of the user.

[0033] In an implementation manner of this embodiment, the collecting the multi-dimensional data of the user and constructing a user life cycle stratification model according to the multi-dimensional data of the user includes: collecting the registration information, browsing behavior, consumption behavior, and device and environment information of the user to obtain the multi-dimensional data of the user; cleaning, denoising, and format conversion processing on the multi-dimensional data of the user, and using the principal component analysis method and the independent component analysis method to extract key features to construct a user behavior feature vector; based on the user behavior feature vector, using a combination of clustering analysis algorithms and classification algorithms to divide users into different life cycle stages to obtain the user life cycle stratification model.

[0034] In this embodiment, the process of constructing the user life cycle stratification model is as follows: First, widely collect the multi-dimensional data of the user, covering registration information (such as registration time, channel, initial settings, etc.), browsing behavior (such as browsing content, duration, frequency, browsing path, etc.), interaction behavior (such as like, comment, share, favorite, vote, participate in topic discussions, etc.), consumption behavior (such as consumption amount, consumption times, consumption time interval, types of purchased goods or services, etc.), and device and environment information (such as device type, operating system version, network access method, geographical location, etc.).

[0035] Then, perform preprocessing operations such as data cleaning, denoising, and format conversion on the collected data to ensure the accuracy and usability of the data. Use dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA) to extract key features and construct user behavior feature vectors.

[0036] Finally, based on the user behavior feature vectors, use a combination of clustering analysis (e.g., improved algorithms such as K-Means++, DBSCAN, etc.) and classification algorithms (e.g., support vector machines, random forests, etc.) to divide users into different life cycle stages; for example, potential users (newly registered but without meaningful operations), novice users (initially exploring platform functions), growing users (increasing activities on the platform and having certain consumption or interactions), mature users (using the platform frequently and stably with high loyalty), declining users (significantly decreasing usage frequency and activity), and churned users (not active for a long time). Within each stage, further segment according to specific user behavior patterns. For example, growing users can be divided into multiple sub-groups based on the concentration of browsing content themes (e.g., the degree of focus in fields such as technology, entertainment, and life), the growth trend of consumption amount (e.g., linear growth, exponential growth, etc.), and the types of interaction behaviors (e.g., professionalism of comments, sharing frequency, and object range, etc.), forming a tree-like population classification system.

[0037] Step S102, formulate detailed segmentation criteria for users in different life cycle stages; wherein, the users in different life cycle stages include: potential users, novice users, growing users, mature users, declining users, and churned users.

[0038] In this embodiment, based on the constructed user life cycle hierarchical model, design the segmentation criteria and methods for population nodes, specifically as follows: Formulate detailed segmentation criteria for users in different life cycle stages. Potential users can be initially classified according to the registration source (such as advertising promotion, partner recommendation, natural search, etc.) and the category of the first viewed content (such as news and information, entertainment videos, e-commerce products, etc.); novice users are segmented based on the type of the first interaction behavior (such as liking news, commenting on videos, collecting products, etc.) and the depth of function usage (such as whether to use advanced search, whether to try personalized recommendation functions, etc.); growing users are classified with reference to the diversity of the browsing content themes (the number and depth of involved fields), consumption upgrade behaviors (purchasing higher-value goods or services), and interactive social behaviors (whether to participate in community activities, whether to follow other users, etc.); mature users are segmented according to loyalty indicators (such as continuous subscription duration, repeat purchase frequency, membership level, etc.), social influence (the number of fans, the number of citations, the scope of content dissemination, etc.); declining users are classified according to the rate of decrease in activity (such as the reduction ratio of weekly usage duration, the reduction amplitude of monthly operation times, etc.), recent consumption changes (decrease in consumption amount, lengthening of consumption interval, etc.); lost users are judged according to the duration of loss (short-term loss, long-term loss), and the behavior pattern before loss (such as frequently canceling orders, losing after reducing interactions, etc.). During the segmentation process, continuously adjust and optimize the algorithm model parameters, and ensure that the node populations have clear and unique behavior characteristic identifiers through methods such as cross-validation, providing accurate target positioning for precise operation.

[0039] Step S103, based on the user life cycle stratification model, segment the users in different life cycle stages according to the segmentation criteria, continuously adjust and optimize the algorithm model parameters, and verify the behavior characteristic identifiers corresponding to the node populations through the cross-validation method to obtain the population strategy map tree.

[0040] In this embodiment, the multi-dimensional user data is the cornerstone for constructing the stratification model, providing rich materials for feature extraction. Data preprocessing is a key link to ensure data quality, ensuring the accuracy of subsequent feature extraction and model calculation. The extracted feature vectors are used as the input of clustering and classification algorithms, and the user life cycle stage and segmentation nodes are determined through complex calculations. The population classification of different stages and nodes provides a precise basis for formulating subsequent personalized strategies. Each part collaborates closely to jointly form the population strategy map tree, realizing the refined stratification and precise positioning of users.

[0041] In this embodiment, by designing the population strategy map tree, the precise stratification of users is realized, deeply understanding the user needs and behavior change trends, providing a solid foundation for formulating personalized operation strategies, and significantly improving the operation effect. At the same time, the scientific and reasonable stratification and segmentation system in the population strategy map tree helps to timely capture the signals of user behavior changes, adjust the operation strategy in advance, effectively enhance user stickiness and loyalty, and reduce the risk of user loss.

[0042] Such asFigure 1 As shown in the figure, an embodiment of the present invention provides a method for operating based on a user strategy map, including the following steps: Step S200, set multi-scenario personalized operation strategies according to the population strategy map tree.

[0043] To solve the problems of the lack of effective integration and coordination between different operation scenarios, unreasonable allocation of operation scenario resources, and the lack of the ability to dynamically adjust according to user needs and behaviors, this embodiment sets multi-scenario personalized operation strategies based on the population strategy map tree.

[0044] Specifically, in an implementation manner of this embodiment, step S200 includes the following steps: Step S201, construct a unified operation scenario management platform, comprehensively incorporate various operation scenarios into the management system, and establish a mapping relationship database between scenarios and populations within the platform. According to the population classification in the population strategy map tree, preset default strategies and personalized strategy templates for each scenario.

[0045] In this embodiment, an operation scenario integration and strategy association framework can be built based on the population strategy map tree. Specifically: Construct a unified operation scenario management platform, and comprehensively incorporate various operation scenarios, including sections (such as news sections, video sections, e-commerce sections, etc.), layouts (home page layout, special topic layout, channel layout, etc.), home page tabs (recommended tab, followed tab, popular tab, etc.), screensavers, channel lists, search pages (text search, voice search), etc. into the management system. Establish a mapping relationship database between scenarios and populations within the platform. According to the population classification in the population strategy map tree, preset default strategies and personalized strategy templates for each scenario.

[0046] For example, for the news section, display hot news headlines and pop-up windows for guiding registration to potential users; recommend in-depth reports, personalized news topics, and exclusive membership rights to mature users. On the search page, optimize the search result ranking and recommend relevant content according to the user's historical search keywords, population characteristics, and the current search scenario context (such as time, location, etc.). Realize real-time information sharing and collaborative linkage between scenarios through technologies such as message queues and event-driven architectures. When a user has an action in one scenario (such as browsing news, purchasing goods, searching for information, etc.), quickly trigger the strategy adjustment of other relevant scenarios to ensure that users can obtain a consistent and personalized service experience in different scenarios.

[0047] Step S202, build an automated recommendation system based on a hybrid recommendation algorithm, and integrate a content-based recommendation algorithm, a collaborative filtering recommendation algorithm, and a deep learning recommendation algorithm in the automated recommendation system; In this embodiment, in addition to building an operation scenario management platform, it is also necessary to build an automated recommendation system based on a hybrid recommendation algorithm, and integrate a content-based recommendation algorithm (analyzing the characteristics of user browsing and consumption content, such as keywords, categories, styles, etc.), a collaborative filtering recommendation algorithm (finding user groups with similar interests based on user behavior similarity and recommending the content they like), and a deep learning recommendation algorithm (using neural networks to mine users' potential interests, such as analyzing image content based on convolutional neural networks to recommend relevant products, or processing user behavior sequences based on recurrent neural networks to predict the next content that users may be interested in) in this system.

[0048] In this embodiment, by collecting user behavior data in real time (including behaviors such as browsing, clicking, favoriting, purchasing, commenting, etc.) and scenario context information (such as current time, location, device status, network environment, etc.); then, inputting these data into the recommendation system, after data cleaning and feature engineering (such as feature extraction, feature transformation, feature selection, etc.) processing, using the recommendation algorithm to calculate the interest scores of users for different content or products, and screening out the recommended items with higher scores. The recommendation results are quickly pushed to the corresponding scenarios through a content delivery network (CDN) and presented to users. At the same time, a feedback mechanism is established to adjust the recommendation algorithm parameters in real time according to the feedback of users on the recommended content (such as click-through rate, dwell time, whether to purchase, whether to favorite, etc.), continuously optimize the recommendation effect, and improve the accuracy of the recommendation and user satisfaction.

[0049] Step S203, generating the multi-scenario personalized operation strategy based on the collaboration of the operation scenario management platform and the automated recommendation system.

[0050] In an implementation manner of this embodiment, the generating the multi-scenario personalized operation strategy based on the collaboration of the operation scenario management platform and the automated recommendation system includes: generating the scenario information and strategy guidance required by the automated recommendation system based on the operation scenario management platform, and determining the recommended scenario context information and target population strategy; based on the automated recommendation system, performing data cleaning and feature engineering processing on the user behavior data and scenario context information collected in real time, and using the content-based recommendation algorithm, the collaborative filtering recommendation algorithm, and the deep learning recommendation algorithm to calculate the interest scores of users for different content or products, screening out the recommended items with higher scores, and providing corresponding recommended content for each scenario.

[0051] In this embodiment, the multi-scenario personalized operation strategy is jointly generated based on the operation scenario management platform and the automated recommendation system. The operation scenario management platform provides scenario information and strategy guidance for the automated recommendation system, clarifying the recommended scenario context and target population strategy. The recommendation system provides accurate recommendation content for the scenario according to user behavior and scenario context information, improving the user experience in the scenario. Through efficient data interaction and collaborative working mechanisms, the two achieve multi-scenario personalized strategy operation, improving user satisfaction and loyalty, and enhancing the overall operation efficiency and competitiveness of the platform.

[0052] In this embodiment, by designing a multi-scenario personalized operation strategy, a cross-scenario consistent and highly personalized user experience is provided, meeting the diverse needs of users, effectively improving user satisfaction and loyalty, and reducing user churn. Optimize the allocation of operation scenario resources, improve resource utilization efficiency, promote the coordinated development of various business segments of the platform, and enhance the overall operation efficiency and market competitiveness of the platform.

[0053] As Figure 1 shown, an embodiment of the present invention provides a user strategy map-based operation method, including the following steps: Step S300, perform operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategy, and perform operation optimization according to the analysis results.

[0054] To solve the problems that the existing data monitoring system has incomplete and untimely data collection, insufficient depth and accuracy of data analysis, inability to accurately reflect user behavior changes and operation problems in real time, and lack of an effective problem positioning and strategy tuning mechanism, making it difficult to quickly find the root cause of the problem and formulate targeted solutions. In this embodiment, by constructing a data collection and integration system, establishing a monitoring index system and an analysis model, and designing a problem positioning and strategy tuning mechanism, real-time, comprehensive, and accurate data monitoring is achieved, and user behavior changes and operation problems are captured in a timely manner.

[0055] Specifically, in an implementation manner of this embodiment, step S300 includes the following steps: Step S301, collect user behavior data, system performance data, business operation data, and external environment data.

[0056] In this embodiment, the data collection and integration system is constructed as follows: High-performance data collectors are deployed at each key node of the platform to comprehensively collect user behavior data (such as page access trajectories, operation behavior sequences, residence time distributions, operation frequencies, etc.), system performance data (such as server response time, throughput, concurrent connection number, bandwidth utilization rate, etc.), business operation data (such as order quantity, sales amount, conversion rate, average customer price, user growth number, etc.), and external environment data (such as market trends, competitor dynamics, changes in industry policies and regulations, etc.). The collected data is transmitted to the data center in real time through a reliable data transmission interface.

[0057] In the data center, advanced data cleaning tools are used to remove invalid, duplicate, and incorrect data. Data fusion technology is applied to associate and integrate data from different sources and in different formats according to dimensions such as users, time, scenarios, and businesses, to build a unified and complete data warehouse. At the same time, a strict data quality management system is established to regularly evaluate and optimize data quality to ensure the accuracy, integrity, and consistency of the data.

[0058] Step S302, establish a monitoring index system and an analysis model based on the user behavior data, the system performance data, the business operation data, and the external environment data; Step S303, monitor multi-dimensional and multi-level monitoring indicators based on the monitoring index system and the analysis model; Step S304, based on the monitoring index system and the analysis model, use time series analysis algorithms to predict user behavior trends, use association rules to mine the potential relationships between user behaviors and business indicators, identify abnormal user groups through clustering analysis algorithms, construct a user behavior funnel model, deeply analyze the reasons for user loss at different stages of the business process, use machine learning algorithms to predict user loss, and generate visual reports and warning information according to the analysis results.

[0059] In this embodiment, the monitoring index system and the analysis model are established as follows: Establish a multi-dimensional and multi-level monitoring index system, covering user activity indicators (such as daily active users, monthly active users, average usage duration, daily average operation times, distribution of active time periods, etc.), user retention indicators (such as next-day retention rate, 7-day retention rate, 30-day retention rate, retention curve analysis, etc.), user conversion indicators (such as registration conversion rate, login conversion rate, purchase conversion rate, subscription conversion rate, key process conversion rate, etc.), business performance indicators (such as total revenue, net profit, gross profit margin, market share, user acquisition cost, user lifetime value, etc.), and user experience indicators (such as page loading speed, page error rate, interaction fluency, satisfaction survey score, complaint rate, etc.). According to the characteristics of different populations and scenarios, the indicators are segmented and weighted to accurately reflect the actual situation under different user groups and operation scenarios.

[0060] Use time series analysis (such as ARIMA model, exponential smoothing method, etc.) to predict user behavior trends, adopt association rule mining (such as Apriori algorithm, FP-Growth algorithm, etc.) to discover potential relationships between user behavior and business indicators, and identify abnormal user groups through clustering analysis (such as K-Means clustering, hierarchical clustering, etc.). Construct a user behavior funnel model to deeply analyze the reasons for user loss at different stages of business processes, and use machine learning algorithms (such as decision trees, random forests, logistic regression, etc.) to predict user loss. Generate visual reports and warning information based on the analysis results to provide intuitive and comprehensive data support for operation decisions.

[0061] Step S305, optimize the page layout and interaction design, adjust the recommendation algorithm parameters and strategies, launch personalized marketing activities, and improve the product or service functions according to the monitoring and analysis results of the monitoring index system and the analysis model.

[0062] In an implementation manner of this embodiment, the monitoring of multi-dimensional and multi-level monitoring indicators based on the monitoring index system and the analysis model includes: when abnormal fluctuations in indicators or warning information are detected, quickly locate the root cause of the problem from multiple dimensions of population, scenario, and time through data backtracking and correlation analysis techniques, and formulate corresponding operation strategy adjustment plans according to the problem location results.

[0063] In this embodiment, based on the monitoring index system and the analysis model, a problem location and strategy optimization mechanism is also designed, specifically: When abnormal fluctuations in indicators are detected or warning messages are triggered, immediately initiate the problem - location process. Through data backtracking and correlation analysis techniques, quickly locate the root cause of the problem from multiple dimensions such as population, scenario, and time. For example, if it is found that the conversion rate of a certain type of user decreases in a specific scenario, deeply analyze whether it is due to changes in user behavior patterns (such as interest transfer, usage habit changes, etc.), the impact of competitor strategies (such as launching similar products or services, price competition, etc.), or operational strategy issues (such as the failure of the recommendation algorithm, unreasonable page layout, poor marketing campaign effectiveness, etc.).

[0064] According to the problem - location results, formulate a targeted strategy - adjustment plan. Such as optimizing page layout and interaction design, adjusting recommendation algorithm parameters and strategies, launching personalized marketing campaigns, improving product or service functions, etc. Conduct A / B testing or gray - scale release in a small - scale user group, compare the effects of different strategy versions, and verify the effectiveness of strategy adjustments. After optimizing the strategy according to the test results, roll it out in full scale and continuously monitor the effects, forming a closed - loop strategy - optimization cycle to continuously improve the scientific nature and effectiveness of operational strategies.

[0065] The data - collection and integration system in this embodiment can provide an accurate and comprehensive data basis for the monitoring indicator system, which is the source and guarantee of the entire data monitoring. The monitoring indicator system uses analysis models to deeply process and analyze data, converting raw data into valuable information and decision - making basis. The problem - location and strategy - optimization mechanism is based on the monitoring results, locates problems through data correlation and in - depth analysis, formulates and optimizes strategies, and each part cooperates with each other closely to achieve data - driven refined operation management.

[0066] This embodiment can achieve real - time, comprehensive, and accurate data monitoring, promptly capture changes in user behavior and operational problems, provide timely and accurate data support for operational decision - making, significantly improve decision - making efficiency and scientific nature; and, by deeply mining the information behind user behavior and business data, accurately locate the root cause of problems, formulate effective strategy - adjustment plans, continuously optimize operational effects, and enhance platform profitability, user satisfaction, and market competitiveness.

[0067] As Figure 2 shown, in the actual application scenario of the solution of this embodiment, the user - strategy - map operation method based on user - lifecycle stratification and personalized precise placement is as follows: 1) Input the user's voice behavior, search behavior, play behavior, probe packets, and through the probe engine, output the member - detection list and interest - detection list; 2) Input the user's historical behavior, voice behavior, play behavior, content packets, and through the recommendation engine, output the long - term interest list, real - time actor list, real - time interest list, and real - time style list; Both the above-mentioned detection engine and recommendation engine can be regulated by key factors at the program regulation level and key factors at the operation regulation level.

[0068] 3) Input the member detection list, interest detection list, long-term interest list, real-time actor list, real-time interest list, and real-time style list into the operation orchestration system. At the same time, input time information, geographical information, service information, and policy information. In the operation orchestration system, through the configured algorithms and policies, combined with interpretable recommendation and user granularity orchestration mechanisms, output the operation optimization results (such as, home screen recommendation, feed recommendation, Tab recommendation, discovery service, associated recommendation, advertisement recommendation, care service).

[0069] Such as Figure 3 As shown, in the actual application scenario of the solution of this embodiment, the ways of operation strategy adjustment are mainly as follows: 1) Data monitoring: metrics / service flow, data monitoring, data inspection, data decision-making, diagnostic report; 2) Problem discovery, operation experiment: multi-objective optimization experiment, population and content experiment, population and recommendation flow orchestration experiment, forced intervention experiment; 3) Operation strategy optimization, strategy intervention / experience precipitation: hierarchical strategy configuration / strategy map display (such as, content features, population strategies, recall strategy re-rank strategy); 4) Auxiliary portrait construction: user life cycle stratification, user usage habit stratification, user membership status stratification, user family structure stratification.

[0070] In this embodiment, the alternative solution to the above user strategy map operation method based on user life cycle stratification and personalized precise delivery is: User data management and operation framework based on blockchain: Utilize the decentralized, immutable, and traceable characteristics of blockchain technology to construct a user data management and operation framework. User data is independently controlled by users and authorized for platform use to ensure data security and privacy protection. Under this framework, user life cycle stratification, personalized precise delivery, and operation strategy management are realized, improving user trust and participation.

[0071] Operation optimization solution empowered by quantum computing: Explore the application of quantum computing in user behavior analysis, recommendation algorithm optimization, and operation strategy decision-making. Utilize the powerful computing power of quantum algorithms to process large-scale and complex user data, achieve faster and more precise user stratification and personalized recommendation, and improve operation efficiency and effect.

[0072] The alternative solution for the core technical features is: Improvement of Reinforcement Learning Recommendation Algorithm: Adopt deep reinforcement learning algorithms, such as Deep Q-Network (DQN) or Policy Gradient (PG), to replace the existing hybrid recommendation algorithm. By enabling the recommendation system to continuously learn and optimize the recommendation strategy during the interaction with users, adjust the recommended content according to the real-time feedback and long-term behavior patterns of users, and improve the adaptability and accuracy of recommendations.

[0073] Expansion of User Portrait Based on IoT Data: Introduce data collected by IoT devices, such as smart home device usage data, wearable device health data, etc., to enrich the dimensions of the user portrait. Combine this IoT data with traditional user data to more comprehensively understand users' living habits, health conditions, consumption scenarios, etc., provide a richer basis for user lifecycle stratification and personalized precise delivery, and further enhance the pertinence of operation strategies.

[0074] This embodiment achieves the following technical effects through the above technical solutions: This embodiment constructs a population strategy map tree based on multi-dimensional user data, can set multi-scenario personalized operation strategies according to the population strategy map tree, then perform operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategies, and perform operation optimization according to the analysis results. This embodiment can achieve precise marketing and personalized services, improve conversion rates and user value, promote the continuous growth of platform business, improve operation efficiency, optimize resource allocation, and reduce operation costs.

[0075] Exemplary Device Based on the above embodiment, the present invention also provides an operation system based on a user strategy map, including: A population strategy map tree construction module for constructing a population strategy map tree based on multi-dimensional user data; A multi-scenario personalized operation strategy module for setting multi-scenario personalized operation strategies according to the population strategy map tree; An operation analysis and strategy optimization module for performing operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategies, and performing operation optimization according to the analysis results.

[0076] This embodiment achieves the following technical effects through the above technical solutions: This embodiment constructs a population strategy map tree based on multi-dimensional user data, can set multi-scenario personalized operation strategies according to the population strategy map tree, then perform operation analysis on the user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategies, and perform operation optimization according to the analysis results. This embodiment can achieve precise marketing and personalized services, improve conversion rates and user value, promote the continuous growth of platform business, improve operation efficiency, optimize resource allocation, and reduce operation costs.

[0077] Based on the above embodiments, the present invention further provides a terminal, and its principle block diagram can be as Figure 4 shown.

[0078] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected through a system bus; wherein, the processor of the terminal is used to provide computing and control capabilities; the memory of the terminal includes a storage medium and an internal memory; the storage medium stores an operating system and a computer program; the internal memory provides an environment for the operation of the operating system and the computer program in the storage medium; the interface is used to connect external devices; the display screen is used to display corresponding information; the communication module is used to communicate with a cloud server or other devices.

[0079] When the computer program is executed by the processor, it is used to implement the operations of the operation method based on the user policy map.

[0080] Those skilled in the art can understand that Figure 4 the principle block diagram shown in

[0081] merely shows the block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0082] In one embodiment, a terminal is provided, which includes: a processor and a memory. The memory stores an operation program based on the user policy map. When the operation program based on the user policy map is executed by the processor, it is used to implement the operations of the operation method based on the user policy map as above.

[0083] Those of ordinary skill in the art can understand that all or part of the processes in the above embodiments of the method can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile storage medium. When the computer program is executed, it can include the processes of the above embodiments of each method. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and volatile memories.

[0084] In summary, the present invention provides a method and system for operating based on a user strategy map, including: constructing a population strategy map tree based on multi-dimensional user data; setting multi-scenario personalized operation strategies according to the population strategy map tree; performing operation analysis on user behavior data collected at each key node of the platform according to the multi-scenario personalized operation strategies, and performing operation optimization according to the analysis results. The present invention realizes precise marketing and personalized services, improves conversion rates and user value, and promotes the continuous growth of platform business.

[0085] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. An operation method based on a user strategy map, characterized in that: include: Build a crowd strategy map tree based on multi-dimensional user data; Set multi-scenario personalized operation strategies according to the crowd strategy map tree; According to the multi-scenario personalized operation strategy, operational analysis is performed on the user behavior data collected from each key node of the platform, and operational optimization is performed based on the analysis results.

2. The user strategy map-based operation method according to claim 1, characterized in that: The crowd strategy map tree is constructed based on multi-dimensional user data, including: Collecting the multi-dimensional data of the user, and building a user life cycle hierarchical model based on the multi-dimensional data of the user; Develop detailed segmentation criteria for users at different life cycle stages; where users at different life cycle stages include: potential users, novice users, growing users, mature users, declining users, and lost users; Based on the user life cycle hierarchical model, users at different life cycle stages are segmented according to the segmentation basis, algorithm model parameters are continuously adjusted and optimized, and behavioral feature identifiers corresponding to node populations are verified through a cross-validation method to obtain the population strategy map tree.

3. The user strategy map-based operation method according to claim 2 is characterized in that: The collecting of the user multi-dimensional data and constructing a user life cycle hierarchical model according to the user multi-dimensional data includes: Collect the user's registration information, browsing behavior, consumption behavior, and device and environment information to obtain multi-dimensional data of the user; Cleaning, denoising and format conversion are performed on the multi-dimensional data of the user, and key features are extracted using principal component analysis and independent component analysis to construct a user behavior feature vector; Based on the user behavior feature vector, the user is divided into different life cycle stages by combining a cluster analysis algorithm and a classification algorithm to obtain the user life cycle hierarchical model.

4. The user strategy map-based operation method according to claim 1, characterized in that: The multi-scenario personalized operation strategy is set according to the crowd strategy map tree, including: Build a unified operation scenario management platform, fully incorporate various operation scenarios into the management system, and establish a mapping relationship database between scenarios and crowds within the platform. According to the crowd classification in the crowd strategy map tree, preset default strategies and personalized strategy templates for each scenario; Build an automated recommendation system based on a hybrid recommendation algorithm, and integrate content-based recommendation algorithms, collaborative filtering recommendation algorithms, and deep learning recommendation algorithms into the automated recommendation system; The multi-scenario personalized operation strategy is collaboratively generated based on the operation scenario management platform and the automated recommendation system.

5. The user strategy map-based operation method according to claim 4 is characterized in that: The collaborative generation of the multi-scenario personalized operation strategy based on the operation scenario management platform and the automated recommendation system includes: Generate the scenario information and strategy guidance required by the automated recommendation system based on the operation scenario management platform, and determine the recommended scenario context information and target population strategy; Based on the automated recommendation system, data cleaning and feature engineering are performed on the user behavior data and scene context information collected in real time, and the content recommendation algorithm, the collaborative filtering recommendation algorithm, and the deep learning recommendation algorithm are used to calculate the user's interest scores for different content or products, and filter out recommended items with higher scores to provide corresponding recommended content for each scene.

6. The user strategy map-based operation method according to claim 1, characterized in that: The operation analysis of the user behavior data collected from each key node of the platform according to the multi-scenario personalized operation strategy, and the operation optimization according to the analysis results, include: Collect user behavior data, system performance data, business operation data, and external environment data; Establishing a monitoring indicator system and an analysis model based on the user behavior data, the system performance data, the business operation data, and the external environment data; Based on the monitoring indicator system and analysis model, monitor the multi-dimensional and multi-level monitoring indicators; Based on the monitoring indicator system and analysis model, the time series analysis algorithm is used to predict user behavior trends, the association rules are used to mine the potential relationship between user behavior and business indicators, the cluster analysis algorithm is used to identify abnormal user groups, and a user behavior funnel model is constructed to deeply analyze the reasons for user loss at different business process stages, and the machine learning algorithm is used to predict user loss, and visual reports and early warning information are generated based on the analysis results; According to the monitoring and analysis results of the monitoring indicator system and analysis model, optimize the page layout and interaction design, adjust the recommendation algorithm parameters and strategies, launch personalized marketing activities, and improve product or service functions.

7. The user strategy map-based operation method according to claim 6 is characterized in that: Based on the monitoring indicator system and analysis model, the multi-dimensional and multi-level monitoring indicators are monitored, including: When abnormal fluctuations in indicators are monitored or warning information is triggered, data backtracking and correlation analysis technology are used to quickly locate the root cause of the problem from multiple dimensions such as population, scenario, and time, and corresponding operational strategy adjustment plans are formulated based on the problem location results.

8. A user strategy map-based operation system, characterized in that: include: Crowd strategy map tree construction module, used to build a crowd strategy map tree based on user multi-dimensional data; A multi-scenario personalized operation strategy module, used to set a multi-scenario personalized operation strategy according to the crowd strategy map tree; The operation analysis and strategy optimization module is used to perform operation analysis on the user behavior data collected from each key node of the platform according to the multi-scenario personalized operation strategy, and to perform operation optimization based on the analysis results.

9. A terminal, characterized in that: include: A processor and a memory, wherein the memory stores an operation program based on a user strategy map, and when the operation program based on a user strategy map is executed by the processor, it is used to implement the operation of the operation method based on a user strategy map as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an operation program based on a user strategy map, and when the operation program based on a user strategy map is executed by a processor, it is used to implement the operation of the operation method based on a user strategy map as described in any one of claims 1-7.

Images