Social Media Comments Analysis
- A prominent retail bank aimed to enhance customer service and brand reputation by understanding social media sentiments
- The bank analyzed Twitter and Facebook comments to identify improvement areas and address customer concerns proactively
- The objective was to analyze Twitter and Facebook comments to gauge sentiment, identify issues, and generate insights to improve customer satisfaction and service quality
- Collected comments from the bank's Twitter and Facebook accounts using APIs and web scraping over six months
- Employed natural language processing to analyze text data and assign sentiment scores (positive, neutral, negative)
- Used topic modeling techniques like Latent Dirichlet Allocation to group similar comments and identify major topics of discussion
- Highlighted specific issues such as long wait times, service quality, mobile app usability, and customer support experiences
- Generated detailed reports with visualizations and provided actionable recommendations to improve customer satisfaction
- Enhanced social media response times with a dedicated team
- – Addressed issues like long wait times, improving positive sentiment by 15%
- – Resolved mobile app pain points, increasing satisfaction by 20%
- – Proactive handling improved brand perception by 10%
- Adopted data-driven customer service and marketing with regular social media monitoring for continuous improvement
