This conference delves into leveraging advanced machine learning techniques to profoundly analyze and quantify customer journeys and churn behaviors. It emphasizes behavioral data science’s role in uncovering nuanced customer insights from multichannel data, using predictive and prescriptive models to enhance engagement and retention. Topics include customer segmentation, propensity modeling, anomaly detection, and AI-driven lifecycle management, with a focus on driving business growth through data-driven customer strategies.
Structure and Sessions (Who Does What):
Organizing Committee: Plans the complete agenda, invites expert speakers, and manages virtual event logistics.
Keynote Speakers: Behavioral data science leaders, AI specialists, and industry influencers provide thought leadership and innovation perspectives.
Session Chairs: Facilitate sessions, moderate Q&A, and foster interactive discussions.
Presenters: Data scientists, academic researchers, and practitioners present practical case studies, algorithms, and frameworks.
Participants: Actively engage in panels, workshops, and networking.
Technical Review Committee: Reviews abstracts and papers to ensure quality and relevance.
Participate and Contribute (Who Benefits):
Data Scientists and Behavioral Analysts: Gain insights on machine learning applications in customer behavior analytics.
Marketing Strategists and Customer Experience Managers: Learn data-driven approaches to reduce churn and optimize journeys.
Academics and Researchers: Present research and publish in prestigious journals.
Business Leaders and Consultants: Understand how to integrate behavioral analytics into growth strategies.
Students and Professionals: Expand skills in advanced customer analytics.
Benefits:
Certificate of Participation after event feedback submission.
Commemorative momento recognizing active participation.
Abstracts and selected papers published in Scopus, CrossRef, Web of Science (WoS), Elsevier, and SCI-indexed journals.
Abstract Topics / Call for Papers (CFP) Tracks:
Machine Learning Models for Customer Segmentation and Churn Prediction
Behavioral Analytics Using Multichannel Customer Data
Anomaly Detection and Root Cause Analysis in Customer Behavior
Propensity Modeling and Customer Lifetime Value Prediction
AI-Driven Customer Lifecycle and Engagement Management
Ethical Considerations in Behavioral Data Science
Real-Time Analytics and Customer Journey Orchestration
Integrating Qualitative and Quantitative Customer Data
Case Studies on Behavioral Data Science Impact
Emerging Techniques in Customer Analytics
Registration:
Online registration open to data scientists, marketing professionals, researchers, and students.
Clear instructions and deadlines for abstract submissions provided.
Access to live sessions, recordings, materials, certification, and publication opportunities.
Detailed Program Grid (2 Half Days, 9 AM to 1 PM)
Time
Day 1
Day 2
09:00 – 09:15
Opening Ceremony & Welcome Address
Recap of Day 1 & Opening Keynote
09:15 – 10:00
Keynote Session I: Foundations of Behavioral Data Science
Keynote Session III: Advanced ML for Churn Prediction
10:00 – 10:45
Technical Session I: Customer Journey Analytics
Technical Session III: Anomaly and Propensity Modeling
Panel Discussion: Ethical Challenges and Business Impact
11:45 – 12:30
Abstract Presentations and Research Highlights
Technical Session IV: Future Directions in Behavioral Analytics
12:30 – 13:00
Participant Feedback and Closing Remarks
Closing Ceremony & Certificate Distribution
This conference provides leading-edge knowledge and practical skills to drive business value through behavioral data science and machine learning innovations in customer journey and churn analysis, complemented by certification and academic publishing pathways.
