The application and practice of large models in digital marketing

What is a large model

Large models refer to deep learning models with a large number of parameters and layers that can learn complex features and rules from large-scale data to outperform humans on a variety of tasks. The emergence of large models is a major trend in the field of artificial intelligence in recent years, and it is also the future development direction. The advantage of large models is that they can use massive amounts of data to capture subtle and implicit information in the data, thereby improving the generalization ability and robustness of the model. At the same time, large models can also realize cross-domain and cross-task transfer learning, that is, use the knowledge learned in one domain or task to help solve problems in another domain or task. In this way, large models can reduce the dependence on annotated data, reduce development costs, and improve efficiency and effectiveness.

Several typical classes of large models

There are many types of large models, which can be broadly divided into the following categories according to their application fields and tasks:

• Large models in the field of natural language processing (NLP): This type of model is mainly used to process and generate natural language, such as text, speech, conversation, etc. Representatives of such models include GPT-3, BERT, XLNet, T5, etc. These models are usually pre-trained and fine-tuned, that is, unsupervised pre-training on a large corpus to learn the general knowledge of the language, and then supervised fine-tuning on a specific task to learn task-related knowledge. In this way, these models can achieve good results on various NLP tasks, such as text classification, named entity recognition, sentiment analysis, machine translation, text summarization, question answering, dialogue, etc. The advantage of these models is that they can use a large amount of text data to learn the grammar, semantics, logic, common sense, etc. of the language, so as to improve the understanding and generation ability of the model. At the same time, these models can also enable cross-language and cross-domain transfer learning, that is, using knowledge learned in one language or domain to help solve problems in another language or domain. In this way, these models can reduce the dependence on annotated data, reduce development costs, and improve efficiency and effectiveness.
• Large models in the field of computer vision (CV): This type of model is mainly used to process and generate visual information such as images and videos. Representatives of such models include ViT, DALL-E, CLIP, BigGAN, etc. These models are usually pre-trained and fine-tuned, that is, unsupervised pre-training on a large-scale image or video dataset to learn general knowledge of vision, and then supervised fine-tuning on a specific task to learn task-related knowledge. In this way, these models can achieve good results on various CV tasks, such as image classification, object detection, face recognition, image segmentation, image generation, video understanding, video generation, etc. The advantage of these models is that they can use a large amount of image or video data to learn the shape, color, texture, dynamic, scene, etc. of vision, so as to improve the recognition and generation ability of the model. At the same time, these models can also realize cross-modal and cross-domain transfer learning, that is, use the knowledge learned in one modality or domain to help solve problems in another modality or domain. In this way, these models can reduce the dependence on annotated data, reduce development costs, and improve efficiency and effectiveness.
• Large model in the field of recommendation system (RS): This type of model is mainly used to process and generate information such as user and product behavior, attributes, preferences, etc., so as to achieve personalized recommendation services. Representatives of such models are DIN, DIEN, BERT4Rec, RecBERT, etc. These models usually take an end-to-end approach, that is, directly from the raw data of users and products, extract features, build models, and make predictions. In this way, these models can achieve great results on a variety of RS tasks such as recall, sorting, filtering, scoring, commenting, interpretation, etc. The advantage of these models is that they can use a large number of data such as user and product behavior, attributes, and preferences to learn the interests, needs, and emotions of users and products, so as to improve the recommendation ability of the model. At the same time, these models can also achieve cross-platform and cross-scenario transfer learning, that is, use the knowledge learned on one platform or scenario to help solve the problem of another platform or scenario. In this way, these models can reduce the dependence on annotated data, reduce development costs, and improve efficiency and effectiveness.

This column introduces

The theme of this column is "Using AI to Drive Digital Marketing Performance Growth", which aims to introduce how to apply large models to optimize business in digital marketing businesses such as e-commerce, advertising and marketing, and user growth. This column will explain from the following aspects:

·        How to profile users in large models: User portraits refer to the analysis and description of users' basic information, behavioral characteristics, interests and preferences, spending power, etc., so as to form user labels and portraits. User portraits are the foundation of digital marketing, and they are also the prerequisite for personalized recommendations and precision marketing. This column will introduce how to use large models to profile users, including how to extract features from user behavior data, how to use large models to model users' interests and preferences, how to use large models to evaluate users' spending power and value, and how to use large models to segment and segment users.

·        How does the big model gain insight into traffic: Traffic refers to the user's visit and browsing behavior on digital platforms, which is an important resource for digital marketing and a key indicator for measuring business effectiveness. Traffic insights refer to the analysis and evaluation of the source, distribution, quality, and conversion of traffic, so as to optimize the acquisition and utilization of traffic. This column will introduce how to use large models to gain insights into traffic, including how to use large models to track and attribute the source of traffic, how to use large models to predict and allocate the distribution of traffic, how to use large models to evaluate and improve the quality of traffic, and how to use large models to optimize and improve the conversion of traffic.

·        How does the large model profile the industrial attributes of commodities: The industrial attributes of commodities refer to the basic information, functional characteristics, and quality level of commodities, which are the core content of digital marketing and an important factor affecting users' purchase decisions. The portrait of the industrial attributes of commodities refers to the analysis and description of the industrial attributes of commodities, so as to form the labels and portraits of commodities. The portrait of the industrial attributes of commodities is the basis for accurate recommendation and matching of commodities, and it is also the way to improve the competitiveness and value of commodities. This column will introduce how to use large models to profile the industrial attributes of commodities, including how to extract features from text, images, videos and other data of commodities, how to use large models to model the functional characteristics of commodities, how to use large models to evaluate the quality level of commodities, and how to use large models to classify and segment commodities.

·        How to use the large model to optimize the recall model: The recall model refers to screening out a part of the products that may be of interest from the massive product library according to the user's portrait and preferences, and use them as a candidate set for subsequent sorting models to sort. The recall model is the first step in digital marketing, and it is also a key link that affects user experience and satisfaction. This column will introduce how to use large models to optimize recall models, including how to use large models to model users' historical behaviors, how to use large models to capture users' real-time behaviors, how to use large models to mine users' potential needs, and how to use large models to filter and streamline recall results.

·        How to use the large model to optimize the filtering model: The filtering model refers to removing some products that do not meet the needs of users or are not suitable for display from the candidate set of the recall model according to the user's portrait and preferences, and use them as the filtering set for the subsequent sorting model to sort. The filtering model is the second step of digital marketing and an important part of ensuring user experience and satisfaction. This column will introduce how to use large models to optimize filtering models, including how to use large models to model users' personalized preferences, how to use large models to evaluate users' sensitivity and rejection, how to use large models to judge the compliance and suitability of products, and how to use large models to optimize and adjust the filtering results.

·        How to use the large model to optimize the sorting model: The sorting model refers to sorting the products according to certain rules and indicators in the filtering set of the filtering model according to the user's portrait and preferences, and display them to the user as the final recommendation result. The ranking model is the third step of digital marketing, and it is also the key link that determines user purchase behavior and performance growth. This column will introduce how to use large models to optimize the ranking model, including how to use the large model to predict the user's purchase intention, how to use the large model to evaluate the attractiveness and value of products, how to use the large model to optimize and balance the sorting rules and indicators, and how to use the large model to interpret and feedback the ranking results.

·        How to use large models to optimize ad creative: Ad creative refers to the copy, images, videos and other content of ads, which is the core element of digital marketing and an important factor that affects user clicks and conversions. Optimization of advertising creative refers to the generation and display of the most suitable advertising creative for users based on the user's portrait and preferences, as well as the industrial attributes of the product. The optimization of advertising creativity is an important means to improve the effectiveness and efficiency of advertising, and it is also an effective way to enhance brand image and value. This column will introduce how to use large models to optimize ad creative, including how to use large models to analyze users' emotions and psychology, how to use large models to refine the advantages and selling points of products, how to use large models to generate and optimize ad copy, images, videos and other content, and how to use large models to test and evaluate the effectiveness of ads.

·        How to use large models to optimize commodity pricing strategy: Commodity pricing strategy refers to determining the selling price and discount of commodities according to their industrial attributes, market demand, competitive conditions, etc., which is the core strategy of digital marketing and an important factor affecting user purchase behavior and performance growth. The optimization of commodity pricing strategy refers to the dynamic adjustment of the selling price and discount of the product according to the user's portrait and preference, as well as the industrial attributes of the product, so as to maximize revenue and profit. The optimization of commodity pricing strategy is an important means to improve the competitiveness and value of goods, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to use large models to optimize product pricing strategies, including how to use large models to evaluate users' spending power and sense of value, how to use large models to calculate the cost and profit of goods, how to use large models to analyze market demand and competition, and how to use large models to dynamically adjust and optimize the selling price and discount of products.

·        How to use large models to optimize ad matching strategies: Ad matching strategies refer to determining the most suitable ads to be displayed to users based on user portraits and preferences, as well as the industrial attributes of products, which is the core strategy of digital marketing and an important factor affecting user clicks and conversions. The optimization of the ad matching strategy refers to the dynamic adjustment of the ads displayed to users according to the user's portrait and preferences, as well as the industrial attributes of the product, to achieve maximum clicks and conversions. The optimization of ad matching strategy is an important means to improve the effectiveness and efficiency of advertising, and it is also an effective way to improve user experience and satisfaction. In this column, you'll learn how to use large models to optimize your ad matching strategy, including how to use large models to model users' interests and needs, how to use large models to evaluate the suitability and relevance of products, how to use large models to calculate the fit and priority of ads, and how to use large models to dynamically adjust and optimize the display and visibility of ads.

·        How to use large models to optimize ad bidding strategy: Ad bidding strategy refers to the amount of advertising bid that is determined to be displayed to users according to the user's portrait and preferences, as well as the industrial attributes of the product, which is the core strategy of digital marketing and an important factor affecting user clicks and conversions. The optimization of the ad auction strategy refers to the dynamic adjustment of the amount of advertising bids displayed to users according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to maximize revenue and profit. The optimization of ad bidding strategy is an important means to improve the effectiveness and efficiency of advertising, and it is also an effective way to improve user experience and satisfaction. In this column, we will introduce how to use large models to optimize ad bidding strategies, including how to use large models to predict users' click and conversion probabilities, how to use large models to calculate the revenue and profit of products, how to use large models to analyze market competition, supply and demand, and how to use large models to dynamically adjust and optimize ad bids and costs.

·        How to use large models to optimize user growth strategies: User growth strategy refers to determining the methods and means to attract and retain users according to user portraits and preferences, as well as the industrial attributes of goods, which is the core strategy of digital marketing and an important factor affecting user loyalty and repurchase rate. The optimization of user growth strategy refers to the dynamic adjustment of methods and means to attract and retain users according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to maximize the user scale and activity. The optimization of user growth strategy is an important means to improve user loyalty and repurchase rate, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to use large models to optimize user growth strategies, including how to use large models to predict user retention and churn risks, how to use large models to design user incentives and rewards, how to use large models to guide users' social interaction and sharing, and how to use large models to monitor and analyze user growth and activity.

·        How to use large models to optimize marketing campaign strategy: Marketing campaign strategy refers to determining the purpose, theme, content, time, and channel of marketing activities according to the user's portrait and preferences, as well as the industrial attributes of the product, which is the core strategy of digital marketing and an important factor affecting user purchase behavior and performance growth. The optimization of marketing campaign strategy refers to the dynamic adjustment of the purpose, theme, content, time, and channel of marketing activities according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to maximize user participation and response. The optimization of marketing campaign strategy is an important means to improve user purchase behavior and performance growth, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to use large models to optimize marketing campaign strategies, including how to use large models to analyze users' purchase cycles and rhythms, how to use large models to predict the seasonality and popularity of products, how to use large models to generate and optimize the purpose, theme, content, time, and channels of marketing activities, and how to use large models to evaluate and feedback the effectiveness of marketing activities.

·        How to use large models for effect evaluation: Effect evaluation refers to determining the indicators and methods for evaluating the effect of digital marketing business according to the user's portrait and preferences, as well as the industrial attributes of the product, which is the core link of digital marketing and an important basis for optimizing digital marketing business. The optimization of effect evaluation refers to the dynamic adjustment of indicators and methods for evaluating the effect of digital marketing business according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to achieve the most accurate and comprehensive effect evaluation. The optimization of effect evaluation is an important means to optimize digital marketing business, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to use large models for effect evaluation, including how to use large models to collect and analyze user behavior and feedback, how to use large models to count and calculate product sales and profits, how to use large models to evaluate and optimize the effectiveness of digital marketing business, and how to use large models to report and display the effect of digital marketing business.

·        How to integrate the advertising system combined with the large model: The advertising system refers to the automatic creativity, matching, bidding, display, feedback, etc. of advertising according to the user's portrait and preferences, as well as the industrial attributes of the product, which is the core system of digital marketing and an important platform for realizing digital marketing business. The optimization of the advertising system refers to the dynamic adjustment of the creative, matching, bidding, display, feedback, etc. of the advertisement according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to achieve the optimal advertising effect. The optimization of the advertising system is an important means to achieve digital marketing business, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to integrate the advertising system combined with large models, including how to use large models to optimize and upgrade each module of the advertising delivery system, how to use large models to optimize and upgrade the overall architecture of the advertising delivery system, and how to use large models to optimize and upgrade the operation and maintenance of the advertising delivery system.

·        How to design a DSP system that integrates a large model:D SP system refers to the demand-side advertising platform, which is an important part of the advertising system and an important channel to realize digital marketing business. The optimization of the DSP system refers to the dynamic adjustment of the advertising demand, advertising bidding, and advertising effect of the DSP system according to the user's portrait and preferences, as well as the industrial attributes of the product, so as to achieve the optimal DSP system effect. The optimization of DSP system is an important means to achieve digital marketing business, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to design a DSP system that integrates large models, including how to use large models to generate and optimize the advertising demand of the DSP system, how to use large models to calculate and optimize the advertising bids of the DSP system, how to use large models to evaluate and optimize the advertising effect of the DSP system, and how to use large models to optimize and upgrade the operation and maintenance of the DSP system.

• How to design a DMP system that integrates large models:D MP system refers to the data management platform, which is an important part of the advertising system and an important foundation for realizing digital marketing business. The optimization of the DMP system refers to the dynamic adjustment of the data collection, data processing, data analysis, and data application of the DMP system according to the user's portrait and preferences, as well as the industrial attributes of the commodity, so as to achieve the optimal DMP system effect. The optimization of DMP system is an important means to achieve digital marketing business, and it is also an effective way to improve user experience and satisfaction. This column will introduce how to design a DMP system that integrates large models, including how to use large models to optimize and expand the data collection of DMP systems, how to use large models to optimize and clean the data processing of DMP systems, how to use large models to optimize and mine the data analysis of DMP systems, and how to use large models to optimize and utilize the data applications of DMP systems.

The above is the introduction of the content of this column, I hope you can have a preliminary understanding and interest in the application and practice of large models in digital marketing. If you want to know more details and cases, welcome to follow my personal account "Product Manager Dugu Shrimp" (the same number on the whole network), in my column "Using AI to Drive Digital Marketing Performance Growth", I will explain to you in detail how to use large models to optimize your digital marketing business and make your performance fly!