NLP-Powered Customer Care Insights
Pension Fund Sector
>86% Accurate Sentiment Analysis + Actionable Insights from Unstructured Customer Interactions
Sentiment Identification Accuracy: >86%
Analysis of Interactions Across >20 Business Processes
Holistic View from Social Media, Emails & Service Databases
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates an NLP-driven solution that transformed massive volumes of unstructured customer interaction data into actionable insights for a leading pension fund company—enabling better understanding of sentiment, key topics, and improvement opportunities to enhance customer experience.
Business Challenge
A leading pension fund company lacked systematic visibility into customer perception and sentiment across interactions. Massive volumes of unstructured data from emails, social media, and customer service databases were difficult to analyze manually. There was no effective way to categorize interactions, quantify key topics/complaints, or derive metrics for data-driven decision-making. This limited the ability to identify improvement areas, respond to customer needs, manage institutional messages, detect fake accounts, and optimize overall service quality.
Implemented Solution
- 1Developed advanced NLP models to perform sentiment analysis, key topic extraction, and entity recognition across multiple data sources (social media, emails, customer service software).
- 2Covered more than 20 business processes to categorize interactions and highlight priority improvement areas.
- 3Applied feature engineering and data quality assurance techniques to ensure model reliability and accuracy.
- 4Integrated diverse unstructured data sources for a comprehensive, 360-degree view of customer interactions.
- 5Trained and deployed the entire solution using cloud services for agile, scalable execution.
Obtained Results
Achieved sentiment identification accuracy of over 86%, providing reliable insights for customer service management.
Identified the most requested topics, frequent complaints, and critical improvement areas to prioritize corrective actions.
Enabled better management of institutional messages and detection of fake accounts on social media.
Established nominal and percentage-based metrics for interactions, supporting strategic decision-making and quantitative tracking.
Transformed unstructured data into actionable intelligence, improving response to customer needs and overall experience.
Evolution in Simov Labs
This NLP customer insights project is now fully integrated into our portfolio at Simov Labs. We evolve these techniques into modern, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for real-time sentiment monitoring, advanced topic modeling, automated complaint routing, predictive customer churn signals, multi-channel integration, and explicit ROI measurement.
Aligned with DAMA and TDWI governance frameworks, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and long-term partnership—ensuring NLP-driven customer care delivers sustainable improvements in experience, retention, brand perception, and operational efficiency. No promises. Just results.
Ready to unlock deep customer insights from unstructured data with accurate, AI-powered analysis?
Book a free 30-minute consultation. We'll discuss your customer care data and where NLP can deliver the most impact.