Digital product data driving and accurate user portrait construction method and system for globalized operation

Abstract

The invention discloses a digitalized product data driving and accurate user portrait construction method and system for globalized operation, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, data collection and integration; s2, data fusion and cross validation; s3, real-time data tracking and analysis; s4, constructing and updating a dynamic user portrait; s5, cross-culture and cross-language adaptation processing is carried out; and S6, user portrait application and optimization. According to the digitalized product data driving and accurate user portrait construction method and system oriented to globalized operation, various behavior data of the user in the product using process are continuously captured through real-time data tracking, useful information is analyzed and extracted in real time, the user portrait is dynamically updated, the timeliness and accuracy of the user portrait are remarkably improved, and the user experience is improved. Therefore, business decision deviation and resource waste caused by lag or inaccuracy of the user portrait are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a method and system for data-driven and precise user profiling of digital products for global operations. Background Technology

[0002] Digital products refer to services or solutions built on digital technologies and delivered via the internet, including SaaS platforms, mobile applications, and smart hardware support services. These products are highly replicable and have low marginal costs, making them naturally suitable for global expansion. Global operations, on the other hand, refer to a company's unified and coordinated business activities across multiple countries and regions, requiring products to have cross-cultural and cross-language adaptability. The combination of these two aspects has given rise to new technological demands: how to achieve localized adaptation and scalable growth of products through data-driven approaches.

[0003] While mainstream digital products like Shopify e-commerce and Zoom video conferencing have achieved initial global deployment, they suffer from significant shortcomings in user understanding. User profiles, serving as a bridge between massive amounts of data and business decisions, directly impact three key aspects of global operations: product localization, targeted marketing, and risk control. Traditional methods rely on manual surveys and static tags, collecting information through questionnaires and other means. However, due to the dynamic and diverse nature of user behavior, static tags struggle to comprehensively and accurately reflect users' true needs and preferences. Data shows that user profiles built using this approach match actual user behavior less than 60%, leading to deviations in business decisions such as advertising and significant waste. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for data-driven and precise user profiling of digital products for global operations, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, it provides a data-driven approach to building precise user profiles for digital products operating globally, including the following steps: S1. Data Collection and Integration: Collect user data from multiple data sources to form a comprehensive user dataset, providing basic data for subsequent user profile construction; S2. Data Fusion and Cross-Validation: Fusion of data from different sources and dimensions to form a unified user data representation, and cross-validation to correct errors or inconsistencies, thereby improving the completeness and reliability of user profiles. S3. Real-time data tracking and analysis: Embed data tracking code in digital products to continuously capture various behavioral data of users during product use through real-time data tracking. Stream processing technologies (such as Apache Kafka, Apache Flink, etc.) are used to analyze the collected behavioral data in real time to extract useful information, namely user behavior characteristics and information, to ensure the timeliness and accuracy of the profile and reflect the latest changes in user behavior. S4. Dynamic User Profile Building and Updating: Dynamically build and update user profiles based on the results of real-time data tracking and analysis; S5, Cross-cultural and Cross-language Adaptation: S51. Through cultural sensitivity analysis, understand the differences in user behavior under different cultural backgrounds; Using Hofstede's cultural dimensions theory, we identify and analyze user behavior characteristics across different cultural backgrounds, including but not limited to values, social habits, and consumption concepts. Based on the analysis results, we develop targeted cultural adaptation strategies. For example, for cultures that value collectivism, product design focuses more on community features and shared experiences; while for individualistic cultures, it emphasizes personalization and self-expression.

[0006] S52. Employs multilingual processing technology to process user data in different language environments; By leveraging Natural Language Processing (NLP) technology, the language input by users can be automatically identified and translated in real time. This helps to eliminate language barriers and ensure that users can use the product without obstacles. Then, the product content is localized according to the language habits and cultural characteristics of the target market, including interface language, help documentation, marketing information, etc., to improve user acceptance and satisfaction. S6. User Profile Application and Optimization: Apply the constructed user profiles to actual business decisions, such as product localization, precision marketing, and risk control, and collect feedback data on business decisions to evaluate the effectiveness of user profiles. Optimize and adjust the user profile construction method based on the feedback data.

[0007] Furthermore, in step S1, user data is collected from various digital products, social media, transaction systems, and other channels using tools such as API interfaces, web crawling technology, and log collection systems. Data cleaning tools are then used to perform preliminary cleaning and organization of the collected data to remove invalid and erroneous data.

[0008] Furthermore, in step S1, the user data includes, but is not limited to, basic user attributes, behavioral data, social data, and transaction data, as detailed below: Basic user attributes include basic information such as age, gender, geographical location, occupation, and educational background. These data reflect the characteristics of the user group. Behavioral data refers to the specific actions and behaviors of users when using a product, such as clicking, browsing, purchasing, and searching. This data reflects users' interests, preferences, and usage habits. Social data: A user's activities on social media, including but not limited to friend lists, shared content, and interaction records. This data reflects a user's social influence and social circle. Transaction data includes purchase records, payment methods, order details, etc., which are directly related to users' spending power and consumption preferences.

[0009] Furthermore, in step S2, a hybrid data fusion algorithm based on weighted averaging and feature extraction is used to integrate multi-source data. Specifically: Data classification: The collected multi-source data is divided into two categories: numerical data and non-numerical data; Numerical data fusion: For numerical data (such as user age, spending amount, etc.), a weight is assigned to each numerical data source. The weight can be determined based on factors such as the reliability and timeliness of the data source. A weighted average method is used for fusion, and the fused numerical data representation is calculated using the weighted average formula. ,in It is the value after merging. It is the first The weight of each data source, It is the first The values ​​from each data source; Non-numerical data fusion: For non-numerical or complex structured data (such as user behavior logs, social media post content, etc.), Natural Language Processing (NLP) techniques are used to convert them into numerical feature vectors. After normalizing the extracted feature vectors, a strategy similar to weighted averaging is used to sum the weighted values ​​of each dimension of the feature vectors to obtain the fused feature vector representation. ,in It is the value after merging. It is the first Weights of each feature dimension, It is the first The values ​​of each feature dimension (after normalization); Comprehensive Integration: Based on specific application scenarios and business needs, determine the weight allocation of numerical data fusion results and non-numerical data fusion results, and use a weighted summation method to merge the two types of fusion results into a unified user data representation. ,in It is a unified user data representation after integration. and These are the weights for numerical data and non-numerical data, respectively.

[0010] Furthermore, in step S2, cross-validation utilizes mutual verification between multiple data sources and data dimensions to assess data consistency and discover complementary information between data. The cross-validation is performed in the following manner: Comparative verification: Compare data from different data sources that describe the same user behavior or attribute, and check whether there are significant differences between them; if there are no significant differences, it indicates that these data sources are consistent in describing the behavior or attribute. Complementary filling: Identify missing or incomplete information in a data source and attempt to find corresponding supplementary information from other data sources; for example, if a user's basic attribute data lacks occupational information, but social data contains work-related activities of the user, then the user's occupation can be inferred through these activities. Trend analysis: Perform trend analysis on user behavior data at different points in time or in different scenarios to observe the consistency of data changes; if the user behavior trends shown by multiple data sources are consistent, it indicates that the trend is real.

[0011] Furthermore, step S4 includes the following sub-steps: S41. User profile initialization: Based on the initial results of real-time data tracking and analysis, and combined with basic user attribute data (such as age, gender, region, etc.), an initial user profile is constructed. The initial user profile contains information such as the user's basic characteristics, behavioral patterns, and interests, providing a basis for subsequent dynamic updates. S42. Establishment of dynamic update mechanism: Set the trigger conditions for dynamic updating of user profiles, such as significant changes in user behavior data or reaching a preset time interval. When the trigger conditions are met, the user profile update process is automatically started, user behavior data is collected and analyzed again, and the features and information in the user profile are adjusted. S43. User Profile Feature Adjustment: Based on the results of real-time data tracking and analysis, machine learning algorithms (such as decision trees, neural networks, etc.) are used to adjust and optimize the features in the user profile; for example, if it is found that a user has recently been frequently purchasing a certain type of product, then the preference feature for that type of product can be added to the user profile.

[0012] On the other hand, a data-driven and precise user profiling system for digital products operating globally is provided, applied to the aforementioned data-driven and precise user profiling method for digital products operating globally, including: Data collection module: responsible for collecting user data from multiple data sources, and performing initial cleaning and organization; Data fusion module: responsible for merging user data from different sources and dimensions to form a unified user data representation; Cross-validation module: responsible for cross-validating the merged user data, identifying and correcting inconsistencies and errors in the data; Tracking and Analysis Module: Responsible for tracking various behavioral data of users during product use in real time, and performing instant analysis to extract useful information; User profile building module: Responsible for dynamically building and updating user profiles based on the results of real-time data tracking and analysis; Adaptation processing module: responsible for identifying and analyzing user behavior characteristics under different cultural backgrounds, providing cultural adaptation strategies for product localization, and using multilingual processing technology to process user data in different language environments; Application Optimization Module: Responsible for applying the constructed user profiles to actual business decisions, and continuously optimizing the user profile construction methods and application strategies based on business feedback; Storage Management Module: Responsible for storing and managing the constructed user profile data, supporting rapid retrieval and updating of user profiles.

[0013] Furthermore, the system also includes: The processor is used to execute computer instructions to enable the system to perform: a data collection module, a data fusion module, a cross-validation module, a tracking and analysis module, a user profile building module, an adaptation processing module, an application optimization module, and a storage management module; A memory for storing computer instructions, wherein the memory stores a computer program that, when executed by a processor, implements the method described above.

[0014] This invention provides a data-driven method and system for building precise user profiles for digital products operating globally, which has the following beneficial effects: This invention continuously captures various behavioral data of users during product use through real-time data tracking, and analyzes and extracts useful information in real time to dynamically update user profiles. This significantly improves the timeliness and accuracy of user profiles, effectively avoiding business decision-making biases and resource waste caused by outdated or inaccurate user profiles. Furthermore, by introducing cultural sensitivity analysis and multilingual processing technology, it fully considers the impact of cultural differences and language barriers on user behavior, significantly enhancing the accuracy and applicability of user profiles in different cultural and linguistic environments, providing strong support for global operations. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the operational steps of a data-driven and precise user profile construction method for digital products aimed at global operations, as described in this invention. Detailed Implementation

[0016] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0017] Example 1: Building a Global User Profile for an E-commerce Platform Step S1, Data Collection and Integration: The API interface was used to collect user transaction data (such as purchase records, payment methods, and order details) from the e-commerce platform's database. The data included: User A purchased electronic products 5 times in the past month, with a total spending of $2,000.

[0018] By using web scraping technology to capture user social data (such as shared content and interaction records) from social media, it was discovered that user A frequently shares articles reviewing technology products and interacts with more than 100 technology enthusiasts.

[0019] The log collection system records user behavior data (such as clicks and browsing), and the records show that user A frequently browses pages on high-end smartphones.

[0020] By integrating basic user attribute data (such as age, gender, and geographical location), it was found that user A is a 28-year-old male living in Berlin, Germany.

[0021] Step S2, Data Fusion and Cross-Validation: Numerical data (such as spending amount and age) are fused using a weighted average method. In this embodiment, the e-commerce platform data has a weight of 0.6 and the social media data has a weight of 0.4. After fusion, the average monthly spending of user A is (20000.6 + social media estimated spending level 0.4, assuming the social media estimate is 1800 USD) = 1920 USD.

[0022] Non-numerical data (such as behavioral logs and social post content) are converted into feature vectors using NLP techniques and then fused through weighted summation.

[0023] Cross-validation revealed that user A's purchase history recorded on the e-commerce platform was highly consistent with his interest in technology products shared on social media.

[0024] Step S3: Real-time data tracking and analysis: Data tracking code was embedded in an e-commerce platform to capture user A's browsing and purchasing behavior in real time. Using Apache Flink stream processing technology, the data was analyzed in real time, revealing that user A had shown a strong interest in a new smartphone in the past week.

[0025] Step S4: Dynamic User Profile Construction and Update Initialize the user profile, including user A's basic characteristics (28-year-old male, Berlin, Germany), behavioral patterns (frequent browsing of electronic products), and interests (technology products, especially smartphones).

[0026] The dynamic update mechanism is triggered because user A's behavioral data has changed significantly (interest in the new smartphone). The data is then collected and analyzed again to adjust the user profile and add preference features for the new smartphone.

[0027] Step S5: Cross-cultural and cross-language adaptation: Based on Hofstede's cultural dimension theory, German culture tends towards individualism; therefore, the user profile emphasizes user A's pursuit of personalized technological products. Multilingual processing technology was employed to translate e-commerce platform content into German and adjust marketing messages according to the language habits of the German market.

[0028] Step S6, User Profile Application and Optimization: User profiling was applied to precision marketing, pushing personalized ads for a new smartphone to user A. Click-through rate and conversion rate feedback data were collected to evaluate the effectiveness of the user profiling, revealing a 20% increase in click-through rate. Based on this feedback, the user profiling construction method was optimized.

[0029] Implementation results: User A's purchase conversion rate increased by 15%, indicating that accurate user profiling effectively supported targeted marketing. The improved user satisfaction of the e-commerce platform in the German market benefited from the implementation of product localization and cultural adaptation strategies.

[0030] Example 2: Building a Global User Profile for an Online Education Platform Step S1, Data Collection and Integration: We collect user learning data (such as course selection, learning progress, and completion rate) from the database of online education platforms. Example data: User B completed 5 programming courses in the past three months with an average completion rate of 90%.

[0031] By scraping users' social data from social media, it was discovered that user B frequently participates in programming technology discussion groups.

[0032] Record user behavior data (such as login time and course access duration), showing that user B's habit is to study between 8 pm and 10 pm.

[0033] By integrating basic user attribute data, it was found that user B is a 22-year-old student living in Tokyo, Japan.

[0034] Step S2, Data Fusion and Cross-Validation: Numerical data (such as course completion rate and login frequency) are fused using a weighted average method. In this embodiment, the platform data weight is 0.7 and the social media data weight is 0.3. After fusion, the average course completion rate of user B is (90% 0.7 + estimated completion rate of social media activity 0.3, assumed to be 85%) = 88.5%.

[0035] Non-numerical data is transformed and merged using NLP techniques.

[0036] Cross-validation revealed that User B's course selections aligned with their technology interests on social media.

[0037] Step S3: Real-time data tracking and analysis: By embedding data tracking code, user B's learning behavior is captured in real time and analyzed instantly using Apache Kafka stream processing technology, revealing that user B has recently begun exploring artificial intelligence courses.

[0038] Step S4: Dynamic User Profile Construction and Update Initialize the user profile, including user B's basic characteristics (22-year-old student, Tokyo, Japan), behavioral patterns (studying at night, high completion rate), and interests (programming technology).

[0039] The dynamic update mechanism is triggered by changes in user B's behavioral data (exploring the artificial intelligence course), which leads to the re-collection and analysis of data, adjustment of the user profile, and addition of interest features related to artificial intelligence.

[0040] Step S5: Cross-cultural and cross-language adaptation: Through cultural sensitivity analysis, Japanese culture emphasizes collectivism and respect for authority. Therefore, user B in the user profile might prefer structured, authoritative course content. Multilingual processing technology was employed to translate the platform content into Japanese, and course descriptions and marketing information were tailored to the characteristics of the Japanese market.

[0041] Step S6, User Profile Application and Optimization: User profiling was applied to course recommendations, pushing advanced AI-related courses to user B. Course enrollment and completion rates were collected to evaluate the effectiveness of the user profiling, revealing an 18% increase in enrollment. Based on this feedback, the user profiling strategy was optimized.

[0042] Implementation results: User B maintained a high course completion rate (89%) and a 90% satisfaction rate with the recommended courses, indicating that user profiling effectively supported personalized learning path recommendations. The improved user retention rate of online education platforms in the Japanese market is attributed to cultural adaptation and the implementation of precise marketing strategies.

[0043] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A data-driven and precise user profiling method for digital products operating globally, characterized in that: Includes the following steps: S1. Data Collection and Integration: Collect user data from multiple data sources to form a comprehensive user dataset; S2. Data Fusion and Cross-Validation: Fusion of data from different sources and dimensions to form a unified user data representation, and cross-validation. S3. Real-time data tracking and analysis: Through real-time data tracking, continuously capture various behavioral data of users during product use, and perform instant analysis to extract useful information; S4. Dynamic User Profile Building and Updating: Dynamically build and update user profiles based on the results of real-time data tracking and analysis.

2. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, In step S1, user data is collected from various digital products, social media, and transaction systems using API interfaces, web crawling technology, and log collection systems. Data cleaning tools are then used to perform preliminary cleaning and organization of the collected data, removing invalid and erroneous data.

3. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 2, characterized in that, In step S1, the user data includes, but is not limited to, basic user attributes, behavioral data, social data, and transaction data, as detailed below: Basic user attributes include the user's age, gender, geographical location, occupation, and educational background, reflecting the characteristics of the user group; Behavioral data refers to the specific operational behaviors of users during product use, reflecting users' interests, preferences, and usage habits. Social data: A user's activities on social media, including but not limited to friend lists, shared content, and interaction records, reflecting the user's social influence and social circle; Transaction data includes purchase records, payment methods, and order details, which are directly related to a user's spending power and consumption preferences.

4. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, In step S2, a hybrid data fusion algorithm based on weighted averaging and feature extraction is used to integrate multi-source data. The specific operation is as follows: Data classification: The collected multi-source data is divided into two categories: numerical data and non-numerical data; Numerical data fusion: For numerical data, a weighted average method is used for fusion. The weighted average formula is used to calculate the fused numerical data representation. ,in It is the value after merging. It is the first The weight of each data source, It is the first The values ​​from each data source; Non-numerical data fusion: For non-numerical or complex structured data, natural language processing techniques are used to convert them into numerical feature vectors. After normalization, a strategy similar to weighted averaging is employed to sum the features vectors in weighted terms across each dimension, resulting in a fused feature vector representation. ,in It is the value after merging. It is the first Weights of each feature dimension, It is the first The values ​​of each feature dimension; Integration: Using a weighted summation method, the two types of integration results are combined into a unified user data representation. ,in It is a unified user data representation after integration. and These are the weights for numerical data and non-numerical data, respectively.

5. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, In step S2, cross-validation utilizes the mutual corroboration between multiple data sources and data dimensions to assess data consistency and discover complementary information between data. The cross-validation is performed in the following manner: Comparative verification: Compare data from different data sources that describe the same user behavior or attribute to check whether there are significant differences between them; Complementary imputation: Identifying missing or incomplete information in a data source and attempting to find corresponding supplementary information from other data sources; Trend analysis: Conduct trend analysis on user behavior data at different points in time or in different scenarios to observe the consistency of data changes.

6. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, Step S4 includes the following sub-steps: S41. User profile initialization: Based on the initial results of real-time data tracking and analysis, and combined with basic user attribute data, construct an initial user profile; S42. Establishment of dynamic update mechanism: Set the trigger conditions for dynamic updating of user profiles. When the trigger conditions are met, the user profile update process will be started automatically. S43. User profile feature adjustment: Adjust and optimize the features in the user profile based on the results of real-time data tracking and analysis.

7. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, It also includes the following steps: S5, Cross-cultural and Cross-language Adaptation: S51. Through cultural sensitivity analysis, understand the differences in user behavior under different cultural backgrounds; Using Hofstede's cultural dimension theory, we identify and analyze user behavior characteristics under different cultural backgrounds, including but not limited to values, social habits, and consumption concepts, and formulate targeted cultural adaptation strategies based on the results of the cultural dimension analysis. S52. Employs multilingual processing technology to process user data in different language environments; Using natural language processing technology, the system automatically identifies the language input by the user and translates it in real time. Then, it localizes the product content, including interface language, help documentation, and marketing information, according to the language habits and cultural characteristics of the target market.

8. The method for data-driven and precise user profiling construction of digital products for global operations as described in claim 1, characterized in that, It also includes the following steps: S6. User Profile Application and Optimization: Apply the constructed user profiles to actual business decisions and optimize them based on feedback.

9. A data-driven and precise user profiling system for digital products operating globally, applied to the data-driven and precise user profiling method for digital products operating globally as described in any one of claims 1-8, characterized in that, include: Data collection module: responsible for collecting user data from multiple data sources, and performing initial cleaning and organization; Data fusion module: responsible for merging user data from different sources and dimensions to form a unified user data representation; Cross-validation module: responsible for cross-validating the merged user data, identifying and correcting inconsistencies and errors in the data; Tracking and Analysis Module: Responsible for tracking various behavioral data of users during product use in real time, and performing instant analysis to extract useful information; User profile building module: responsible for dynamically building and updating user profiles based on the results of real-time data tracking and analysis; Adaptation processing module: responsible for identifying and analyzing user behavior characteristics under different cultural backgrounds, providing cultural adaptation strategies for product localization, and using multilingual processing technology to process user data in different language environments; Application Optimization Module: Responsible for applying the constructed user profiles to actual business decisions, and continuously optimizing the user profile construction methods and application strategies based on business feedback; Storage Management Module: Responsible for storing and managing the constructed user profile data, supporting rapid retrieval and updating of user profiles.

10. A data-driven and precise user profiling system for digital products operating globally, as described in claim 9, is characterized in that... include: The processor is used to execute computer instructions to enable the system to perform: a data collection module, a data fusion module, a cross-validation module, a tracking and analysis module, a user profile building module, an adaptation processing module, an application optimization module, and a storage management module; A memory for storing computer instructions, wherein the memory stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-8.

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