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            Sentiment Analysis

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            Sentiment Analysis

            Use sentiment analysis in Data 360 to evaluate unstructured text and learn how customers feel about your brand, product, or service. For each record, such as a customer review or survey response, the model assigns a positive, negative, or neutral label and a confidence score. To filter audiences, build dashboards, and trigger automations, apply the model outputs.

            Note
            Note

            All sentiment analysis models and algorithms use the new runtime.

            Use Cases

            Forecasting supports various use cases.

            TYPE USE CASE WHAT DOES IT SOLVE
            Marketing Refine Campaigns To improve messaging, analyze sentiment in campaign responses.
              Segment Audiences Target promoters, neutrals, and detractors differently.
              Track Brand Perception Monitor how campaigns impact sentiment over time.
            Service Prioritize Cases Identify and escalate negative sentiment quickly.
              Improve Support Quality Analyze sentiment in interactions to coach reps.
              Reduce Churn Detect unhappy customers early and trigger recovery actions.
            Sales Deal Health Scoring To gauge deal momentum, analyze sentiment in emails, call transcripts, and meeting notes.
              Pipeline Prioritization Focus on opportunities with sentiment that improves or remains consistently positive.
              Upsell and Cross-Sell Identify satisfied customers with positive sentiment as strong expansion candidates.

            Considerations

            Before you apply a sentiment analysis model, review these considerations.

            • Use clean, relevant text data, such as customer reviews or case comments.
            • Combine sentiment with other data, such as behavior or demographics, for better insights.
            • Analyze trends over time rather than individual results.

            Limitations

            Before you use sentiment analysis, review these limitations.

            • Short, vague, or highly technical text is difficult to interpret.
            • Sarcasm and nuanced language affect accuracy.
            • Results depend on the quality and context of the input data.
             
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