AI Digital Channel Personalization via Autonomous User Clustering
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Solution Overview
Problem
Current digital marketing processes for creating personalized digital channels are manual, costly, and slow, leading to limited personalization and high costs due to the need for large digital marketing teams.
Innovation Solution
A system utilizing artificial intelligence (AI) to automate personalized digital experiences by collecting visitor data, clustering users based on behavior, generating interaction models, and recommending content to maximize business outcomes and learning speed, thereby reducing the need for manual processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used to create personalized digital channels, then personalization can be achieved, but the cost increases and the process becomes slow
Solution Approach 1:
The system enables automatic self-personalization through AI-driven user behavior analysis and content recommendation engines that autonomously tailor digital channel experiences without manual intervention, resolving the contradiction by making the system self-sufficient in personalization tasks
Solution Approach 2:
Manual mechanical processes are replaced with automated AI/ML systems that analyze user data, generate personalization rules, and deliver customized content automatically, thereby maintaining personalization capability while dramatically improving process speed and reducing costs
2Adaptability or versatility
If large digital marketing teams are assembled to manage personalized content, then personalization quality improves, but costs increase significantly
Solution Approach 1:
The system replaces human marketing teams with autonomous AI agents that perform content creation, personalization, and optimization tasks automatically, eliminating the need for large teams while maintaining or improving personalization quality and significantly reducing costs
Solution Approach 2:
Human manual work in digital marketing is substituted with automated machine learning algorithms and AI systems that can process and personalize content at scale without additional per-unit costs, resolving the contradiction between personalization quality and cost
3Adaptability or versatility
If manual personalization processes are used, then some level of personalization is achieved, but innovation speed decreases
Solution Approach 1:
Manual iterative personalization processes are replaced with automated AI/ML systems that continuously learn from user interactions and rapidly generate innovative personalization strategies, eliminating time losses associated with manual processes while maintaining personalization effectiveness
Data Source
AI summary
A method, system, and apparatus provide the ability to personalize a digital channel. A digital channel is provided to multiple users and visitor information at each visit is collected. The visitor information includes data about each visit and multiple content items that are presented. The users are autonomously clustered by segmenting the user population into behavioral groups such that mutual information is maximized between the users in an assigned behavioral group and the content items. Based on the clustering, a model is generated that estimates a score for each interaction between users and content items. The model is updated at a defined interval. Based on the score, content items to recommend to a specific user are determined. The recommendation jointly maximizes an outcome and a learning speed of the model. The personalized digital channel is delivered to the specific user based on the recommended multiple content items.