A One-Stop Solution for AI-Driven Experience Measurement

Today, enterprise experience measurement and management face three core pain points: subjective indicator selection, high measurement costs with long cycles, and shallow data analysis. Traditional models relying on empirical judgment, single indicators, manual surveys, and superficial data analysis struggle to adapt to diverse scenarios and uncover genuine user experience pain points. AI-driven experience measurement and management addresses these issues through intelligent indicator recommendation, end-to-end automated measurement, and multi-dimensional data analysis, forming a full-link closed-loop to optimize insight-to-decision processes, effectively reducing costs and enhancing efficiency for breakthroughs in experience management.

This workshop aims to help participants master how to leverage AI technologies for more scientific, intelligent, and efficient product experience measurement and management, and apply these skills to practical work to ultimately enhance product experience.


1.Opening Introduction
·Speaker self-introduction and professional background
·Significance of IXDC Conference and the workshop, emphasizing AI’s transformative role in experience management
·Clarification of learning objectives and workshop schedule
2.In-depth Analysis of Experience Measurement Pain Points
·Analyzing the drawbacks of traditional indicator selection relying on experience or single metrics
·Dissecting high costs and long cycles caused by manual operations
·Illustrating limitations of manual data analysis in uncovering experience bottlenecks
3.Detailed Explanation of AI-Driven Solutions
·Product attribute-based automated indicator recommendation technologies and cases
·End-to-end automated measurement processes integrating AI technologies with effect demonstration
·Implementation paths for multi-dimensional data integration analysis and intelligent decision-making closed-loop
4.Group Hands-on Practice
·Deriving recommended indicators based on sample products
·Conducting automated measurement for survey-based and walkthrough evaluation processes
·Integrating multi-source data, configuring as needed, and completing final analysis reports
5.Group Presentation and Feedback
·Groups showcasing AI measurement plans and elaborating on innovation and feasibility
·Speaker providing feedback for optimization and guiding in-depth knowledge application
6.Summary, Review, and Outlook
·Sorting out core knowledge and strengthening systematic understanding
·Participants sharing learning gains and application plans
·Outlook on development trends of AI in experience measurement

1、Icebreaker & Speaker Introduction
2、In-Depth Analysis of Experience Measurement Pain Points
3、Explanation on AI-Driven Experience Measurement and Management Solution
4、Q&A
5、Group Practical Drill
6、Case Practice 1: Intelligent Indicator Recommendation. AI dynamically matches and generates a scientific indicator system based on products, users, and business objectives.
7、Case Practice 2: Automated Processes. Survey-related processes automatically generate and distribute questionnaires, while inspection-related processes achieve automated evaluation.
8、Case Practice 3: In-depth Data Analysis. Integrate multi-source data and output professional structured experience insight reports and optimization strategies.
9、Results Presentation and Feedback
10、Summary, Review and Outlook

1、UX/UI Designers
2、Product Managers
3、Data Analysts
4、Marketing Operations
5、Anyone interested in the integration of AI and user experience

1、Understand the concepts of experience measurement and management
2、Master the AI-driven experience measurement methodology: Learn end-to-end AI solutions, including product-based metric recommendation, AI-automated measurement, data fusion, and strategy generation.
3、Apply AI-driven experience measurement tools in practice: Through industry case exercises, master using AI to design measurement systems, conduct automated analysis, and generate actionable insights.