Client Confidentiality Note: Litza Tech protects client identities. This case study details our work with a SaaS company while ensuring anonymity.
A rapidly expanding Software-as-a-Service (SaaS) company found its customer support team increasingly strained. As their user base grew, so did the volume of incoming support tickets, feature requests, bug reports, and general feedback flooding in through email, in-app chat, community forums, and review sites. The support team struggled to keep up, leading to longer response times. More critically, valuable insights buried within this text data – recurring user frustrations, emerging bugs, popular feature ideas – were being missed or identified too slowly, hindering proactive support and informed product development.
Litza Tech deployed a tailored solution leveraging principles from our AI/ML and Data Analytics offerings, closely resembling our Orion Insight Engine. The project involved:
- Data Integration: Setting up secure integrations to pull text data from the client’s primary feedback channels: their ticketing system (e.g., Zendesk), community forum database, and key app store review feeds.
- AI Text Analysis Pipeline: Implementing an AI pipeline utilizing advanced Natural Language Processing (NLP) models to:
- Auto-Categorize Issues: Automatically classify incoming tickets and feedback into predefined categories (e.g., Bug Report, Feature Request, How-To Question, Billing Issue) with high accuracy, enabling faster routing to the right support personnel or team.
- Topic Modeling & Trend Detection: Employ unsupervised learning techniques to identify emerging themes and clusters of related issues across all feedback sources, even those not previously categorized. This helped spot new bugs or widespread confusion about a recent feature release quickly.
- Aspect-Based Sentiment Analysis (ABSA): Analyze the sentiment expressed towards specific aspects of the software or service mentioned in the feedback (e.g., positive sentiment towards ‘new reporting feature’, negative sentiment towards ‘login process complexity’).
- Insight Dashboard & Alerting: Creating a dashboard for the support leads and product managers visualizing key trends: top reported issues, sentiment shifts related to specific features, common points of user confusion, and emerging topics. Configured alerts for significant spikes in negative sentiment or reports of critical bugs.
- Knowledge Base Integration: Identified frequently asked questions and common troubleshooting steps from the analyzed data to proactively suggest updates and additions to the support team’s internal knowledge base and the public-facing FAQ.
The implementation yielded significant operational benefits. The AI-powered auto-categorization and routing reduced the average time for initial ticket triage by 40%. Support agents could focus more on resolving complex issues rather than manual sorting. The product team gained near real-time visibility into user pain points and feature requests, allowing them to prioritize the development backlog more effectively based on quantitative feedback data. Trend detection enabled the team to identify and address two significant bugs post-release much faster than they would have previously. Overall, the client saw a marked improvement in key support metrics (like First Response Time and Resolution Time) and a positive uptick in user satisfaction scores related to support interactions and product responsiveness. Litza Tech helped turn their overwhelming volume of customer feedback into a strategic asset.
