Configuring analytics in Pega Customer Service

You can enable metrics that meet your organization's standards, including:

• Scoring metrics (1–10 scale for objective evaluation)
• Professionalism score (courtesy, respectful behavior)
• Conversation satisfaction score (tone, resolution, quality)
• Interaction effort score (ease of resolution)
• Resolution prediction score (first-call resolution likelihood)
• Customer and agent sentiment scores (sentiment tracking)
• Detection metrics (for example, profanity detection)
• Coaching metrics (for example, professionalism coaching with tailored suggestions).

These preconfigured metrics are part of the Interaction Case Type, as shown in the following figure:

Enable a metric:

1. In the navigation pane of App Studio, click Case types.
2. Open the Interaction Case Type.
3. Click the Settings tab, and then click Analytics.
4. To the right of metric that you want to enable, click the Edit icon.
5. In the Edit field dialog box, in the Field status list, select Enabled.

You can configure custom metrics in App Studio to align with your business goals. This includes defining evaluation criteria, controlling visibility by role, and tailoring Pega GenAI™ instructions for different field types (Text, Number, Boolean).

For example, you can add a custom metric called Topic and keyword detection to identify the topics discussed and keywords used during the interaction. This metric helps surface the main themes of the conversation, such as product names, complaints, or requests, and supports better understanding of customer intent and CSR responsiveness

To add this metric:

1. In the navigation pane of App Studio, click Case types.
2. Open the Interaction Case Type.
3. Click the Settings tab, and then click Analytics.
4. Click Add field, and complete the New field dialog box:
   1. In Field name, enter a name for the metric; for example, Keyword and topic detection.
   2. In Field description, provide a short explanation; for example, This field lists key topics and keywords extracted from the conversation.
   3. In the Field type list, select Text.
   4. In the GenAI instruction text box, write a clear instruction based on the field type; in this case, provide qualitative guidance to GenAI: List the main topics discussed in this conversation. Highlight important keywords, such as product names, complaints, or requests.
      
   5. Optional: Check the Display reasoning box to show the rationale behind the metric.
   6. In the Field visibility section, set when and to whom the metric is visible:
      • During customer interactions
      • During simulator interactions
      • For CSRs: Show during Wrap-up or Past interactions
      • For Managers: Visibility is based on Access Roles.
   7. In the Field status list, activate the metric by selecting Enabled to.

5. Click Submit.
6. Click Save to apply changes.