How is call sentiment analysis reported?

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Call sentiment analysis, powered by AI and natural language processing (NLP), provides deep insights into the emotional tone and overall feeling of customer interactions. Reporting on this data moves beyond simple call metrics to understand the "voice of the customer" and the effectiveness of agent communication.

Here's how call sentiment analysis is typically reported:

1. Overall Sentiment Distribution:
Report Type: Pie charts or bar graphs.
Metrics: Percentage of calls categorized as:
Positive: The customer or agent expressed satisfaction, happiness, gratitude, etc.
Neutral: The conversation was largely factual, without strong emotional expression.
Negative: The customer or agent expressed frustration, anger, disappointment, or dissatisfaction.
Some advanced systems may offer more granular buy telemarketing data categories like "slightly positive," "highly negative," "frustrated," "excited," etc.
Analysis: Provides a high-level overview of the general emotional landscape of your telemarketing interactions. A high percentage of negative calls might indicate widespread issues with a product, service, or process.
2. Sentiment Trends Over Time:
Report Type: Line graphs or area charts.
Metrics: Average sentiment score or percentage of positive/negative calls tracked daily, weekly, or monthly.
Analysis:
Identify Spikes/Dips: A sudden spike in negative sentiment could correspond to a product outage, a billing error, a new policy change, or a poorly executed campaign.
Track Impact of Changes: Did a new script or training program lead to an increase in positive sentiment? Did a recent product launch cause more negative sentiment than expected?
Seasonal Patterns: Are there predictable times of the year when sentiment tends to be more positive or negative?
3. Sentiment by Topic/Reason for Call:
Report Type: Bar graphs, treemaps, or heatmaps.
Metrics: Sentiment score associated with specific topics discussed in calls (e.g., "billing inquiry," "technical support," "product features," "order status," "cancellation request").
Analysis: This is incredibly powerful. It helps pinpoint exactly what is driving negative or positive sentiment. For example:
"Calls about product X's new feature have 80% negative sentiment." (Indicates a product issue).
"Calls about onboarding issues have 60% negative sentiment, while calls about delivery status are 90% neutral." (Prioritize fixing onboarding process).
"Billing inquiries often start neutral but end positive when resolved." (Highlights agent effectiveness in handling a sensitive topic).
4. Agent-Specific Sentiment Performance:
Report Type: Agent scorecards, bar graphs, or leaderboards.
Metrics: Average sentiment score per agent, percentage of positive/negative calls handled by each agent, or sentiment trajectory within their calls.
Analysis:
Identify Top Performers: Agents consistently achieving high positive sentiment scores can be identified for best practice sharing and recognition.
Spot Coaching Opportunities: Agents with consistently low sentiment scores or frequent negative shifts during calls might need targeted training on empathy, de-escalation, or objection handling.
Sentiment Shift Analysis: Some tools can show how sentiment changes during a call (e.g., starts negative but turns positive by the end, indicating a successful resolution). This is a strong indicator of agent skill.