Sector
Entertainment
Tools used
R
Python
Skills
Clustering
Sentiment Analysis
Natural Language Processing
Feature Importance Analysis
Predictive Modelling
Key Questions Answered:
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Customer engagement patterns and loyalty point accumulation
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Customer segmentation for targeted marketing
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Sentiment Analysis from customer review text data
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Factors influencing customer loyalty
Key Insights
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Older customers tend to have lower spending scores and accumulate fewer loyalty points compared to younger customers.
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Higher-income customers generally accumulate more loyalty points, suggesting the importance of tailored rewards and promotions for this segment.
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Customer reviews indicate a positive overall sentiment, but also provide feedback on popular and underperforming products, informing targeted marketing strategies and product enhancements
Recommendations
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Enhance Loyalty Programs: Develop exclusive rewards and premium memberships for high-spending customers, while offering value-driven promotions to engage lower-income, high-spending customers.
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Personalised Marketing Campaigns: Create age-based and income-based marketing campaigns to effectively reach and retain different customer segments.
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Leverage Text Data for Insights: Utilise sentiment analysis and customer feedback to identify areas for product improvements and inform marketing initiatives.
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Continuous Model Refinement: Explore feature engineering, non-linear models, and outlier handling to further improve the predictive accuracy of the loyalty points model.