C5i > Case Studies > Gained customer experience insights from conversations using advanced text analytics and machine learning techniques

Gained customer experience insights from conversations using advanced text analytics and machine learning techniques

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Company Size
1,000+
Product
  • Text Analytics
  • Machine Learning
Tech Stack
  • Natural Language Processing
  • Ridge Classifier
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Customer Satisfaction
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Natural Language Processing (NLP)
Services
  • Data Science Services
About The Customer
The customer is a leading technology company in the Information Technology industry. They were capturing consumer conversations from multiple channels and categorizing the customer experience manually on a minimized sample basis. This method did not provide a holistic view of customer experience insights. The company wanted to automate the text classification on the entire data set to gather more detailed insights about the customer experience.
The Challenge
The client, a leading technology company, was capturing consumer conversations from multiple channels such as tech forums, call center agent transcripts, email, and chat. They were categorizing the customer experience (text data) manually on a minimized sample basis which did not provide a holistic view of customer experience insights. The client wanted to automate the text classification on the entire so they could gather more detailed insights about the customer experience.
The Solution
Blueocean Market Intelligence simplified the complex hierarchical structure to ensure the appropriate classification as per business requirements. They used Natural Language Processing techniques such as verb replacement, lemmatization, normalization, tokenization, and stemming to ensure that the categories are captured. They converted multi-class records into single class by applying a basic as well as weightage technique. Key features were extracted by determining the importance of the terms by determining the weight of combinations. A Ridge Classifier was used to classify comments based on defined business rules.
Operational Impact
  • Achieved an accuracy close to 70%
  • Provided a roadmap to enable the future classification of issues on general blogs and forums
Quantitative Benefit
  • 20% efficiency improvement
  • 400x reduced costs per coded case
  • 2% to 100% inclusion
  • 4x data sources
  • 40% to 70% accuracy improvement

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