Using Data Science Techniques to Promote Cross-Selling Opportunities and Understand Client Needs

Overview

Explore market basket analysis and sentiment analysis for identifying cross-selling opportunities and understanding client needs in this data science presentation.

Syllabus

• Introduction to Data Science in Marketing
Overview of Data Science Techniques
Importance of Data-Driven Decisions in Marketing
• Understanding Cross-Selling Opportunities
Definition and Benefits of Cross-Selling
Case Studies: Successful Cross-Selling Strategies
• Market Basket Analysis
Fundamentals of Market Basket Analysis
Implementing Association Rule Mining
Identifying Product Bundles for Cross-Selling
Hands-on Workshop: Using Python for Market Basket Analysis
• Sentiment Analysis for Client Understanding
Introduction to Sentiment Analysis
Tools and Techniques for Analyzing Client Sentiment
Applications in Customer Experience Enhancement
Practical Exercise: Conducting Sentiment Analysis on Real-World Data
• Integrating Findings into Marketing Strategies
Combining Market Basket and Sentiment Analysis Insights
Designing Targeted Marketing Campaigns
Personalization Tactics for Improving Client Engagement
• Ethical Considerations in Data Science
Data Privacy and Security
Ethical Implications of Using Client Data
• Course Project
Real-World Project: Identifying Cross-Selling Opportunities for a Retailer
Presentation and Feedback
• Conclusion and Next Steps
Recap of Key Learnings
Further Resources and Readings
Opportunities for Advanced Study in Data Science and Marketing

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Subjects

Data Science