Case Study Advanced text mining by extracting insights from various forums helps in addressing and analyzing customer feedback
Client: Leading technology company Industry: Information Technology Business Impact: • Consumer insights • Feedback classification
Business Challenge The client used to capture volumes of conversations across different customer feedback forums as well as agent notes related to customer calls that were stored in databases. They had a large team that extracted samples of these conversations and coded these conversations into one or more pre-defined code frames and categorized them accordingly. The engagement scope outlined building a “text mining and codification engine” through the application of Natural Language Processing to automatically categorize conversations based on different buckets. This categorization, in turn, helped client analyze the text data objectively and quantitatively. The focus was to improve the accuracy of the categorization throughout the engagement lifecycle. Additionally, the task was to identify key metrics and formulate visualization capabilities for insights from unstructured (primarily textual) data.
Solution The overall solution had four major milestones as defined below: Step 1 Step 2 Step 3 Step 4
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Training
Classification
Quality Assurance
Reporting
• • •
Initial focus of the work was to de-complicate the complex hierarchical structure to ensure appropriate classification as per business requirements Design a comprehensive Natural Language Processing based rule to ensure most categories are captured Scaling classification using Machine Learning Algorithms (Support Vector Machines and Neural Networks)
Once the analysis was completed, it was followed by an intuitive reporting system, which had drill-down/out capability filters to quickly acquire and focus on what was needed.
Outcome The text mining framework was able to classify texts automatically. There were five levels of classification ensuring that the result was accurate. This enabled the client to take quick, informed decisions based on customer feedback. • •
Cognized business significance for future versions and enhanced user experience Improved efficiency of the strategic and quality management team by 20%
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