In the dynamic landscape of customer support, the ability to discern the underlying sentiment within customer messages is paramount. Sentiment Analysis emerges as a robust tool, empowering support agents to decode whether customers convey positive, negative, or neutral sentiments. This insight facilitates tailored responses and enriches the overall conversational experience.

Key Benefits

• Enhanced Customer Experience: By accurately detecting and understanding consumer sentiment, agents can craft personalized and empathetic responses, thereby elevating overall customer satisfaction.
• Efficiency Boost: Prioritizing and promptly addressing negative sentiment messages reduces resolution times, enhancing support efficiency.
• Data-Driven Insights: The accumulation of sentiment data offers valuable insights into consumer sentiment trends, pinpointing areas for support enhancement and strategic improvement.
• Seamless Integration
  Integrated directly into the Agent Workspace, Sentiment Analysis seamlessly becomes a part of your support team's workflow, enabling them to gauge player sentiment effortlessly.

Understanding Sentiment Analysis

The functionality of Sentiment Analysis revolves around several key aspects:

1. Real-Time Sentiment Detection

As conversations unfold, the system dynamically evaluates the sentiment of customer messages, providing agents with a continuous view of the customer's emotional state. This real-time analysis categorizes sentiments as positive, neutral, or negative.

Note: Sentiment analysis runs only on user messages text messages and responses to bot messages of text type.

2. Contextual Understanding: Multi-Message Responses

Earlier, sentiment analysis handled messages individually, often missing the context in multi-message responses. Now, the system evaluates consecutive messages together, providing a unified sentiment that captures the overall tone.

This contextual approach creates more accurate and reliable sentiment analysis. It helps agents respond to the customer's overall mood rather than to isolated messages.

3. Enhancing Sentiment Analysis Based on CSAT Ratings

The system now incorporates CSAT ratings to refine sentiment detection, ensuring it aligns with customer feedback. This enhancement provides a more accurate and holistic representation of the customer experience, helping teams better understand and respond to user sentiment effectively.

4. Callout Events

Sentiment transitions, such as from negative to neutral, are highlighted on the issue conversation screen, drawing attention to critical shifts in customer sentiment.

5. Advanced Search and Smart Views

Agents can leverage advanced search queries to filter and prioritize customer issues based on their sentiment. Moreover, creating sentiment-based shared smart views facilitates the categorization and resolution of issues according to detected sentiments.

To filter the issues by sentiments using advanced search, follow the steps given below:

1. Navigate to Issues > All New Issues.
2. In the advanced search section, enter the text/field values relevant to the search.
   

Agents can create sentiment-based shared smart views to categorize and address customer issues based on their detected sentiments. This can help them to provide personalised responses.

Creating Sentiment-Based Smart View

To create a sentiment-based shared smart view, follow the steps given below:

1. Navigate to Issues.
2. Click on the “+” icon next to the SHARED SMART VIEW.
   A pop-up will appear where you can enter the name of the Smart View and apply filters.
   
3. Click on ADD FILTER.
   The filters will be available for you to choose from.
4. Select the sentiment-related filters.
   
   
5. For Shared Smart Views, you can also configure the sharing settings in the Share with area.
   Select ‘Everyone’ to allow all of your team members to access this Smart View, or select ‘Only Admins’ or ‘Admins + selected Groups’ to restrict access.
6. Click SAVE.
   The Shared Smart View will immediately become visible to everyone you’ve shared access with per the ‘Shared with’ settings.

6. HSAPI

Access to sentiment details is facilitated through HSAPI, empowering deeper analysis and insights.

Color Codes for Sentiment Analysis

The sentiment analysis feature employs color-coded representations to convey the sentiment of customer messages:

• Green: Indicates a positive sentiment.
• Yellow: Indicate a neutral sentiment.
• Red: Indicates a negative sentiment.