AI-Powered Marketing Strategies: Targeting and Personalization

AI-driven Marketing Strategy: Revolutionizing Targeting and Personalization

Artificial intelligence (AI) is also bringing revolutionary changes to the field of marketing. AI is now able to achieve advanced targeting and personalization that matches the needs and behavior of customers, something that was difficult to do with traditional marketing methods. In this article, we will explain in detail the basic concepts of AI marketing, specific strategies, use cases, and future prospects.

What is AI marketing?

Definition and Overview of AI Marketing

AI marketing is an outline of a methodology that utilizes artificial intelligence (AI) technology to improve the efficiency of marketing activities, measure their effectiveness, and support decision-making. AI analyzes large amounts of data at high speed and makes discoveries that humans cannot make, making it possible to think of and execute more effective marketing strategies.

Differences from traditional marketing

Traditional marketing often relies on generic methods based on experience and intuition, making it difficult to measure effectiveness. On the other hand, AI marketing is more accurate because it utilizes calm analysis results based on data. AI can also provide a personalized approach to customers based on their behavior and preferences.

Reasons for the popularity of AI marketing and its benefits

The reason why AI marketing is attracting attention is because of its diverse benefits.

• Efficiency: AI can automate and streamline various marketing tasks, such as data analysis, content creation, and ad delivery, allowing marketers to focus on more strategic tasks.
• quickly and continuously measure effective immediate results.
• Personalization: AI can serve personalized content and ads based on customer behavior and preferences, which can increase customer satisfaction and improve conversion rates.
• Cost reduction: AI can also help reduce costs by optimizing advertising budgets and reducing unnecessary ad serving.
• Discover new customer insights: AI can find hidden patterns and correlations in data that are difficult for humans to spot. This allows you to gain new customer insights that can be used to inform your marketing strategies.

AI targeting strategy

AI is a powerful tool for increasing the accuracy of targeted advertising. Here we will explain targeting strategies that utilize AI.

Customer Segmentation

Customer segmentation is the classification of customers into groups that share common characteristics. AI can analyze data such as customer demographic attributes (age, gender, location, etc.), interests , and behavioral history (purchase history, website browsing history, etc.) to create the best possible customer experience.

There are various methods for customer segmentation using AI.

• RFM Analysis: A method of identifying customers based on their recency, frequency, and monetary value.
• Clustering: A technique for grouping customers based on their similarities.

Customer segmentation using AI contributes to more precise targeting of advertising. For example, by delivering advertising with messages and creatives that are tailored to a specific customer base, advertising effectiveness can be maximized.

Predictive modelling

Predictive modeling is a method of forecasting a customer’s future behavior based on past data and current circumstances. AI can use machine learning algorithms to predict a customer’s likelihood of purchase, churn rate, lifetime value, and more.

AI-based predictive modeling uses a variety of algorithms.

• Decision tree: A method for predicting customer behavior by building a tree-state model based on customer attributes and behavioral history.
• Logistic regression: A method used to classify customer behavior into binary categories (e.g. buy/don’t buy, churn/don’t buy).
• Random Forest: A method to improve prediction accuracy by knowing multiple decision trees.

AI-powered predictive modeling can help identify and improve marketing strategies, for example by taking a more proactive approach to customers with a high probability of purchasing, or by implementing retention initiatives for customers with a high churn rate.

Lookalike Modeling

Lookalike modeling is a method to discover potential customers with similar attributes to a given customer. AI analyzes the attributes and behavioral patterns of a given customer and identifies potential customers with seemingly similar characteristics. This can improve the accuracy of advertising targeting and lead to new customer acquisition.

• Example: An online fashion store used lookalike modeling to deliver targeted ads to users with similar attributes to their loyal customers, resulting in a 20% increase in ad conversion rates and a significant increase in new customer acquisition.

AI-driven personalization strategy

AI can improve customer satisfaction and contribute to increased customer satisfaction and sales. Here we will explain personalization strategies using AI.

Recommendation Engine

A recommendation engine is a system that recommends products and content based on a customer’s behavioral history, preferences and tastes.

• Collaborative filtering: This technique groups users who buy similar products or view similar content together, and recommends products or content that are popular within that group.
• Content-based filtering: This method analyzes the characteristics of products and content and recommends similar products and content based on a customer’s past behavioral history and preferences.

Recommendation engines are used in a variety of services, including e-commerce sites, video distribution services, and music streaming services. By proposing optimal products and content to customers, they can increase purchase value and frequency of use.

• Example: Amazon’s recommendation engine analyzes customer purchase history, browsing history, ratings, etc., and displays recommendations such as “People who bought this product also bought these products.” It is said to account for 35% of Amazon’s sales, and its effectiveness is enormous.

Dynamic Pricing

Dynamic pricing is a method of changing prices rapidly based on demand and supply.

Dynamic pricing is used in a variety of businesses, including airline ticket and hotel reservations, e-commerce sites, etc. It allows you to maximize your revenue by raising prices when demand is high and lowering prices when demand is low.

• Example: Uber introduced dynamic pricing, adjusting the free price as needed, which helps drivers earn more and shorten customer journeys.

Personalized Content Generation

Generative AI can automatically generate personalized content based on customer properties and behavioral history. By optimizing various content such as emails, websites, and advertisements for each customer, you can improve customer experience and increase conversion rates.

• Examples: Netflix increases user viewing time by showing personalized recommendations, and Spotify analyzes users’ music preferences and creates personalized playlists.

Personalized customer service with chatbots

AI-powered chatbots can respond in a personalized manner to the needs and circumstances of customers. They provide services that were difficult to provide with traditional customer support, such as 24/7 support and multilingual support, and this can improve customer satisfaction.

• : At one e-commerce site, a generative AI chatbot is introducing recommended products and answering questions based on customers’ past purchases and browsing history. This helps customers select products and proceed with the purchase process, improving customer satisfaction.

AI Marketing Tools

To effectively implement AI marketing, it is important to choose the right tool. Here, we will introduce the main AI marketing tools and explain the key points to consider when selecting a tool.

Major AI marketing tools

• Adobe Sensei: This is an AI feature that has been gaining attention in Adobe Creative Cloud products. AI assists with various creative tasks such as image editing, video editing, and design creation. For example, it can automatically correct images, remove unwanted objects, and automatically generate subtitles for videos.
• Salesforce Einstein: An AI feature in Salesforce’s CRM (customer relationship management) platform. AI supports various marketing and sales activities, such as analyzing customer data, lead scoring, and automating sales activities. Einstein uses past customer data to predict which customers are most likely to close a deal and advises sales representatives on how to approach them first.
• HubSpot: HubSpot is a platform that integrates marketing, sales, and customer service. It offers AI-powered chatbots, content optimization tools, lead nurturing tools, and more. For example, HubSpot’s chatbots can automatically answer customer inquiries and turn conversations with website visitors into lead nurturing.
• Albert: Albert is an AI platform that automates digital advertising operations. It optimizes advertising budgets, selects target audiences, creates ad creatives, and more. It automatically adjusts budget allocation and targeting.
• Persado: Persado is an AI-powered marketing language platform that optimizes ad copy, email file names, landing page text, and more, based on sentiment analysis and behavioral psychology. It suggests wording and phrasing to improve ad click-through rates and conversion rates.

Tool selection points

When choosing an AI marketing tool, consider the following points:

• Purpose: Be clear about the purpose for which you want to use the AI tool. For example, the best tool will vary depending on the purpose, such as customer segmentation, demand forecasting, or ad copy creation.
• Budget: Prices of AI marketing tools vary widely depending on features and scale. Choose the right tool based on your company’s budget.
• , you need to check what segmentation methods it supports and what data analysis functions it has.
• Ease of use: It is important that the interface is clear and easy to use. Also, make sure it is available in your local language.
• Integration: Make sure that the tool can be integrated with other systems and tools. For example, by choosing a tool that can be easily integrated with a CRM system or MA (marketing automation) tool, you can make the most of your data.

AI Marketing Success Stories

AI marketing has been introduced in most companies, and there are many success stories. Here, we will introduce overseas cases by industry and reporting purpose.

E-commerce website

• Case study: A major e-commerce site introduced an AI-based recommendation engine to recommend products that matched customers’ interests. As a result, customers became more cautious about their purchases, and customer and purchase frequency improved.
• Purpose: Increase sales, improve customer satisfaction

Retail

• Case study: A major supermarket chain is introducing an AI-based demand forecasting system to reduce food waste. The system analyzes past sales data, weather data, and other data to forecast demand for each product. Calculate the optimal order quantity.
• Purpose: Cost reduction, inventory optimization

Finance

• Case Study: A bank introduced an AI-based fraud prevention system to detect credit card fraud early and prevent damage. The system learns fraud patterns from past fraud data. , and monitor transactions to expose fraud.
• Purpose: Risk management, security enhancement

Manufacturing

• Case study: An automobile manufacturer introduced an AI-based quality control system to improve the detection rate of defective products on the production line. The system uses AI to analyze image data of products and automatically detect defects such as scratches and stains.
• Purpose: Improve quality and efficiency

others

• Case study: An airline introduced an AI-based dynamic pricing system to optimize ticket prices according to demand, successfully maximizing revenue and reducing seat vacancies.
• Objective: Maximize profits, optimize prices

These cases show that AI marketing is being used in a wide range of industries for a variety of purposes, including increasing sales, reducing costs, improving customer satisfaction, and managing risks.

Issues and points to note about AI marketing

To maximize its potential, AI marketing requires keeping in mind some challenges and caveats.

Data quality and quantity

The performance of an AI model depends heavily on the quality and quantity of training data. High-quality data is accurate, comprehensive, and unbiased data. Any deviations can reduce the accuracy of the AI model and lead to erroneous analysis results and predictions.

• Data collection: In order to collect the necessary data, it is necessary to consider appropriate data sources and establish data collection methods. In addition, it is necessary to comply with laws such as the Personal Information Protection Act and to give due consideration to protecting the privacy of data.
• Data cleaning: Collected data may contain errors or missing values. This data must be cleaned to make it suitable for analysis. Data cleaning is one of the most time-consuming and labor-intensive tasks in AI marketing, but it is an essential process to improve the accuracy of the model.
• Data bias: If there is bias in the learning data, the AI model may output biased results. For example, if there is a lack of data for a particular region or age group, the accuracy of predictions for that group may be low. To reduce data bias, it is necessary to collect diverse data sets and develop algorithms to correct bias.

Privacy Protection

AI marketing often involves collecting and using data such as customers’ personal information and behavioral history, so privacy protection must be taken into consideration. The purpose of use must be clearly disclosed and appropriate security measures must be in place.

• Personal Information Protection Act: The Personal Information Protection Act is a law that sets out rules regarding the collection, use, provision, etc. of personal information. AI marketing also requires compliance with the Personal Information Protection Act and thorough appropriate handling of personal information.
• GDPR (General Data Protection Regulation): GDPR is a regulation on personal data protection within the EU. Companies doing business within the EU must comply with GDPR. GDPR sets strict standards for the protection of personal information. If you are caught, you may be subject to high penalties.
• Privacy Policy: Companies must publish and disclose to users a privacy policy that secures the collection and use of personal information, the method of use, and personal information provided to third parties. It is important to make it easy for users to access.
• Security measures: Data such as personal information and behavioral history are at risk of cyber attacks and information leaks. When it comes to AI marketing, it is necessary to ensure the safety of data while taking strong security measures.

The AI black box problem

AI, especially deep learning models, have complex structures, so it can be difficult for humans to understand why they have output such results. Since the basis for judgment is unclear, it becomes a matter of judgment as to whether the output results of AI can be trusted.

• Explainable AI (XAI): Research and development of explainable AI (XAI) is underway to solve the black box problem. XAI is a technology that explains the reasons for AI decisions in a way that humans can understand, and by increasing the transparency of AI, it contributes to improving the understanding of AI.
• Progression of AI models: By utilizing tools that progress the internal structure and learning process of AI models, it becomes easier to understand how the AI behaves.
• Final decision by humans: Do not blindly accept the decisions made by AI, but rather have the final decision made by a human. In particular, when making important decisions, it is important to thoroughly check the basis for the AI’s decisions.

Ethical issues

AI marketing can also raise ethical questions.

• Discrimination: AI may make discriminatory decisions based on certain attributes, such as sex, gender, age, sexual orientation, etc. For example, it is ethically problematic for AI to display unfavorable advertisements to certain demographics or make recommendations that exclude certain demographics.
• Manipulation: AI can analyze human psychology and behavior and generate ads and content that encourage purchases or compete for certain actions. This can violate autonomy, so care must be taken.
• Transparency: It is necessary to disclose to users how AI collects and uses data, and what algorithms are used to make decisions. This will dispel distrust and promote ethical use of AI.

To address these ethical issues, it is important to establish guidelines on AI ethics and for AI developers and users to take ethical responsibility.

cost

Introducing an AI marketing tool involves various costs, including not only initial costs but also operation and maintenance costs and human resource development costs.

• Initial cost: The cost of implementing an AI marketing tool varies greatly depending on the type, function, and scale of the tool. For cloud-based tools, you can consider the initial cost, but for on-premise tools, you will need to purchase hardware and software.
• Operation and maintenance costs: AI marketing tools require regular updates and maintenance. In addition, continuous data collection and analysis is also required to maintain and improve the accuracy of the AI model. This is a must-have.
• Human resource development costs: In order to use AI marketing tools effectively, you need personnel with knowledge and skills in AI. It is also necessary to spend a fair amount on developing AI personnel through in-house training and external seminars.

To maximize the benefits of introducing AI, it is important to choose the appropriate tool while considering the balance between cost and effectiveness.

AI-generated marketing strategies

Generative AI will further evolve traditional AI marketing and enable new marketing strategies. Here, we will explain content marketing, ad creatives, and personalized ads that utilize generative AI.

Content Marketing

AI generation can automatically generate content such as blog articles and social media posts, reducing the time and cost involved in content production and allowing you to produce more content more efficiently.

• Auto-generate blog articles and social posts: Generative AI can generate natural-sounding, human-like copy based on keywords and themes, freeing up writers and editors to focus on more creative tasks.
  • Example: A certain web media uses AI to automatically generate news articles, which allows them to deliver more news quickly.
• Generate SEO-friendly content: Generative AI can automatically generate content with SEO-friendly keywords and phrases, helping you rank better in search engines and earn more rewards.
  • Example: An e-commerce site uses AI to automatically generate product description text for product pages. By creating product description text optimized for SEO, the site is able to increase traffic from search engines and contribute to increased sales.
• Creating video scripts and ad copy: Generative AI can automatically generate video scripts and ad copy, allowing creators to focus on more creative tasks.
  • Example: An ad agency is using generative AI to automate the creation of demographic-specific ad copy, reducing ad production time and allowing them to test more ad variations.

Ad creative

By utilizing image generation AI and video generation AI, generative AI can automatically generate advertising creatives such as banner ads and video ads.

• Create banner ads and video ads using image generation AI: Generative AI can automatically generate banner ads and video ads based on text and images. This allows you to focus on your work.
  • Example: An apparel brand is using image generation AI to automatically generate banner ads that match the season and trends, reducing the cost of advertising production and enabling more timely advertising.
• Optimizing ad design to target audience: Generative AI can optimize ad design to match consumer attributes and interests, which can improve ad click-through rates and conversion rates.
  • Example: A cosmetics manufacturer uses AI to automatically generate ad designs tailored to different age groups and skin types. This allows them to deliver ads that resonate with their target audience, helping to increase sales.

Personalized ads

AI can be generated and used to deliver personalized ads based on customer attributes and behavioral history, allowing you to display ads that are tailored to your customers’ interests and maximizing advertising effectiveness.

• Ad delivery based on customer attributes and behavioral history: Generative AI can analyze data such as customer age, gender, place of residence, purchase history, and website browsing history to deliver ads that are most suitable to each customer.
  • Example: An ecommerce site uses AI to generate recommendations based on customers’ past purchase and browsing history, helping to improve sales by giving customers a higher rating for their purchases.
• Maximizing advertising effectiveness through sudden bidding: Generative AI can maximize advertising effectiveness by temporarily adjusting advertising bid prices.
  • Example: Criteo provides an AI-powered conversational bidding platform that allows advertisers to optimize their ad click limits and conversion rates.

AI Marketing Success Stories

AI marketing has been introduced in most companies, and there are many success stories. Here, we will introduce overseas cases by industry and reporting purpose. Let’s see how it solves marketing problems and contributes to business growth.

E-commerce website

• Example: Amazon
  • Problem: Recommend products that match the customer’s interests from a number of possible products.
  • AI: Developed a recommendation engine that combines browser ring and content-based filtering. Analyzes customer purchases, browsing history, ratings, etc. to provide personalized product recommendations.
  • : Sales through recommendations have grown to account for 35% of total sales, contributing to improved customer satisfaction and purchase rates.
• Case study: ZOZOTOWN
  • Challenge: To provide more accurate recommendations that reflect the diversity of fashion items.
  • AI: Image recognition AI is used to analyze customers’ preferred styles, colors, brands, etc. Body type data allows for more personalized fashion item recommendations.
  • : Improved purchasing effectiveness, reduced return rate, improved customer engagement

Retail

• Example: Target
  • Challenge: Effectively send coupons for baby products to pregnant female customers.
  • Introduction of AI: Analyzing customer purchase history, a model was developed to predict the likelihood of pregnancy. Coupons for baby products were sent to customers who had a high probability of becoming pregnant.
  • : Significant increase in coupon usage rate and sales increase.
• Case study: Walmart
  • Challenge: To integrate inventory management between physical stores and online stores and efficiently replenish inventory.
  • AI: Utilizing predictive AI, which requires implementation, to analyze sales data from each store. Taking into account seasonal fluctuations, weather, events, etc., the optimal inventory amount is calculated and automatic ordering is performed.
  • Effect: Reduce inventory, prevent stockouts, reduce logistics costs

Finance

• Case study: JPMorgan Chase
  • Challenge: Recognize and prevent fraud in financial transactions.
  • AI implementation: Using machine learning algorithms, fraud patterns are learned from past fraudulent transaction data.
  • Effect: Improved fraud rates, reduced losses
• Example: Mizuho Bank
  • Challenge: Propose financial products that meet customer needs.
  • Introduction of AI: Developed a system that analyzes customer attribute information and transaction history to propose the most suitable financial products.
  • Effect: Improved customer satisfaction, higher success rate, and more efficient sales staff work

Manufacturing

• Case study: Siemens
  • Problem: Predict failures in manufacturing line equipment and perform preventive maintenance.
  • Introduction of AI: Develop an AI model that analyzes equipment operation data collected from IoT sensors and detects signs of malfunction.
  • Effect: Reduced equipment downtime, reduced maintenance costs, improved productivity
• Case study: FANUC
  • Challenge: Optimize robot operation and improve production efficiency.
  • AI: Using reinforcement learning, we have developed a system in which a robot learns optimal movements through challenges.
  • Effect: Improved work efficiency of robots, improved quality, and elimination of labor shortages

others

• Example: Netflix
  • Problem: Recommend content based on user preferences.
  • AI: We have developed a recommendation engine that combines introductory filtering and content-based filtering, etc. We analyze users’ viewing history and ratings to provide personalized content recommendations.
  • : Improved user engagement, increased viewing time, and decreased contract rate
• Example: The Washington Post
  • Challenge: Efficiently create breaking news articles.
  • Introduction of AI: We have developed an AI system called Heliograf to automatically generate standard news articles such as sports results and election updates.
  • Effect: Improved reporting speed, more effective use of journalist resources

These cases show that AI marketing is solving a wide range of problems in various fields and contributing to business growth.

The Future of AI Marketing

AI technology is poised to dramatically change the future of marketing. Here, we look at the future of AI marketing from three perspectives: collaboration between AI and humans, the evolution of AI marketing, and promoting ethical use of AI.

Collaboration between AI and humans

AI does not take away the jobs of marketers, but rather expands their capabilities and provides an environment in which they can focus on more creative work. AI can efficiently perform tasks such as data analysis, prediction, and automation, but there are still areas in which AI cannot replace human creativity, intuition, and empathy.

It is believed that marketing activities will evolve further through collaboration between AI and humans. For example, while the division of labor is generally such that AI performs data analysis and predictions, and humans interpret the results and make the final decision, it is possible to realize more effective strategic marketing by having AI and humans cooperate, discussing the strengths of each other, such as by having humans edit and revise the content generated by AI or brush up on the ideas proposed by AI.

The evolution of AI marketing

AI marketing is expected to continue to evolve, with new technological methods and innovations emerging.

• Improved prediction accuracy: As AI models acquire more training data and their algorithms evolve, they will become better at predicting customer behavior and market trends, which will lead to more personalized marketing and more effective ad delivery.
• Emergence of new AI technologies: AI technologies such as natural language processing, image recognition, and voice recognition are evolving every day. By applying these technologies to marketing, new marketing methods will be developed, such as improving the conversational ability of chatbots, image and video sentiment analysis, and customer support by voice.
• Evolution of sudden marketing: AI can suddenly analyze customer behavior and situations and instantly execute corresponding marketing. For example, when a customer views a specific product on an EC site, an advertisement related to that product can be temporarily displayed, or when a customer inquires at a store, a coupon can be delivered. Temporary marketing improves the customer experience and increases purchases.

Promoting ethical use of AI

As AI marketing evolves, it will become increasingly important to consider ethical issues. We must develop and operate AI with an emphasis on transparency, fairness, and accountability, taking into account the impact that the use of AI has on society.

• Transparency: It is important to make AI algorithms and decision-making criteria public so that users can understand how the AI works.
• Fairness: We need to ensure that our algorithms are fair, so that our AI does not discriminate against certain groups.
• Accountability: AI decisions need to be held accountable by developers and users.

Companies need to formulate guidelines on AI ethics, educate their employees, improve their internal systems, and promote the ethical use of AI. Governments and international organizations also need to develop laws and international rules regarding AI ethics.

Summary: Improve customer experience and grow your business with AI marketing

AI has the potential to dramatically change the way marketing is done and accelerate the growth of companies. Targeting and personalization using AI enables marketing that meets customer needs, contributing to improved customer satisfaction and increased sales.

However, to make AI marketing successful, various challenges must be overcome, including data quality and quantity, privacy protection, AI black box issues, and ethical issues. By utilizing AI correctly, companies will be able to gain a competitive advantage and promote sustainable growth.

AI marketing is still a developing field, but its potential is limitless. With future technological innovations, AI is expected to evolve even further and further enrich our lives and businesses.