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Have you heard the term "omnivore"? As the end of the year approaches, one of the top keywords introduced in Trend Korea 2025, a book that marketers look to annually, is "omnivore." Traditionally, "omnivore" refers to animals that eat a variety of foods, but in the book, it highlights the idea of consumers who explore diversity, express individuality, and transcend conventional boundaries of age, gender, and income in their preferences.
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In such an environment, the author urges marketers to go beyond simplified audience targeting and build micro-segments. By leveraging AI and machine learning to analyze consumer behavior data, marketers can implement hyper-personalized marketing strategies. Hyper-personalized marketing involves analyzing individual customer behaviors and preferences to provide tailored experiences. With AI and real-time data analysis, companies can quickly analyze consumer data, understand intentions, and respond accordingly. According to Statista, 78% of companies that adopted hyper-personalization strategies experienced revenue growth, showing the importance of effective data analysis.
Here are some examples of companies that have successfully applied hyper-personalization techniques and grown sales.
Kakao Styleโs fashion platform "Zigzag" has been a leading platform using personalization technologies since 2020. With AI-powered, segmented data within the app, it tailors product recommendations, promotions, and banners to each customer. In August last year, they introduced the AI-driven image search service "Zigzag Lens." Customers can upload fashion photos they find on social media or take their own, and the system extracts detailed information, such as clothing category, color, sleeve length, neckline, and fit, to recommend similar styles. With this enhanced search system, they achieved a 140% increase in user numbers year-over-year.
Shinhan Bank's official app "SOL" offers personalized information through its MyData service "Moneyverse," allowing customers to understand and manage their financial patterns via AI analysis. For example, it provides tailored financial products or popular choices among peers, along with other features like asset and consumption reports and a calendar for managing key dates. Using big data effectively, Shinhan Bank has become a top consumer asset management platform in Korea for three consecutive years.
To implement hyper-personalized marketing, it is crucial to segment customer data and utilize AI and machine learning to execute tailored marketing for each segment. According to Forrester, companies that actively leverage data see up to a 300% increase in revenue.
For instance, fashion brands like "Zigzag" analyze customer purchase histories to create style-specific groups and recommend products tailored to each group. To begin data-driven customer segmentation, understanding the customer journey is key. Customers leave various traces throughout the process of purchasing and using products. CRM (Customer Relationship Management) systems exist to systematically collect and analyze these traces for marketing purposes. By enhancing CRM systems to store precise purchase data, businesses can understand customer behavior patterns and implement tailored strategies accordingly.
Recently, advanced CRM solutions with AI capabilities have emerged, enabling real-time data analysis and providing automated marketing strategy recommendations for team members.
Real-time recommendation systems analyze customers' browsing data to provide products and content that align with their preferences and past behaviors at the right moment. These systems are widely used in various industries, from OTT platforms like Netflix and Tving to e-commerce.
For example, Amazon generates 35% of its revenue through its real-time recommendation system, which analyzes customer search and purchase data. Real-time recommendations not only drive immediate purchases but also help customers quickly find relevant products and content amid overwhelming options, improving customer satisfaction.
However, effective recommendations require sufficient data. Building an in-house recommendation system using machine learning can demand significant resources. If operating such a system internally is challenging, AWS Personalize can be utilized to simplify application setup and automate complex steps such as ML model training and deployment.
Predictive modeling analyzes historical customer data to forecast future behaviors, enabling timely and personalized promotions. Sports brands, for example, analyze customer purchase patterns to send discount coupons at the right time, boosting conversion rates.
Nike leverages customer purchase histories and preferences to recommend tailored products and promotions, while Adidas provides personalized discounts via digital platforms to drive sales growth.
Predictive modeling can also enhance recommendation system performance. Common methods include collaborative filtering, content-based filtering, and deep learning models. Collaborative filtering calculates user-item similarity based on past behaviors, while content-based filtering analyzes item attributes to suggest similar products. Deep learning models capture complex interactions between users and items, offering sophisticated recommendations.
AI chatbots boost customer engagement by interacting in real-time, answering questions, and recommending personalized products. Starbucks, for instance, uses AI chatbots to analyze customers' ordering habits, suggesting frequently purchased drinks or introducing new menu items. According to Salesforce, AI chatbots enhance customer satisfaction by over 60%.
These chatbots are continuously evolving, integrating personalized contexts and emotions to provide highly customized experiences.
Retargeting ads effectively re-engage customers who visited a website but left without making a purchase. AI analyzes behavioral data to deliver retargeting ads at optimal times. For example, if a customer adds an expensive item to their cart but doesn't complete the purchase, AI remembers this and displays ads with special discount codes.
According to Criteo, retargeting ads achieve click-through rates 10 times higher than general display ads.
Personalized recommendations maximize customer conversion potential. Invesp reports that personalized marketing can boost conversion rates by up to 10x.
Personalized services lead to greater satisfaction and increased brand loyalty. McKinsey reports that personalized experiences improve customer loyalty by 70%.
Continuous hyper-personalization helps sustain long-term relationships with customers. Reflecting customer preferences in personalized experiences reinforces positive brand impressions.
Hyper-personalized marketing revolves around deeply understanding customer behaviors and preferences to provide tailored experiences. By actively utilizing AI and data analytics, businesses can predict customer behaviors and respond appropriately at the right time, maximizing revenue potential. Hyper-personalized marketing will continue to become more sophisticated, making it a critical factor in maintaining competitive advantage.
Drive business growth by exceeding customer expectations through tailored experiences. Strengthen your hyper-personalized marketing strategy with AI-powered customer segmentation, real-time recommendation systems, predictive modeling, AI chatbots, and retargeting ads.
