Hybrid Conferencee

International Conference on Predictive Analytics in Consumer Behavior (ICPACB - 26)

15th - 16th October 2026 | Geneva, Switzerland
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Call for Papers Extended:
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Certificate of Presentation:
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Abstract Submissions Open:
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Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Geneva conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
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Best Paper & Best Paper Presentation Award:
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SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 8
SDG 8 Decent Work and Economic Growth
SDG 9
SDG 9 Industry, Innovation and Infrastructure
SDG 12
SDG 12 Responsible Consumption and Production
Track 01

Predictive Analytics in E-commerce

This track focuses on the application of predictive analytics in the e-commerce sector, exploring how data-driven insights can enhance online shopping experiences. Participants will discuss innovative methodologies for predicting consumer behavior and optimizing sales strategies.

Track 02

Customer Segmentation Techniques

This session delves into advanced customer segmentation methodologies, emphasizing the importance of tailored marketing strategies. Researchers will present their findings on how effective segmentation can lead to improved customer engagement and retention.

Track 03

Behavioral Modeling in Marketing

This track examines the role of behavioral modeling in understanding consumer decision-making processes. Presenters will share insights on how these models can inform marketing strategies and enhance customer targeting.

Track 04

Churn Prediction and Management

Focusing on the critical issue of customer churn, this session will explore predictive models that identify at-risk customers. Discussions will center on strategies for retention and the implications of churn prediction for business sustainability.

Track 05

Market Forecasting Techniques

This track highlights innovative approaches to market forecasting, utilizing predictive analytics to anticipate consumer trends. Participants will discuss the implications of accurate forecasting for strategic business planning.

Track 06

Data Mining for Consumer Insights

This session will explore the application of data mining techniques to extract actionable insights from consumer data. Researchers will present case studies demonstrating the impact of data mining on understanding consumer preferences.

Track 07

Machine Learning in Consumer Behavior Analysis

This track focuses on the integration of machine learning algorithms in analyzing consumer behavior patterns. Presentations will cover advancements in machine learning that enhance predictive accuracy and business decision-making.

Track 08

Customer Profiling and Personalization

This session examines the significance of customer profiling in creating personalized marketing experiences. Participants will discuss methodologies for effective profiling and the resulting impact on customer satisfaction.

Track 09

Predictive Modelling for Buyer Journey Analysis

This track investigates the use of predictive modeling techniques to analyze the buyer journey. Presenters will share insights on how understanding the buyer journey can inform marketing strategies and improve conversion rates.

Track 10

Data-Driven Marketing Strategies

Focusing on the intersection of data analytics and marketing, this session will explore how data-driven strategies can enhance marketing effectiveness. Discussions will include case studies that illustrate successful implementation of data analytics in marketing campaigns.

Track 11

Customer Lifetime Value Prediction

This track addresses the methodologies for predicting customer lifetime value (CLV) and its implications for business strategy. Participants will explore how accurate CLV predictions can inform resource allocation and customer relationship management.