How to use large models to obtain user data and improve the effect of digital marketing

Summary:

This article describes how to obtain user data and improve the effectiveness of digital marketing, including:

There are two main ways to obtain user data for large models: active acquisition and passive acquisition, which can be flexibly selected and combined according to different scenarios and purposes.

There are two main sources for large models to obtain user data: online data and offline data, which can be flexibly selected and combined according to different scenarios and purposes.

After the large model obtains user data, it also needs to carry out some processing on user data, such as data cleaning, data integration, data analysis, etc., so as to improve the quality and value of user data and provide better support and guidance for digital marketing.

The role of large models in data collection is very important and significant, it can help to obtain more, better and more useful user data, so as to provide stronger support and guidance for digital marketing.

Data security refers to the protection and respect of user data, such as complying with data protection regulations, encrypting the transmission and storage of data, and restricting data access rights, so as to ensure the security, integrity, and control of user data.

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In the world of digital marketing, user data is an invaluable resource that helps us understand user needs, preferences, behaviors, and feedback, so that we can optimize our product design, advertising, and user growth strategies. However, how to effectively obtain user data? Traditional methods are often through manual means, such as setting up questionnaires, interviews, surveys, etc., to collect user opinions and feedback. Although this method can obtain some valuable data, it also has many limitations, such as small data volume, low data quality, slow data update, and difficult data analysis. With the development of artificial intelligence technology, especially the emergence of large models, we have a better option, which is to use large models to obtain user data.

What is a large model

Large models refer to those AI models with very large scale, which often have billions or even trillions of parameters, can process massive amounts of data, learn a variety of complex tasks, and generate a variety of useful content. Representatives of large models include GPT-3, BERT, DALL-E, etc., which have achieved amazing results in natural language processing, computer vision, natural language generation and other fields. The advantage of large models is that they can leverage large amounts of data, extract valuable information from it, and generate useful content to provide us with better services and experiences.

How the big model obtains user data

There are two main ways for large models to obtain user data, one is active acquisition and the other is passive acquisition. Active acquisition means that we use a large model to actively ask questions to users and collect users' answers, so as to obtain user data. Passive acquisition refers to the use of large models to monitor and analyze user behavior to obtain user data. Both methods have their own advantages and disadvantages, and we can flexibly choose and combine them according to different scenarios and purposes.

Proactive acquisition

We can use large models to design and generate some questions, such as user satisfaction surveys, user demand analysis, user feedback collection, etc., and then send these questions to users through various channels, such as websites, social media, emails, text messages, etc., to collect user answers, so as to obtain user data. The advantage of this method is that we can obtain some more clear and specific data, such as user ratings, opinions, suggestions, etc., which can help us better understand user satisfaction, needs, problems, etc., so as to improve our products and services. The disadvantage of this method is that it requires the active participation and cooperation of users, if users are unwilling to answer questions, or the answers are untrue, incomplete and inaccurate, then we will not be able to obtain effective data, and even cause data bias and misleading.

In order to improve the willingness and quality of users to answer questions, we can take advantage of some features of the large model, such as:

• Use the natural language generation ability of the large model to generate some interesting and attractive questions, such as asking questions in the form of humor, witty, whimsy, etc., or using some interesting pictures, videos, audio and other materials to assist in asking questions, so as to stimulate users' interest and curiosity, and increase users' participation and interactivity.
• Using the natural language understanding ability of the large model, analyze the user's answers, and give some appropriate feedback, such as replying with praise, encouragement, thanks, etc., or replying with some useful information, suggestions, rewards, etc., so as to enhance the user's trust and satisfaction, and improve the user's loyalty and retention rate.
• Using the natural language adaptability of the large model, according to the user's characteristics, such as age, gender, region, interests, etc., some suitable questions are customized, such as asking questions in different languages, styles, topics, etc., or asking questions in different forms, difficulties, lengths, etc., so as to increase the user's comfort and sense of identity, and improve the quality and accuracy of the user's answers.

Passively acquired

We can use large models to monitor and analyze user behaviors, such as users' browsing, clicking, searching, and purchasing behaviors on the website, or users' following, liking, commenting, and sharing behaviors on social media, or users' behaviors such as entering, staying, trying, and purchasing in physical stores, so as to obtain user data. The advantage of this method is that we can obtain some more implicit and deep data, such as users' interests, preferences, habits, motivations, etc., which can help us better understand the psychology and behavior of users, so as to optimize our products and services. The disadvantage of this method is that it requires the privacy and security of users, and if users do not know or agree with our collection and analysis of their behavior data, or we do not use and protect this data reasonably and legally, then we may infringe on the rights and interests of users, and even cause users to resent and resist.

In order to ensure the privacy and security of users, we can take advantage of some features of the large model, such as:

• Use the natural language generation ability of the large model to generate some clear and friendly privacy policies, such as explaining the purpose, method, scope, and duration of our collection and use of user data in simple, clear, and transparent language, or using some charts, examples, FAQs, etc., to explain how we protect and respect the security, integrity, and controllability of user data, so as to increase users' understanding and trust, and obtain users' consent and support.
• Using the natural language understanding ability of the large model, analyze the user's feedback, and give some reasonable responses, such as replying with explanations, apologies, improvements, etc., or replying with some compensation, compensation, apology, etc., so as to reduce users' dissatisfaction and complaints, and improve users' tolerance and understanding.
• Using the natural language adaptability of the large model, according to the user's characteristics, such as sensitivity, risk, security, etc., adjust some appropriate parameters, such as the frequency, degree, and scope of collecting and using user data, or the options, permissions, and deadlines for providing and retaining user data, so as to increase the user's sense of security and control, and improve the user's privacy and security.

Data source

There are two main sources for large models to obtain user data, one is online data and the other is offline data. Online data refers to the data generated by various activities of users on the Internet, such as users' browsing, clicking, searching, purchasing and other behaviors on websites, or users' following, liking, commenting, sharing and other behaviors on social media, or users' browsing, collecting, adding, placing orders and other behaviors on e-commerce platforms. Offline data refers to the data generated by various activities of users in the real world, such as users' behaviors such as entering, staying, trying, and purchasing in physical stores, or data provided by users through questionnaires, telephone interviews, face-to-face communication, etc. Both types of data have their own advantages and disadvantages, and we can flexibly choose and combine them according to different scenarios and purposes.

Online data

The advantages of online data are large data volume, high data quality, fast data update, and easy data analysis. Because users' activities on the Internet can be recorded and tracked, we can use various tools, such as website analysis, social media analysis, e-commerce analysis, etc., to collect and analyze users' online data, so as to obtain some valuable information, such as the number of users' visits, access duration, access path, access source, access device, visit frequency, access preferences, access purpose, access results, etc. This information helps us understand the behavioral characteristics of users, such as how users find our products or services, how users use our products or services, how users evaluate our products or services, how users convert into our customers or fans, how users recommend our products or services to others, etc. This information can help us optimize the design, functionality, content, interaction, experience, etc. of our products or services, so as to improve user satisfaction, loyalty, retention, conversion rate, recommendation rate, etc., and ultimately improve the effectiveness of our digital marketing.

The disadvantages of online data are low data privacy, poor data security, fierce data competition, and scattered data. Because users' activities on the Internet can be recorded and tracked, we also have to face some risks and challenges, such as users' privacy may be leaked or abused, users' data may be stolen or destroyed, users' data may be used or interfered with by competitors or malicious actors, and users' data may be scattered across different platforms or channels, which is difficult to integrate or unify. These risks and challenges may affect users' trust and sense of security, users' participation and cooperation, the authenticity and validity of users' data, the collection and analysis of our data, and ultimately the effectiveness of our digital marketing.

In order to overcome the shortcomings of online data, we can take advantage of some features of large models, such as:

• Use the natural language generation ability of the large model to generate some clear and friendly privacy policies, such as explaining the purpose, method, scope, and duration of our collection and use of user data in simple, clear, and transparent language, or using some charts, examples, FAQs, etc., to explain how we protect and respect the security, integrity, and controllability of user data, so as to increase users' understanding and trust, and obtain users' consent and support.
• Using the natural language understanding ability of the large model, analyze the user's feedback, and give some reasonable responses, such as replying with explanations, apologies, improvements, etc., or replying with some compensation, compensation, apology, etc., so as to reduce users' dissatisfaction and complaints, and improve users' tolerance and understanding.
• Using the natural language adaptability of the large model, according to the user's characteristics, such as sensitivity, risk, security, etc., adjust some appropriate parameters, such as the frequency, degree, and scope of collecting and using user data, or the options, permissions, and deadlines for providing and retaining user data, so as to increase the user's sense of security and control, and improve the user's privacy and security.
• The natural language fusion capability of the large model is used to integrate online data from different sources, such as website data, social media data, and e-commerce platform data, so as to build a complete and comprehensive user portrait, such as the user's basic information, interests, consumption habits, purchase intentions, purchase behaviors, purchase results, etc., so as to improve the value and effect of user data.

Offline data

The advantages of offline data are high data authenticity, large data depth, wide data coverage, and strong data interaction. Because users' activities in the real world can be observed and experienced, we can use various tools, such as physical store analysis, questionnaire analysis, telephone interview analysis, etc., to collect and analyze users' offline data, so as to obtain some valuable information, such as the user's number of visits, arrival time, arrival path, arrival source, arrival equipment, arrival frequency, visit preference, destination purpose, visit results, etc. This information helps us understand the behavioral characteristics of our users, such as how users find our physical stores, how users experience our products or services in our physical stores, how users evaluate our physical stores, how users convert into our customers or fans, how users recommend our physical stores to others, etc. This information can help us optimize the design, functionality, content, interaction, experience, etc. of our physical stores, so as to improve user satisfaction, loyalty, retention, conversion rate, recommendation rate, etc., and ultimately improve our digital marketing effectiveness.

The disadvantages of offline data are small data volume, low data quality, slow data update, and difficult data analysis. Because users' activities in the real world are limited and changing, we also have to face some difficulties and challenges, such as the user's arrival may be affected by factors such as time, place, weather, traffic, etc., the user's experience may be affected by factors such as mood, environment, and crowd, the user's evaluation may be affected by emotions, attitudes, biases and other factors, and the user's data may be difficult to collect and record, difficult to organize and analyze, and difficult to update and track. These difficulties and challenges may affect the participation and cooperation of users, the authenticity and validity of users' data, the collection and analysis of our data, and ultimately the effectiveness of our digital marketing.

In order to overcome the shortcomings of offline data, we can take advantage of some features of large models, such as:

• Use the natural language generation ability of the large model to generate some interesting and attractive questions, such as asking questions in the form of humor, witty, whimsy, etc., or using some interesting pictures, videos, audio and other materials to assist in asking questions, so as to stimulate users' interest and curiosity, and increase users' participation and interactivity.
• Using the natural language understanding ability of the large model, analyze the user's answers, and give some appropriate feedback, such as replying with praise, encouragement, thanks, etc., or replying with some useful information, suggestions, rewards, etc., so as to enhance the user's trust and satisfaction, and improve the user's loyalty and retention rate.
• Using the natural language adaptability of the large model, according to the user's characteristics, such as age, gender, region, interests, etc., some suitable questions are customized, such as asking questions in different languages, styles, topics, etc., or asking questions in different forms, difficulties, lengths, etc., so as to increase the user's comfort and sense of identity, and improve the quality and accuracy of the user's answers.

data processing

After the large model obtains user data, it also needs to carry out some processing on user data, such as data cleaning, data integration, data analysis, etc., so as to improve the quality and value of user data and provide better support and guidance for our digital marketing.

Data cleansing

Data cleansing refers to the preprocessing of user data, such as de-duplication of data, handling missing values, and handling outliers, so as to improve the accuracy and validity of user data. The purpose of data cleaning is to eliminate some noise and interference in user data, such as user misoperation, wrong input, malicious filling, etc., so as to make user data more authentic and credible.

There are many ways to cleanse data, such as:

• De-duplication: Duplicate data refers to the presence of two or more identical or similar pieces of data in a user's data, such as a user answering the same question multiple times, or a user providing the same information on different platforms or channels. Duplicate data can affect the statistics and analysis of user data, resulting in redundancy and bias of data. The method of de-duplication is to find and remove duplicate data by comparing the content, source, time, etc. of user data, and keep only the latest or most complete piece of data.
• Missing value handling: Missing value refers to some blank or incomplete data in the user's data, such as the user did not answer a question, or the user did not provide certain information. Missing values can affect the integrity and availability of user data, resulting in data shortages and losses. The method of missing value handling is to make user data more complete or concise by supplementing or deleting missing data. The way to fill in the missing data is to fill in the missing data by speculating or asking the user, such as using the natural language generation ability of the large model to generate some reasonable default values or prompts to guide the user to fill in the missing data. The way to delete missing data is to filter or ignore users, such as using the natural language understanding ability of large models to analyze the quality of users' answers, and filter out or ignore users who have too much or too little missing data.

• Outlier handling: Outliers refer to data that does not conform to normal rules or logic, such as the user's answers are too extreme, inconsistent, or unreasonable. Outliers will affect the rationality and credibility of user data, resulting in errors and deviations in data. The method of outlier handling is to make user data more reasonable or consistent by detecting or correcting abnormal data. The way to detect abnormal data is to find and flag abnormal data by comparing or analyzing user data, such as using the natural language understanding ability of large models to analyze the content of users' answers, and find and flag those responses that are inconsistent or unreasonable with other users or themselves. The way to correct abnormal data is to make user data more normal or average by modifying or replacing abnormal data. The way to modify abnormal data is to make the user's data closer to the normal range or logic by adjusting or correcting the abnormal data, such as using the natural language generation ability of the large model to generate some appropriate modification or correction words to guide the user to modify or correct the abnormal data. The way to replace abnormal data is to delete or insert abnormal data to make user data more consistent with the normal distribution or trend, such as using the natural language generation ability of large models to generate some appropriate modification or correction words to guide users to modify or correct abnormal data. The way to replace abnormal data is to delete or insert abnormal data to make user data more consistent with the normal distribution or trend, such as using the natural language generation ability of large models to generate some reasonable deletion or insertion words to guide users to delete or insert abnormal data.

Data integration

Data integration refers to some post-processing of user data, such as integrating data from different sources to build user portraits, so as to improve the integrity and value of user data. The purpose of data integration is to form a comprehensive and unified user perspective, such as the user's basic information, hobbies, consumption habits, purchase intentions, purchase behaviors, purchase results, etc., so as to make user data more useful and meaningful.

There are many ways to integrate data, such as:

• Integrate data from different sources: Data from different sources refers to user data coming from different platforms or channels, such as online and offline data, or website data, social media data, e-commerce platform data, physical store data, etc. Data from different sources may have different formats, structures, contents, qualities, etc., which require some transformation, matching, alignment, supplementation, etc., before they can be integrated. The way to integrate data from different sources is to use the natural language fusion ability of the large model to fuse data of different formats, structures, contents, and qualities together to form a unified and standard data set, such as using the natural language generation ability of the large model to generate some transformations, matching, alignment, and supplements, and guide users to integrate data from different sources.
• Build user portraits: User portraits refer to the analysis and induction of user data to form a representative and characteristic user model, such as the user's gender, age, region, occupation, education, income, family, interests, preferences, needs, problems, goals, motivations, behaviors, feedback, evaluation, etc. User personas can help us better understand the characteristics and needs of users, so as to provide users with more personalized and customized products and services. The method of constructing user portraits is to use the natural language analysis ability of the large model to classify, cluster, correlate, and infer some user data to form a hierarchical and logical user model, such as using the natural language generation ability of the large model to generate some classification, clustering, association, and inference words, and guide users to build user portraits.

The role of large models in data collection

The role of large models in data collection is very important and significant, it can help us obtain more, better and more useful user data, so as to provide more powerful support and guidance for our digital marketing. The role of large models in data collection is mainly reflected in the following aspects:

• The ability of large models to process large amounts of data: Large models can process massive amounts of data, extract valuable information from them, and generate useful content, thereby providing us with more user data, such as user behavior data, feedback data, evaluation data, etc. This data helps us understand our users' needs, preferences, behaviors, and feedback, so that we can optimize our product design, advertising, and user growth strategies.
• The ability of the large model to integrate multi-source data: The large model can integrate data from different sources to build a complete and comprehensive user portrait, so as to provide us with better user data, such as users' basic information, hobbies, consumption habits, purchase intentions, purchase behaviors, purchase results, etc. This data can help us better understand the characteristics and needs of our users, so that we can provide users with more personalized and customized products and services.
• The ability of large models to analyze complex data: Large models can analyze complex data and find meaningful patterns and trends, thereby providing us with more useful user data, such as user classification, clustering, association, and inference. This data helps us better understand the psychology and behavior of our users so that we can optimize our products and services.

summary

In this article, we introduce the methods and steps of how the big model obtains user data and improves the effectiveness of digital marketing, including:

• There are two main ways for large models to obtain user data: active acquisition and passive acquisition, which we can flexibly choose and combine according to different scenarios and purposes.
• There are two main sources of user data obtained by large models: online data and offline data, which we can flexibly select and combine according to different scenarios and purposes.
• After the large model obtains user data, it also needs to carry out some processing on user data, such as data cleaning, data integration, data analysis, etc., so as to improve the quality and value of user data and provide better support and guidance for our digital marketing.
• The role of large models in data collection is very important and significant, it can help us obtain more, better and more useful user data, so as to provide more powerful support and guidance for our digital marketing.
• Data security refers to the protection and respect of user data, such as complying with data protection regulations, encrypting the transmission and storage of data, and restricting data access rights, so as to ensure the security, integrity, and control of user data.

I hope this article can be helpful to you, if you want to know more about how to apply large models to optimize your business in digital marketing businesses such as e-commerce, advertising and marketing, and user growth, please pay attention to the column "Intelligent Marketing - How Large Models Empower Operations and Product Managers" in my personal account "Product Manager Lornshrimp" (the same number on the whole network), I will share more dry goods and cases there to make your digital marketing to the next level. Thank you for reading and supporting. ??

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