What is AI-Based Sentiment Analysis?
What Are Utterances?
- Raise the priority of incident IR-0000028 to the next higher level.
- Can you please raise the priority on my open ticket?
- Have any company-wide issues been reported today?
- Is there an outage?
- How do I setup company email on my cell phone?
- I need help setting up email on my mobile device.
What Are Intents?
What Are Entities and How Do You Extract Them?
How Do You Extract the Right Data from Customer Support Tickets?
Why Do You Need AI-Based Sentiment Analysis?
Customer interactions—whether they indicate positive, neutral, or negative sentiment—can be used to circumvent issues, inform internal teams of problems, and influence new and existing customer behavior. AI-based Sentiment Analysis reveals how individual customers feel about your products, services, and policies. This information can help you identify areas that may need to be improved by:
- Capturing IT effort that is overlooked or misinterpreted by Key Performance Indicators. KPIs such as call duration are not necessarily the best way to measure the effectiveness your IT support staff. For example, a long phone call may mean that your agent is handling a complex issue—not having trouble resolving it. You can use Sentiment Analysis to identify the agents that are consistently involved in calls with a positive sentiment, so you can reward them and use them to mentor less experienced team members.
- Providing more data for root cause analysis. By pulling sentiment data into your IT department’s KPI reports, you can find correlations that might otherwise be hidden. You can use line charts, for example, to examine your rate of customer retention plotted versus the number of calls with negative sentiment. Then, you can listen to recordings of the phone calls that have negative sentiment and correlate to a decrease in retention to find out why customers are leaving.
- Guiding quality assurance auditors to areas that require attention. Auditors do not have enough time to listen in on every phone call and monitor every interaction for quality. Sentiment Analysis helps identify the calls that had negative sentiment, giving auditors a good starting point for their reviews.
- Amplifying the Voice of Customer. Sentiment Analysis data can be combined with post-call surveys to reveal more information about how customers really feel about their interactions with your agents.
How Can SunView Software Help?
Internal and external customers expect fast and personalized experiences. When they reach out to your service desk, they want to feel like someone is really listening and cares about helping them. But it can be hard to provide a great experience when thousands of tickets are flooding your IT department. With AI-based Sentiment Analysis, you can identify critical or urgent issues as soon as they arrive and assign them a high priority. Then you can set up rules to automatically route urgent tickets to the right team, agent, or chatbot.