Gain actionable insights into customer sentiment across your ticket portfolio
Neo's Customer Experience Analysis feature uses advanced sentiment analysis to evaluate client interactions across tickets, emails, and other communications. By processing natural language data from multiple sources, Neo can generate comprehensive reports highlighting trends in customer satisfaction, identifying at-risk accounts, and recognizing opportunities for service improvement. This proactive approach to monitoring client sentiment helps MSPs address issues before they escalate, strengthen client relationships, and make data-driven decisions to enhance service delivery.
Configure which aspects of customer experience to analyze by selecting from multiple criteria types including Sentiment, Communication Efficiency, Message Count, Time Spent, and Errors/Neglections. This flexibility allows you to focus on specific dimensions of your service delivery that align with your quality improvement goals.
Decide between company-level reporting (organizing insights by client company with overall sentiment trends) or ticket-level reporting (providing detailed analysis of individual tickets). Company-level reporting helps identify patterns across organizations, while ticket-level reporting enables targeted improvement of specific interactions.
Determine which sentiment categories should be included in your analysis report. By default, the system focuses on highlighting "Awesome" and "Negative" experiences to help you celebrate successes and address concerns, but you can customize this to include all sentiment types for a complete picture.
Establish a regular cadence for customer experience analysis through workflow triggers. Configure the analysis to run automatically after specific events or on a scheduled basis, ensuring you have continuous visibility into the customer experience your team delivers.
For optimal results, analyze at least 20-30 tickets per company; smaller samples may not reveal meaningful patterns or trends.
Consider analyzing tickets from a specific timeframe (past month, quarter) to identify current trends rather than historical issues that may have been resolved.
The AI distinguishes between internal team communication and customer interactions, ensuring fair assessment of service quality regardless of communication patterns.
Before implementing, consider how findings will be shared with your team to ensure the focus remains on improvement rather than criticism.
