How to use large models to create efficient and accurate user portraits

In the era of digital marketing, understanding users is a must-have skill for every product manager and operations person. User personas, also known as tagged descriptions of users, are an effective tool that can help us deeply analyze users' needs, preferences, behaviors, and values, so as to improve marketing effectiveness, optimize user experience, and improve operational efficiency. However, with the increase and complexity of user data, traditional user portrait methods can no longer meet our needs. We need to use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the accuracy of user portraits, reduce the cost of user portraits, and improve the real-time performance of user portraits. This article will introduce the importance and application methods of large models in user portraits from the following aspects:

The concept of user personas

Definition of a user persona

• User portraits, also known as user models, user segments, user tags, etc., are standardized and quantitative descriptions of users, which can help us better understand the characteristics and needs of users, so as to provide users with more appropriate products and services. The functions of user portraits are mainly as follows:
• Improve marketing effectiveness: Through user portraits, we can divide users into different market segments, formulate different marketing strategies for different user groups, and improve the pertinence and conversion rate of marketing. For example, we can push different advertising content and promotions according to the user's age, gender, region, income and other information to attract the user's attention and interest. In this way, we can save marketing costs, improve marketing effectiveness, and increase users' purchase willingness and loyalty. However, how to accurately obtain and analyze the user's information is a challenge. At this time, we can use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the accuracy of user portraits. Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. For example, an e-commerce platform uses a large model to extract information such as users' shopping preferences, spending power, purchase motivations, and purchase cycles from users' purchases, browsing, clicks, favorites, comments, and other behaviors, and builds multi-dimensional portraits of users, such as categories, prices, brands, styles, occasions, etc., so as to push goods and services that are more in line with users' needs and budgets.
• Optimize user experience: Through user personas, we can better understand users' preferences, needs, pain points, and expectations, so as to optimize the functionality, design, and interaction of products, and improve user satisfaction and loyalty. For example, we can provide personalized recommendations, search, navigation and other services based on users' interests, behaviors, scenarios and other information to meet the diverse needs of users. In this way, we can increase the frequency of users, increase the stickiness of users, and enhance the reputation of users. However, how to effectively capture and meet the individual needs of users is a difficult problem. At this time, we can use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the real-time performance of user portraits. The large model can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. For example, a video platform uses a large model to update user portraits in real time based on users' behaviors, such as type, theme, style, mood, and taste, based on their behaviors, such as viewing, favorites, comments, and ratings, so as to recommend video content that is more in line with users' tastes and moods.
• Improve operational efficiency: Through user profiling, we can manage and analyze user data more effectively, improving the efficiency and effectiveness of operations. For example, we can formulate different operational strategies based on user value, activity, retention rate and other information to improve user activity and retention rate. In this way, we can increase the value of users, increase their contributions, and enhance their loyalty. However, how to efficiently collect and process users' data is a problem. At this time, we can use large models, that is, artificial intelligence models based on large-scale data and deep learning, to reduce the cost of user portraits. Large models can use deep learning technology to build and update user portraits through automated processes, reduce manual intervention and costs, and improve the efficiency and scale of user portraits. For example, a game platform uses a large model to automatically extract information such as users' game preferences, spending power, game level, and game goals from users' games, recharges, consumption, evaluations, and other behaviors, and build multi-dimensional portraits of users, such as types, levels, skills, preferences, goals, etc., so as to provide users with more suitable game products and services.

The constituent elements of a user persona

The constituent elements of a user portrait, that is, the user's tags, can be divided into the following categories:

·??????? Basic user information: This is the basis of user portraits, including the user's age, gender, region, occupation, education, income and other information, which can reflect the user's basic attributes and background. This information is usually provided by the user when registering or logging in, or obtained through a third-party platform, which is relatively easy to obtain and process. However, this information also has some limitations, such as it may be incomplete, inaccurate, not updated, etc., so it needs to be verified and updated regularly. In addition, this information cannot fully reflect the user's personality and needs, because the same basic information may correspond to different user groups, such as the same 25-year-old woman, who may have different interests, spending power, purchase motivations, etc. Therefore, we need to combine other types of information to further refine the user persona. For example, an education platform divides users into different basic categories, such as students, parents, teachers, professionals, etc., according to their age, gender, region, occupation, education, and other information, and then provides different educational products and services, such as courses, materials, consulting, training, etc., according to different basic categories.

·??????? User behavior data: This is the core of user portraits, including users' browsing, clicking, purchasing, commenting, sharing and other behaviors, which can reflect users' interests, needs, habits and behavior patterns. This information is usually generated when users use products or services, or is obtained by tracking and analyzing users' behavior tracks, which is relatively difficult to obtain and process. However, this information also has some advantages, such as being a more realistic, nuanced, and dynamic reflection of the user's characteristics and needs, because the user's behavior is a direct expression of the user and can change with the user's changes. Therefore, we need to use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the collection and analysis of user behavior data. Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. For example, an e-commerce platform uses a large model to extract information such as users' shopping preferences, spending power, purchase motivations, and purchase cycles from users' purchases, browsing, clicks, favorites, comments, and other behaviors, and builds multi-dimensional portraits of users, such as categories, prices, brands, styles, occasions, etc., so as to push goods and services that are more in line with users' needs and budgets.

·??????? User preference information: This is a supplement to the user portrait, including the user's preferences, attitudes, values, emotions and other information, which can reflect the user's personality and style. This information is usually expressed by users when using products or services, or obtained through questionnaires, evaluations, feedback, etc., which is more subjective and diverse. However, this information also has some value, such as reflecting the characteristics and needs of the user in a more nuanced, rich, and interesting way, because the user's preferences are a personalized expression of the user, and it can increase the user's engagement and loyalty. Therefore, we need to use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the acquisition and utilization of user preference information. Large models can use deep learning technology to build and update user preference information through automated processes, reduce manual intervention and costs, and improve the efficiency and scale of user portraits. For example, a music platform uses a large model to extract information such as users' music preferences, emotional tendencies, and psychological states from users' behaviors such as listening, collecting, commenting, and scoring, and constructs multi-dimensional portraits of users, such as genres, styles, emotions, scenes, etc., so as to recommend music content that is more in line with users' tastes and moods.

·??????? User social information: This is an extension of the user's profile, including the user's friends, followers, fans, communities, groups and other information, which can reflect the user's social relationship and influence. This information is usually created by users when using products or services, or obtained through social media, social networks and other platforms, which is relatively extensive and complex. However, this information also has some meaning, such as a broader, deeper, and more powerful reflection of the user's characteristics and needs, because the user's social interaction is the user's group expression, and can influence and be influenced by the behavior and preferences of other users. Therefore, we need to use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the integration and application of users' social information. The large model can use real-time data to adjust and optimize user social information in a dynamic way, increasing the flexibility and timeliness of user portraits. For example, a social platform uses a large model to update the user's portrait in real time according to the user's friends, followers, fans, communities, groups, and other information, such as interests, occupation, education, income, influence, etc., so as to provide users with more suitable social products and services, such as dynamics, topics, activities, live broadcasts, etc.

Application scenarios of user portraits

Personas can be applied to multiple digital marketing scenarios, and here are some common examples:

·??????? Precision marketing: Through user portraits, we can divide users into different market segments, formulate different marketing strategies for different user groups, and improve the pertinence and conversion rate of marketing. For example, an e-commerce platform divides users into different categories based on their purchasing behaviors and preferences, such as fashionistas, beauty lovers, and digital fans, and then pushes different advertising content and promotions according to different user categories to attract users' attention and interests. However, to achieve precision marketing, we need to have an accurate, comprehensive and dynamic user portrait, which requires the help of large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the effect and efficiency of user portraits. Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. At the same time, the large model can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. In this way, we can push the most appropriate advertising content and offers for users according to their latest behaviors and preferences, and improve the conversion rate and revenue of marketing.

·??????? Personalized recommendations: Through user personas, we can better understand users' preferences, needs, pain points, and expectations, so as to provide users with more suitable products and services. For example, a video platform recommends different video content, such as movies, TV series, variety shows, and animations, based on users' viewing behaviors and preferences, to meet the diverse needs of users. However, to achieve personalized recommendations, we need to have a detailed, rich, and interesting user portrait, which requires the help of large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the acquisition and utilization of user portraits. Large models can use deep learning technology to build and update user portraits through automated processes, reduce manual intervention and costs, and improve the efficiency and scale of user portraits. At the same time, the large model can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. In this way, we can recommend video content that best matches users' tastes and moods based on their latest viewing behaviors and preferences, increasing user satisfaction and loyalty.

·??????? User experience optimization: Through user personas, we can optimize the functionality, design, and interaction of products to improve user satisfaction and loyalty. For example, a travel platform provides users with different travel products and services, such as hotels, air tickets, attractions, guides, etc., based on users' travel behaviors and preferences, to improve the user's travel experience. However, to achieve user experience optimization, we need to have a comprehensive, accurate, and real-time user portrait, which requires the help of large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the effect and efficiency of user portraits. Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. At the same time, the large model can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. In this way, we can provide users with the most suitable travel products and services based on their latest travel behaviors and preferences, and improve their travel experience and satisfaction.

·??????? Risk control: Through user profiling, we can identify and prevent users' risky behaviors and protect users' safety and interests. For example, a financial platform evaluates and controls risks for users based on their credit, financial, transaction and other information, such as fraud, overdue, breach of contract, etc., to ensure the safety of users' funds. However, in order to achieve risk control, we need to have a reliable, accurate, and real-time user portrait, which requires the help of large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the effect and efficiency of user portraits. Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. At the same time, the large model can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. In this way, we can assess and control risks for users based on the latest credit, financial, transaction and other information, such as fraud, overdue, default, etc., to ensure the safety of users' funds.

Challenges and opportunities for user personas

Personas are a complex and important undertaking with some challenges and opportunities, but here are some of the areas worth paying attention to:

User Privacy Protection

User privacy protection is an important prerequisite and condition for user portraits, which involves the security and compliance of user data. User data is the basis of user portraits, but it is also the user's sensitive information, which can only be collected, stored, analyzed, and used with the user's authorization and consent, as well as in compliance with relevant laws and regulations. Otherwise, user data may be leaked, misused, or misused, resulting in user distrust, dissatisfaction, or loss. Therefore, we need to establish and improve the protection mechanism of user data, such as encryption, desensitization, authorization, auditing, etc., to ensure the security and compliance of user data. In this way, we can use user data to provide users with better products and services while protecting user privacy. However, the protection of user data also brings some challenges and opportunities, such as how to effectively collect and process user data under the premise of ensuring the security and compliance of user data, and improve the effectiveness and efficiency of user portraits. At this time, we can use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the protection and utilization of user data. The application of large models can bring the following benefits:

• Encrypt user data: Large models can use encryption technology to encrypt user data to ensure the security and privacy of user data. For example, a medical platform uses a large model to encrypt users' health data to prevent user data from being leaked or stolen. In this way, users can provide their own health data with confidence and enjoy more professional and intimate medical services.
• Masking user data: Large models can use masking technology to desensitize user data to ensure compliance and availability of user data. For example, an education platform uses a large model to desensitize users' learning data, remove sensitive information such as users' names, ID numbers, and mobile phone numbers, and retain effective information such as users' learning performance, learning progress, and learning feedback. In this way, users can comply with relevant laws and norms while enjoying more personalized and valuable learning services.

• Authorized user data: The large model can use authorization technology to authorize user data to ensure the autonomy and controllability of user data. For example, a social platform uses a large model to authorize users' social data, allowing users to decide whether to agree to share their social data, who to share it with, how much to share it, and how long to share it, so as to protect users' data rights and interests. In this way, users can choose whether to participate in the sharing of social data according to their own wishes and needs, and at the same time enjoy a broader, deeper and more powerful social service.

• Audit user data: The large model can use audit technology to audit and process user data to ensure the traceability and supervision of user data. For example, a financial platform uses a large model to audit and process users' financial data, record users' financial behaviors and transaction flows, detect users' risky behaviors and abnormal transactions, and ensure the safety of users' funds. In this way, users can view their financial data at any time to understand their financial situation, and at the same time enjoy safer, more convenient and more favorable financial services. under

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Application of large models

The application of large models is an important opportunity and trend of user portraits, which involves the accuracy, cost and real-time performance of user portraits. Large models, that is, artificial intelligence models based on large-scale data and deep learning, can help us improve the effectiveness and efficiency of user portraits. Specifically, the application of large models can bring the following benefits:

• Improve the accuracy of user portraits: Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits. For example, a music platform uses a large model to extract information such as users' music preferences, emotional tendencies, and psychological states from users' behaviors such as listening, collecting, commenting, and scoring, and constructs multi-dimensional portraits of users, such as genres, styles, emotions, scenes, etc., so as to recommend music content that is more in line with users' tastes and moods. In this way, users can enjoy more personalized, interesting, and valuable music services, increasing user satisfaction and loyalty.

• Reduce the cost of user portraits: Large models can use deep learning technology to build and update user portraits through automated processes, reducing manual intervention and costs. For example, a social platform uses a large model to automatically extract user tags, such as interests, occupations, education, etc., from users' social information, without manual labeling and review. In this way, the construction and update of user personas can be faster, more efficient, and more labor-saving, and improve the efficiency and scale of user personas.

• Improve the real-time performance of user portraits: Large models can use real-time data to adjust and optimize user portraits in a dynamic way, increasing the flexibility and timeliness of user portraits. For example, a game platform uses a large model to update user portraits, such as levels, skills, and preferences, in real time based on users' game behaviors and feedback, to improve the user's gaming experience. In this way, the user portrait can reflect the latest characteristics and needs of the user in a more timely, accurate and flexible manner, and improve the sensitivity and responsiveness of the user portrait.

summary

User personas are an important tool for digital marketing, which can help us better understand the characteristics and needs of users, so as to improve marketing effectiveness, optimize user experience and improve operational efficiency. However, with the increase and complexity of user data, traditional user portrait methods can no longer meet our needs. We need to use large models, that is, artificial intelligence models based on large-scale data and deep learning, to improve the accuracy of user portraits, reduce the cost of user portraits, and improve the real-time performance of user portraits. The application of large models can bring the following benefits:

• Improve the accuracy of user portraits: Large models can use large-scale data and complex algorithms to learn and mine the deep-seated characteristics and needs of users, and improve the quality and coverage of user portraits.
• Reduce the cost of user portraits: Large models can use deep learning technology to build and update user portraits through automated processes, reduce manual intervention and costs, and improve the efficiency and scale of user portraits.
• Improve the real-time performance of user portraits: Large models can use real-time data to adjust and optimize user portraits in a dynamic way, increase the flexibility and timeliness of user portraits, and improve the sensitivity and responsiveness of user portraits.

If you want to know more about the application and cases of large models in digital marketing, please follow my personal account "Product Manager Dugu Shrimp" (the same number on the whole network), in my column "Intelligent Marketing - How Large Models Empower Operations and Product Managers ", I will share more dry goods and experiences to help you use large models to improve your product and operation capabilities, and make your digital marketing more efficient, more accurate and more interesting.