AI in Marketing/Advertising, technical Analysis and Use Cases

Over the years, AI technology can be used for targeting or personalization, optimization, and other things related to leverage. 최근 Generative AI technology is evolving and expanding its use beyond its main domain of advertising/marketing into the creative space. Generative AI, including ChatGPT, midjourney, stable diffusion, and more, is a text-input is enough to create the right image, copywriting, and video, making the promise of automating marketing creatives a reality.The emergence of domestic startups specializing in AI services in the advertising field is also noticeable.

In the U.S., solutions that use AI to automatically create subject lines tailored to specific customers and find the right time to send marketing emails are already quite popular. New York Times argued that artificial intelligence and machine learning, which uses data and algorithms to mimic the way humans learn, have been quietly advancing advertising for years. One ad agency even rose to prominence in the industry based on specialized AI tools that helped clients write ad copy and boost their profile visibility on search engines.

Statistics on AI Marketing Benchmarks, marketers' 61.4% have used AI in their marketing efforts. And 19.2% of marketers have spent more than 40% of their marketing budget on AI-powered campaigns. 54.5% of incumbents say AI is likely to significantly improve their marketing efforts.

However, with the rapid development of AI, there are also limitations for the advertising and marketing industry. In this article, we'll discuss how AI can be used in advertising and marketing, including the technologies involved, use cases, why it's needed, and how to overcome limitations.

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Why Marketing/Advertising AI Matters

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15 Applications of Artificial Intelligence in Marketing. The most effective AI technologies for marketing across the customer life-cycle

AI Marketing Trends

According to a report from Gartner, By 2025, 80% of marketing processes will be automated using AI technology. The widespread adoption of AI in the marketing industry is due to its ability to automate processes, personalize customer experiences, and maximize return on investment (ROI). AI marketing is transforming the industry by streamlining marketing processes and allowing marketers to focus on strategic and creative tasks. According to the Influencer Marketing Hub's AI Marketing Survey, 61.5% of have used or are using AI in their marketing operations.

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How is advertising and marketing being impacted by AI?

Here are some of the areas where AI has been applied in advertising and marketing.

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Hyper-personalized content

AI-powered tools can generate content such as product descriptions, social media posts, and blog articles. These tools use natural language processing (NLP) and machine learning algorithms to generate content that is interesting and relevant to your target audience. In addition, you can write ad copy or create visual ad creatives.

The reason why the advertising and marketing industry is focusing on the "personalization" keyword is simple. In the past, in the age of mass media, the number of media the public was exposed to was not large and the number of companies investing in marketing and PR was not large, so it was all about reach and frequency of a strong key message. However, now that mobile has become popular, the number of influencers has increased to the point where one influencer is one media, people's interests have become fragmented and their lifestyles have diversified into a myriad of numbers, so personalized messages and perfectly targeted media selection and management according to each individual's Consumer Decision Journey has become extremely important.

As a result, marketing creatives have become tens of thousands of different types, depending on the customer segment and the nature of the media, and the task of producing them has become overwhelmingly human. With the help of AI, personalized marketing can analyze a customer's specific behavioral habits or location information to create personalized messages, and then automate exposure and optimization. In a marketplace that is already flooded with similar products, rather than highlighting the USPs of a product, it's important to create a Personalized marketing has also had an impact.

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Predictive analytics and ad targeting

AI's predictive analytics capabilities can help you analyze customer data using machine learning algorithms can be used to analyze customer data and enable optimization in your marketing campaigns. Customer response prediction is used to analyze which products customers are most likely to be interested in, and to predict the behavior of similar customers through experiments with different triggers for conversion. You can also find segments of your audience with similar behaviors and find conversion opportunities for them, and build data to improve the effectiveness of your marketing and advertising.

This AI-powered ad targeting allows you to use machine learning algorithms to identify the most effective audiences for your ad campaigns. The idea is to analyze customer data to identify the patterns and behaviors of customers that are most likely to lead to actual conversions. Of course, the accuracy of this predictive analytics depends on the quality of the 1st party data a company or brand has.

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Marketing automation and chatbots

Marketing automation tools are making it possible for a single marketer to do what used to take a team. They can automatically send fully personalized push messages or emails based on a customer's behavior, as if they were written by a human, or create periodic resurrections (actions to bring dormant customers back to active users) even after they've left your store or website. In B2B, automation tools can go from performing passive tasks like sending out invoices to making decisions like recognizing errors in sent documents and requesting corrections as AI is applied.

AI chatbots are one of the most popular AI marketing tools in recent years. Not only do AI chatbots allow businesses to provide instant CS and customer support during hours when humans are unavailable, but they also make the experience of talking to a bot more than just a Just like talking to a human, you can even provide expert troubleshooting. They can also be programmed to answer frequently asked questions, freeing up your customer support team to spend time on simple responses.

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Optimize for voice search

With voice assistants like Siri and Alexa already familiar to the public, businesses have become interested in optimizing for voice search. Unlike traditional UX, AI-powered voice search can help customers find what they're looking for more easily, based on experiences that are optimized for people's everyday behaviors.

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Marketing/Advertising AI Solutions

2023 AI Marketing Market Map

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AI marketing market map Influencer Marketing Hub — Influencer Marketing Hub

AI-powered marketing software is having a rapidly changing and disruptive impact on the marketing industry. From predictive analytics and chatbots to content creation and optimization, AI is transforming the way companies interact with customers and drive business results. While there are still challenges to overcome, the potential for AI marketing is huge. Companies that effectively utilize these tools will be able to gain an advantage in an increasingly competitive digital marketplace.

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Technologies used in marketing and advertising AI

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Natural Language Processing, NLP
Natural language processing is a technology that gives computers the ability to understand and interpret human language. The advertising industry uses NLP to analyze customer text data (e.g., social media posts, reviews, etc.) and perform sentiment analysis, keyword extraction, topic modeling, and more to understand consumer needs and tendencies or evaluate the effectiveness of advertising campaigns.
Generative Models
Generative models are techniques for generating new content from given data. For example, a Generative Adversarial Network (GAN) is used to generate fake images that look like the real thing. In the advertising industry, these generative models can be used to create creative, original design elements or effective ad campaigns with maximized visual impact. The various image generation models are appealing to marketers because they reduce the time and cost of creating advertising images and give them the freedom to bring in creative ideas.
Image Segmentation and Video Recognition
Image and video recognition is the ability of computers to understand and categorize visual data (images or videos). In the advertising industry, automatic tagging of image or video content, product recognition, face detection, and more enable precise targeting or personalized advertising strategies.
Recommendation Systems
Recommendation systems are technologies that analyze a user's preferences and behavior patterns to provide personalized recommendations to that user. Marketing and IT product planners and developers can improve customer experience and conversion rates by making personalized suggestions based on past purchase history or online behavior data, or by recommending similar products through product metadata.
Speech Recognition
Speech recognition is a technology that allows computers to understand and translate human voice commands or utterances. In the advertising industry, speech recognition is used for a variety of marketing strategies, including voice search and voice action execution. Other applications include text-to-speech, where scripts created by a large language model (LLM) are spoken aloud and embedded into videos.

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Marketing/Advertising AI AI Applications

Coca-cola

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In March 2023, Coca-Cola launched Via our blog An artificial intelligence platform that combines OpenAI's GPT-4 with image generation AIs like DALL-E and Midjourney ‘Create Real Magic' campaign. To accompany the announcement, we launched a contest to create original AI-powered creations using iconic Coca-Cola brand designs, including the company's distinctive outlined bottle and letter logo, as well as dozens of brand elements from our advertising archive, including our iconic Santa Claus and polar bears.

In an interview, Coca-Cola's global chief marketing officer explained that "Coca-Cola is still in the early stages of evaluating the potential impact of AI," but the Create Real Magic campaign is an example of how quickly generative AI is gaining traction in marketing. Coca-Cola's campaign was also a first step in the company's move to use AI to test ideas and adapt them quickly.

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Google

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The importance of 1st party data — More than two-thirds of agencies cited improving the use of 1st party data as a high or top priority for their clients over the next 24 months.
(source: AI Marketing Guide ② Enhancing your expertise with AI)

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1st party data is data that a company collects directly from customers or users. For example, your name or email address, phone number, or website activity or social media interactions. First-party data is considered the most valuable type of data for many businesses because it's the most accurate and reliable. It's also referred to as customer data because it's directly related to the products your brand sells and the customers who buy those products, and it's data that no other business has.

Against a backdrop of ever-increasing user expectations of companies and brands, as well as data compliance around the world, the use of first-party data has never been more important. As a result, those in the advertising and marketing industry are working closely together to securely leverage consumer data to create value.

Google can use machine learning to identify patterns in your search history, website browsing activity, and purchase history. After identifying these patterns, Google can use them to predict what you are likely to do next. For example, if a user has a history of searching for information about a particular product, Google can predict that the user is likely to be interested in that product. Google can then use this information to show the user relevant ads or product recommendations.

Another way is to use deep learning to create models that can predict individual behavior with high accuracy. For example, Google can use deep learning to create a model that can predict whether a user is likely to click on a particular ad or make a purchase.

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Marketing/Advertising AI Limitations and How to Overcome Them

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Top 7 Disadvantages of Using AI in Digital Marketing

Limits

AI helps businesses better understand their customers' interests and needs by analyzing their behavioral data, which allows them to deliver more relevant content and campaigns to their customers. AI can also automate marketing processes to make marketers more efficient, and help them measure and improve marketing performance.

However, AI marketing/advertising also has some limitations.

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Quality and quantity of data

AI learns from large amounts of data to discover patterns and make predictions, so the quality and quantity of data has a significant impact on AI performance. Insufficient or inaccurate data can lead to can lead to incorrect predictions or biased results.
In addition, an insufficient amount of data can prevent AI from making accurate predictions. For example, suppose a company wants to use AI to predict the success of a new product. In this case, the lack of data may make it difficult for AI to accurately predict the success of the product.

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Protecting privacy

AI analyzes user information to provide personalized advertising. However, privacy laws place restrictions on the processing of users' personal information.
The European Union's GDPR requires companies to obtain users' consent to collect and process their personal information. Therefore, companies must comply with privacy laws when creating AI-powered marketing strategies.

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Human touch and creativity

Artificial intelligence excels at pattern recognition and predictive analytics, but it's not yet Human emotion and creativity are not perfectly mimicked. They still require the intuitive judgment and creative thinking of human marketers.
For example, suppose a company uses AI to plan a new advertising campaign. In this case, AI can analyze the data and recommend the best creative or target audience. However, AI does not understand human emotions or come up with creative ideas like humans do. Therefore, companies need to leverage the intuitive judgment and creative thinking of human marketers when creating AI-powered marketing strategies.

In addition, advertising and marketing AI is being researched and debated to overcome limitations such as technical complexity and cost, transparency and trust, and copyright issues.

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How to overcome

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Tackle bias in AI algorithms and data collection
Bias comes from a variety of sources, including biased training data or algorithm design. To overcome these issues, it's important to recognize the importance of first-party data in your organization, and to build and manage data governance dimensions of enterprise operations are essential. It's also essential to implement a wide range of data sets, involving people with diverse backgrounds and perspectives. Regular audits and evaluations need to be conducted to identify and mitigate bias in algorithms.
Ensure data privacy and security
Organizations need a robust Privacy Framework and comply with applicable data protection regulations. This should entail anonymizing and aggregating data and implementing strong encryption techniques. Regularly monitoring and updating your security measures will also go a long way in protecting sensitive information.
Keeping human creativity and authenticity in content creation
AI can automate and streamline certain aspects of content creation, but it's hard to preserve human intuition or originality. It's important to strike the right balance between automated processes and human intervention. This will help preserve the inherent authenticity and creativity of human-created content.

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Bottom line: Marketing/advertising AI can yield much more accurate and original results, but human collaboration is still needed.

Marketing & Advertising AI With AI, marketing can become hyper-personalized, tasks can be automated, and creatives can be more accurate and creative. But AI is far from perfect, and this is where human involvement and collaboration are needed. Marketing and advertising is all about persuading customers, and to make it experiential, we need to utilize AI in a functional way, but with a human touch to make it even better than it already is.

In the coming years, marketers and advertisers will use AI to create unconventional and unforgettable customer experiences. As a marketer and a customer, I'm excited to see how this will change the game. ^^

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Reference.

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AI in Marketing/Advertising, technical Analysis and Use Cases

From generating hyper-personalized messages on your own from your customer's data.