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Self-Learning Knowledge Dashboard
Identify and close knowledge article gaps autonomously to improve customer support. Analyze historical service interactions to extract unique contact reasons and score your current documentation. Review the lowest Knowledge Coverage Scores to generate targeted recommendations. Use AI to create articles that directly answer underlying customer questions.
Required Editions
For example, a knowledge manager uses the dashboard to identify high-volume customer issues with low coverage scores. The manager then generates targeted articles to close documentation gaps and improve first-contact resolution.
| Available in: Lightning Experience |
| Available in: Enterprise, Unlimited, Developer, and Agentforce 1 Editions with Data Cloud where Tableau Next, Customer Signals Intelligence, Einstein Work Summaries, and Knowledge Creation are enabled. |
Learn about the key metrics on the Self-Learning Knowledge Intelligence dashboard.
| Metric | Calculation |
|---|---|
| Knowledge Coverage Score | Shows the average coverage score of the knowledge base for each contact reason. Use this metric to evaluate if the published articles answer customer questions. |
| Contact Reason | Shows the number of topics or reasons for interactions between customers and service representatives. |
| Times Article Used | Shows the number of times service representatives and customers use an article for a specific contact reason. Use this metric to assess the relevance and utility of an individual knowledge article. |
| Service Interaction Details | Shows the total number of interactions made from each customer with the service reps. |
| Average Positive Sentiments | Shows the percentage of customer interactions classified as positive based on underlying service interactions. |
| Average Negative Sentiments | Shows the percentage of customer interactions classified as negative based on underlying service interactions. |
| Average Mixed Sentiments | Shows the percentage of customer interactions classified as mixed based on underlying service interactions. Use this metric to evaluate interactions that contain both positive and negative feedback. |
| Knowledge Recommendations | Shows recommendations for creating articles to address identified knowledge gaps. Use this metric to prioritize content development and improve knowledge coverage. |
| Contact Reason by Knowledge Score | Shows contact reasons and average knowledge coverage grouped by sentiment. |
| Contact Reason by Volume | Shows the interaction volume and sentiment breakdown for each contact reason. Use this metric to prioritize high-volume customer issues. |
| Contact Reasons Table | Lists reasons for customer interactions, including interaction volume. |
| Average Knowledge Age (Days) | Shows the average number of days since knowledge article was published or last updated. Use this metric to identify outdated content. |
| Sentiment Trend | Shows the trends in customer sentiment over a selected time period. Use this metric to monitor changes in customer satisfaction. |
| Related Articles | Shows the knowledge articles associated with a selected contact reason. |
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