AI In Market and Data Research

Overview:

Introduction:

This 5-day training course provides a comprehensive introduction to applying Artificial Intelligence (AI) in market and data research. Participants will learn how AI tools and techniques can revolutionize data collection, analysis, and interpretation, leading to more accurate and actionable insights.

Conference Objectives:

By the end of this conference, participants will be able to:

• Understand the fundamental concepts of AI and its relevance to market and data research.

• Learn to utilize AI-powered tools for data collection, cleaning, and preparation.

• Develop skills in applying machine learning algorithms for data analysis and pattern recognition.

• Develop knowledge and confidence to apply a data-driven approach to strategic decision-making 

• Learn to visualize and interpret AI-driven insights for effective decision-making.

• Strengthen your decision. making  skills by integrating data science techniques into key business strategies 

Target Audience:

• Market researchers

• Data analysts

• Business analysts

• Marketing managers

• Product managers

• Anyone interested in leveraging AI for market and data research

   

Conference Outline:

Unit 1:

Introduction to AI for Market Research

• Basic concepts of AI, machine learning, and deep learning.

• Overview of AI's role in data collection, analysis, and reporting.

• Understanding structured and unstructured data and various data sources.

• Addressing bias, privacy, and responsible AI practices.

• Introduction to popular AI platforms and libraries.

Unit 2:

AI-Powered Data Collection and Preparation

• Using AI tools for automated web data extraction.

• Leveraging AI for gathering and analyzing social media insights.

• Techniques for handling missing data, outliers, and inconsistencies.

• Creating relevant features from raw data for machine learning models.

• Combining data from multiple sources for comprehensive analysis.

Unit 3:

Machine Learning for Market Analysis

• Regression and classification algorithms for predictive modeling.

• Clustering and dimensionality reduction for pattern discovery.

• Identifying distinct customer groups using machine learning.

• Forecasting market demand and identifying emerging trends.

• Assessing the performance of machine learning models.

Unit 4:

Natural Language Processing (NLP) for Market Insights

• Text processing, tokenization, and sentiment analysis.

• Analyzing customer reviews and social media mentions.

• Identifying key themes and patterns in textual data.

• Utilizing chatbots for customer surveys and feedback collection.

• Extracting insights from customer interactions and voice recordings.

   

Unit 5:

AI-Driven Insights and Reporting

• Creating interactive dashboards and visualizations for data storytelling.

• Automating the creation of market research reports.

• Translating complex AI outputs into actionable recommendations.

• Analyzing real-world examples of AI applications.

• Exploring emerging technologies and applications.