Global tech company scales customer intelligence across the support journey

The Challenge

A leading global technology company had no consistent way to understand why customers were contacting the business across the full customer lifecycle. Conversations spanning product discovery, purchasing, delivery, support, and repairs were often analyzed within individual touchpoints, making it difficult to connect recurring issues to the products and experiences driving them. The company needed a more reliable way to classify customer contact reasons, connect interactions to the relevant products, identify dissatisfaction signals, and uncover patterns that could support business decisions.

The Solution

To create a more complete understanding of customer interactions across the support journey, the company implemented Sprinklr's AI-powered Conversational Analytics solution across Live Chat, SMS, WhatsApp, email, social, and app-based support channels. Rather than relying on a single AI model, the company worked with Sprinklr to build a foundation that could accurately classify conversations, connect them to products and customer experience signals, and generate actionable insights at scale. 

Building a trusted foundation for customer intelligence 

Rather than analyzing customer interactions at individual touchpoints, the company worked with Sprinklr to create a consistent understanding of why customers were contacting the business across the customer lifecycle. By aligning customer conversations to common contact reasons and business context, teams gained a more trusted foundation for understanding customer needs and experiences. 

To make that possible, the company designed its contact-driver taxonomy to reflect its own business language, customer journeys, and operational needs. Working closely with stakeholders, the team refined classifications, optimized prompts, and developed separate models for pre-purchase and post-purchase interactions. These efforts improved the accuracy of downstream analysis and established a foundation the business could trust. 

Turning conversation data into actionable business insight 

With a reliable understanding of customer intent, the company could move beyond basic contact classification and begin connecting conversations to products, customer experience signals, and emerging trends. Contact-driver analysis was combined with product mapping, dissatisfaction detection, conversation summaries, topic analysis, and Insights Hub, creating a more complete view of what customers were experiencing and why. 

Customer conversations were no longer analyzed in isolation. Interactions could be linked to the relevant product category, while dissatisfaction signals such as repeat contacts, escalation requests, long wait times, and potential churn risk provided additional context for understanding customer experiences. 

With this foundation in place, teams could identify patterns and emerging issues across large volumes of customer conversations. Insights Hub helped surface potential root causes behind customer friction, enabling the company to investigate issues such as refund-process complexity, payment-related challenges, and knowledge gaps before they developed into larger operational problems. Product, operations, and support teams gained a more proactive way to understand customer needs and prioritize action.

The Outcome

The company transformed conversation analytics from a limited reporting exercise into a production-scale source of customer intelligence. By improving the accuracy of contact-driver classification and applying it across the customer lifecycle, teams gained a more reliable understanding of why customers were contacting the business and what experiences were driving those interactions.  

The resulting intelligence helps product, operations, and support teams identify patterns, prioritize investigation, and better understand emerging customer issues before they became larger operational challenges. 

Key results

• Achieved highest-level contact-driver accuracy of 85%.  
• Achieved 80%+ accuracy at deeper classification levels across the customer journey. 
• Deployed the solution across approximately 130,000 customer cases. 
• Created a shared intelligence foundation connecting contact drivers, products, customer experience signals, and emerging issues across the customer lifecycle.  
• Enabled teams to proactively identify issues such as refund-process complexity, payment-related challenges, and knowledge gaps before they became broader escalations. 

Rather than relying solely on retrospective reporting, the company gained a more proactive way to understand customer needs, investigate emerging issues, and turn customer conversations into business insight.