Customer Sentiment Analytics for a Leading Healthcare Company
About the Customer
The customer, a leading healthcare services provider, wanted to accurately analyze and interpret sentiments from large volumes of textual review data to understand customer opinions and brand perception and improve overall customer engagement.
The Challenges
- Inconsistent Information Delivery: Understanding customer sentiments from the vast volume of unstructured review data.
- Domain Agnostics Insights: Huge risk of reputation damage for delayed responses to unhappy customers.
- Data Sources Volume: Scaling process across multiple channels (social media, reviews, surveys) through automation.
- Slow Response Time: Needed to provide immediate answers, improving the user experience.
- Scalability Issues: Not able to handle volumes of queries simultaneously.
Our Solution
The AInnovTM Sentiment Analytics Solution was proposed to provide actionable insights from customer reviews, improve decision-making, and enhance customer engagement.
- Data Collection & Preprocessing: Process huge volumes of review data from multiple sources and Preprocess using Tokenization, Lemmatization/Stemming, Noise removal, etc.
- Model Selection & Training: Use Pretrained LLM models and fine-tune on domain-specific sentiment datasets.
- Sentiment Classification: Using advanced NLP techniques to classify the review and define it under predefined categories automatically.
- Sentiment Categorization: Link the review to a specific component of the service.
- Automated Business response: Generate an automated response contextual to the User Review for enhancing personalized Customer engagement and experience
The Outcomes
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Increase in accuracy of the responses received as per the query
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Reduction in human effort for information extraction
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