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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