Customer feedback provides invaluable insights that can help businesses enhance their products and services. In today's Text Analytics class project, we analyzed consumer reviews for a popular fruit delivery service, applying advanced text analytics to uncover prominent themes and sentiments, highlighting areas for enhancing customer satisfaction and loyalty.
Dataset Overview
The dataset analyzed consists of hundreds of customer reviews, each rated on a scale from 1 to 5. To gain a clearer view of overall sentiment, ratings of 4 and 5 were considered positive, while ratings of 1 and 2 were classified as negative..
While the majority of feedback was positive, recurring issues in the lower ratings provided critical insights into areas requiring improvement, particularly around fruit quality and delivery timing.
Word Cloud Insights
Word clouds helped visualize frequently mentioned terms across both positive and negative reviews. Positive feedback prominently featured words such as “fresh,” “ripe,” and “delicious,” indicating customer satisfaction with the product's quality. Negative reviews, however, frequently included terms like “bruised,” “late,” and “overripe,” suggesting dissatisfaction with delivery quality and timing.
Digging Deeper with N-Gram Analysis
To dive deeper, we used n-gram analysis to capture common two- and three-word phrases that highlighted specific aspects of customer experience:
This analysis revealed that while customers valued the taste and quality of fresh fruit, concerns arose around packaging and timeliness, particularly for sensitive items like bananas and apples.
Key Takeaways and Actionable Insights
If you're interested in applying these insights to your business and uncovering valuable patterns in customer feedback, feel free to reach out. We would love to discuss how our data-driven approach can guide your strategy and help improve your product offerings.
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