Greetings,
I am Kakeya, the representative of Scuti Jsc.
At Scuti, we specialize in offshore and lab-based development in Vietnam, leveraging the power of generative AI. Our services include not only development but also comprehensive generative AI consulting. Recently, we have been privileged to receive numerous requests for system development integrated with generative AI, reflecting the growing demand for innovative AI-driven solutions.
Now, the use of generative AI in the marketing industry is rapidly advancing. Wouldn’t you like to incorporate generative AI into your company to significantly improve operational efficiency?
In this article, we explain how generative AI is revolutionizing marketing activities, achieving personalization and automation. Through specific success stories and applications, you will be able to appreciate its effectiveness.
The Basics and Technical Foundation of Generative AI
What is Generative AI?
Generative AI is a type of artificial intelligence that automatically generates content such as text, images, and audio.
Generative AI has the ability to create new content based on vast datasets. This technology utilizes advanced techniques like deep learning and neural networks, making it possible to produce more natural and human-like outputs.
The history of generative AI can be traced back to the early attempts at artificial intelligence. Initial generative AI systems were based on relatively simple rule-based systems. However, as technology evolved, more sophisticated algorithms were developed, leading to the emergence of advanced generative AI as we know it today. Particularly, the advancements in deep learning since the 2010s have led to the rapid evolution of generative AI.
Typical functions of generative AI include text generation, image generation, and audio generation. In text generation, it can automatically create news articles and creative writing. In image generation, it can produce images of faces or landscapes that do not exist in reality. For audio generation, it can mimic specific voice qualities to create audio content.
Thus, with its diverse functions and wide range of applications, generative AI is expected to be utilized in various fields, including marketing.
Technical Foundation and Evolution of Generative AI
The technical foundation of generative AI lies in machine learning and deep learning. These technologies form the core that enables generative AI to learn from data and produce natural content.
Machine learning is a technology that learns patterns and rules from data and uses them to make predictions and classifications. Early machine learning utilized simple algorithms such as linear regression and decision trees, but these had limitations in learning complex data patterns.
Deep learning is a subfield of machine learning that specifically uses artificial neural networks. By constructing multi-layer neural networks, it can learn more complex data patterns.
The evolution of this technology has led to significant performance improvements in generative AI. In particular, the introduction of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models has greatly expanded the capabilities of generative AI.
There are three main approaches to model training in generative AI:
- Supervised Learning: This approach uses labeled data to train the model. By providing correct data, the model learns to generate accurate outputs. For example, in image captioning, a large number of image and description pairs are prepared for the model to learn from.
- Unsupervised Learning: This approach uses unlabeled data to train the model. It learns the latent patterns and structures within the data. Generative Adversarial Networks (GANs) are an example of unsupervised learning, where two neural networks compete to generate realistic data.
- Reinforcement Learning: This approach involves an agent learning by interacting with the environment and receiving rewards. It is applied in interactive systems and game AI. The agent learns the optimal actions through trial and error to find the best content generation methods.
Application of Generative AI in Marketing
Methods and Effects of Content Generation
Generative AI has significantly transformed content generation methods in the marketing field, maximizing their effectiveness. Here, we specifically explain the methods and effects of generating text, images, and videos.
Text Generation
Text generation is one of the most widely utilized areas of generative AI. AI can automatically generate news articles, blog posts, and product descriptions. For example, OpenAI’s GPT series learns from large amounts of text data and generates natural, human-like text. By leveraging this technology, marketing teams can efficiently create diverse content and appeal to many targets in a short time. The effects include significant reduction in work time, decrease in human errors, and consistent messaging.
Image Generation
Using technologies like Generative Adversarial Networks (GANs), high-quality images for advertisements and social media posts can be automatically generated. For instance, fashion brands use AI to generate new designs and styles for promotion. This technology reduces design costs and improves the ability to quickly adapt to the market while maintaining brand uniqueness.
Video Generation
Video generation is a particularly rapidly evolving area of generative AI. AI can automatically create short video clips and animations. For example, platforms like Synthesia synthesize the speaker’s face and voice based on text to generate promotional videos. This technology makes it easy to create personalized video messages, enhancing engagement with consumers.
Practice and Examples of Personalization
Generative AI has a significant impact on personalization in marketing.
By utilizing customer data to create customized messages tailored to individual needs and preferences, more effective marketing strategies can be achieved.
Collection and Analysis of Customer Data
Customer data includes purchase history, website behavior data, social media activities, survey results, etc. Based on this data, the interests and concerns of customers are identified, and customized messages are generated accordingly.
Creation of Customized Messages
For example, in email marketing, by analyzing customers’ past purchase history and browsing history, personalized emails recommending the most suitable products and services for each customer are automatically generated. This increases the relevance of the messages, thereby improving open and click-through rates.
Concrete examples include companies like Netflix and Amazon. These companies use vast amounts of customer data to display personalized content and product recommendations for each customer.
Netflix leverages viewing history and rating data to recommend the best movies and dramas for individual users, increasing viewing time. On the other hand, Amazon analyzes purchase history and browsing history to provide personalized product recommendations, contributing to increased sales.
Efficiency through Marketing Automation
By utilizing generative AI, marketing activities can be automated, significantly improving operational efficiency. Notably, substantial effects can be seen in campaign automation and customer segmentation.
Campaign Automation
Generative AI automates the design, execution, and optimization of marketing campaigns. For example, in email marketing, AI analyzes customer behavior data and interests to send the right content at the optimal time. This automation frees the marketing team from individual campaign settings, allowing them to focus on strategic tasks. Additionally, real-time performance monitoring and feedback-based optimization are possible, maximizing campaign effectiveness.
Customer Segmentation
This process uses generative AI to group customers based on various attributes and behaviors. This allows marketing messages to be customized for each segment, achieving more effective communication. Generative AI quickly analyzes large volumes of customer data, detecting subtle patterns that traditional methods might overlook. For example, it can provide detailed insights into customer preferences and behavior patterns based on purchase history, website visits, and social media activities.
The benefits of such automation include the following:
- Time and Cost Savings: Compared to manual processes, the time and cost associated with campaign setup and customer analysis are significantly reduced.
- Improved Personalization: By sending appropriate messages to each customer segment, engagement and conversion rates are enhanced.
- Real-Time Response: Quickly responding to changes in customer behavior allows for timely marketing activities.
Concrete examples include marketing automation platforms like HubSpot and Salesforce. These platforms leverage generative AI to automate campaign management and customer segmentation, dramatically improving marketing efficiency.
Concrete Examples of Generative AI Utilization
Detailed Explanation of Success Stories
The utilization of generative AI has led to concrete successes in various companies. Here, we will detail the cases of Carvana and Coca-Cola.
Carvana’s Video Generation
Carvana, an online platform for purchasing cars, uses generative AI to enhance the customer experience. Particularly noteworthy is the generation of personalized videos for individual customers.
Carvana analyzes customer purchase data and browsing history to automatically generate videos that recommend potential vehicles for purchase. This allows customers to efficiently find vehicles that meet their needs, increasing their purchase intent. Additionally, video generation using generative AI contributes to reducing production time and costs, enabling quick responses to a larger number of customers.
Coca-Cola’s Campaign Case
Coca-Cola leverages generative AI in its marketing campaigns to strengthen customer engagement. Specifically, AI is used to analyze consumer preferences and behavior data, enabling the deployment of optimized advertisements and promotions for each consumer.
For example, customized campaigns are conducted tailored to specific regions or events, delivering personalized messages to each consumer and achieving high advertising effectiveness. The introduction of generative AI allows Coca-Cola to make real-time adjustments to campaigns, enhancing the flexibility and effectiveness of its marketing activities.
Generative AI Use Cases by Industry
Generative AI is utilized across various industries, achieving significant results in each field. Here, we introduce specific examples in the retail and entertainment industries.
Retail Industry
In the retail industry, there are many examples of improving customer experience through the use of generative AI. Particularly, Amazon employs generative AI to provide personalized product recommendation systems. AI analyzes customers’ purchase history, browsing history, and rating data to recommend the best products for each customer. This makes it easier for customers to find products that match their interests, increasing their purchase intent.
Additionally, AI-powered chatbots are also being introduced to quickly and accurately respond to customer inquiries, improving the quality of customer service.
Entertainment Industry
In the entertainment industry, the use of generative AI is also advancing. Netflix uses generative AI to provide personalized content recommendations to viewers.
A system that automatically recommends movies and dramas matching the viewer’s preferences based on viewing history and rating data has been introduced. This makes it easier for viewers to find content that suits them, increasing viewing time. Additionally, generative AI is also used in the creation of new content, with examples including AI providing script ideas and improving the efficiency of video editing.
Benefits and Effects of Introducing Generative AI
Examples of Efficiency and Cost Reduction
The introduction of generative AI has enabled many companies to achieve operational efficiency and cost reduction. Here, we introduce specific examples of time and cost savings through automation.
Time Reduction
Generative AI contributes to the automation of various business processes.
For example, in marketing operations, the automation of campaigns using AI is progressing. In one company, generative AI is used to automatically generate hundreds of email templates and send them to target customers at the optimal time. This significantly reduces the time required for manual email creation and sending, allowing the marketing team to focus on strategic tasks.
Additionally, the automation of data analysis by AI is also advancing, enabling real-time market trend analysis and supporting quick decision-making.
Cost Reduction
For instance, in customer support, the introduction of AI chatbots is progressing. In one large company, AI chatbots handle about 70% of customer inquiries, significantly reducing personnel costs.
AI chatbots operate 24/7, quickly resolving customer issues and improving customer satisfaction. Furthermore, the optimization of advertising campaigns using generative AI has resulted in reduced advertising costs and improved return on investment.
By automatically selecting the optimal ad creatives and delivery timing, AI minimizes unnecessary advertising expenses and enables effective marketing activities.
Enhancing Creativity and Its Impact
The introduction of generative AI enhances corporate creativity and significantly impacts the generation of diverse content. Here, we introduce specific examples of idea generation and diverse content creation.
Idea Generation
Generative AI supports the creation of innovative ideas in the creative process.
For example, in planning advertising campaigns, AI analyzes market data and consumer trends to propose optimal advertising concepts. This allows the marketing team to come up with novel ideas that traditional methods might not consider.
Additionally, AI learns from past campaign data, extracting success factors to quickly provide effective ideas.
Diverse Content Generation
Generative AI enables the automatic generation of various types of content, such as text, images, and videos.
For instance, media companies use generative AI to automatically generate news articles. AI writes articles based on large amounts of data, allowing for the creation of numerous articles in a short time. This enables timely information delivery to readers and enhances the competitiveness of media companies.
In the fashion industry, generative AI is used to propose new designs and styles. AI analyzes past fashion trends and consumer preferences to automatically generate the latest designs. This technology allows designers to efficiently create diverse designs and respond quickly to market needs.
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Impact of Generative AI
The impact of generative AI on enhancing creativity is extensive. Firstly, the diversity of ideas expands, enabling companies to engage in more creative projects.
Additionally, the efficiency of content generation improves, allowing for the production of a large volume of high-quality content in a short time. This helps companies capture and maintain customer interest, enhancing engagement.
By leveraging generative AI, companies can significantly boost their creativity and quickly generate diverse content, establishing a competitive edge in the market. This leads to expected business growth and development.
Risks and Countermeasures of Introducing Generative AI
Types of Major Risks in Generative AI
While there are many benefits to introducing generative AI, there are also risks such as data bias and copyright issues.
Data Bias
Generative AI learns from vast amounts of data, but if the training data contains bias, there is a risk that the generated content will also reflect that bias.
For example, if a dataset contains biases towards a particular race or gender, those biases may appear in the AI’s output. This can negatively impact the brand image.
Copyright Issues
Generative AI creates new content based on existing data, which can lead to copyright issues. Especially if copyrighted material is used without permission, there is a risk of copyright infringement. This can result in legal disputes and compensation issues.
To address these risks, the following countermeasures are necessary:
- Data Bias Countermeasures: It is important to select unbiased data and regularly review the data. Additionally, establishing an evaluation process to monitor AI outputs for bias can be effective.
- Copyright Issues Countermeasures: Ensure that the data and materials used are properly licensed and establish guidelines to avoid legal risks. It is also important to implement a process to evaluate AI-generated content for copyright issues and confirm that there are no problems.
Specific Measures for Risk Mitigation
While the introduction of generative AI offers many benefits, it is crucial to implement specific measures to mitigate risks. Here, we discuss risk management through human review and data scrutiny.
Human Review
Introducing human review for content generated by generative AI is effective in mitigating risks. Experts check the AI’s output for errors and biases. This process ensures the quality and reliability of the content generated by AI.
For example, in generating content for marketing campaigns, it is recommended that creative directors or marketing teams review the AI’s output and make necessary adjustments.
Data Scrutiny
Scrutinizing the datasets used for training generative AI is also an important measure. Ensuring that the data used is free from biases and errors and is diverse helps minimize AI bias.
Specific methods for data scrutiny include carefully selecting the sources of datasets and regularly updating the data. Additionally, it is essential to remove unnecessary data and noise during the data preprocessing stage.
Future Prospects of Generative AI and Its Impact on Marketing
Evolution of Generative AI Technology and Its Impact
Generative AI technology is rapidly evolving, and this evolution significantly impacts the marketing field. Notably, the advancements in multimodal AI and autonomous AI are key points of interest.
Evolution of Multimodal AI
Multimodal AI is a technology that can integrate and process multiple data formats such as text, images, and audio. This evolution enables generative AI to produce richer and more complex content.
For example, it becomes easier to create advertisements combining text and images or interactive marketing content integrating audio and video. This strengthens engagement with customers and enhances the effectiveness of marketing campaigns.
Evolution of Autonomous AI
Autonomous AI is a technology that can learn and adapt with minimal human intervention. This allows AI to analyze market changes and customer behavior in real-time, automatically proposing and executing optimal marketing strategies.
For example, AI can automatically analyze consumer purchasing patterns and provide the best offers and promotions to each consumer. This improves the accuracy of marketing and allows for the efficient allocation of resources.
Impact on Marketing
These technological advancements affect every aspect of marketing.
For example, the personalization of campaigns advances further, with customized messages and offers automatically generated for each customer. This enhances customer satisfaction and is expected to strengthen brand loyalty.
Additionally, the evolution of AI leads to more advanced data analysis, allowing for quicker and more accurate development of marketing strategies.
Enhancing Customer Experience and Creating New Business Models
The evolution of generative AI dramatically improves customer experience and promotes the creation of new business models. Here, we explain how generative AI evolves customer experience and generates innovative business models.
Evolution of Customer Experience
Generative AI enhances customer experience by providing personalized services.
For example, AI learns from customers’ past behaviors and preferences, generating individually customized product recommendations and marketing messages. This makes it easier for customers to find the products and services best suited to them, improving satisfaction.
Additionally, AI tools like chatbots and virtual assistants offer 24/7 support, providing quick and accurate responses.
Creation of New Business Models
Generative AI technology leads to the creation of new business models. For instance, by automatically generating content, companies can reduce content creation costs and respond quickly to market demands. This allows them to explore new revenue streams, such as subscription models and on-demand services.
Realization of New Marketing Strategies
By analyzing data in real-time and quickly responding to customer needs, AI enables new marketing strategies such as dynamic pricing and customized promotions.
For example, on online shopping platforms, AI can analyze browsing and purchase history to present the best offers at the moment. This increases customers’ purchase intentions and boosts sales.