FAQs: Zia Dashboard

FAQs: Zia Dashboard

Access and Enablement

What is the Zia Dashboard used for?
Zia Dashboard provides visual dashboards for analyzing support operations. It is part of a larger system (implied to be a customer support platform) and can help businesses understand and analyze their support performance. It can be enabled for the entire organization or specific departments that want to monitor and analyze their support-related data.
How is Zia enabled and where is the dashboard opened?
  1. Navigate to Setup > Zia.
  2. Turn on the Zia toggle.
To open the dashboard
  1. Navigate to the Analytics module.
  2. In the left panel, under Dashboards, select Zia Dashboard.
Can Zia be enabled only for the department that needs monitoring?
Yes. Administrators can enable Zia for the organization or for a particular department. Use department-level enablement where separate support functions need focused monitoring instead of a single organization-wide view.
What types of business data are commonly tracked, and why is measuring this data important?
Businesses commonly track:
  1. Customer data: Demographics, purchase history, satisfaction
  2. Marketing metrics: Website traffic, social media engagement, campaign ROI
  3. Sales and revenue data: Total sales, revenue by product
  4. Employee performance: Individual performance, productivity, training efficiency
Measuring such data provides crucial insights for making data-driven decisions related to improving daily operations, maintaining business continuity, identifying bottlenecks, and troubleshooting flaws. For example, understanding marketing ROI helps allocate funds effectively, while analyzing support channel requests informs resource allocation.
What is a business dashboard, and how does it help in understanding business data?
A business dashboard is a visual representation of data, typically using pictorial or graphical formats. It helps businesses understand and analyze complex information by breaking it down into comprehensible pieces. Instead of sifting through raw data, a dashboard provides a comprehensive overview, making it easier to identify trends, patterns, and key performance indicators.
How does Zia's sentiment analysis work, and what are the benefits of monitoring sentiment trends?
Zia's sentiment analysis evaluates the tone of incoming customer responses over the past 24 hours, categorizing them as positive, neutral, or negative. Sentiment trend analysis then displays this sentiment data in an hourly and daily bar graph. Monitoring these trends helps businesses understand how customers are reacting to their products, services, or changes (like a new pricing plan).

A sudden surge in negative sentiment can signal dissatisfaction and prompt proactive measures to address customer concerns. Tracking sentiment over time can also help in evaluating the effectiveness of support strategies and resource distribution.

Prediction Dashboard

What does the Prediction dashboard show?
The Prediction Dashboard in Zia offers a visual overview of current trends and Zia's prediction capabilities. It includes components like
  1. Trends Vs Incoming/Outgoing Responses" (predicting ticket traffic)
  2. Trending Auto Tags (identifying frequently occurring issues)
  3. Sentiment Analysis (categorizing the tone of customer responses)
  4. Sentiment Trend Analysis (showing sentiment over time).
  5. Field Prediction Dashboard, which compares Zia's predicted values for ticket fields (like category, priority, owner) with actual data.
Monitoring the accuracy of these predictions is important because it indicates how well Zia is learning and adapting to the business's data patterns. If Zia frequently makes incorrect predictions or misses predictions, it may need to be retrained with more data to improve its accuracy and enhance the efficiency of ticket assignment and automation processes.

How does Zia determine expected ticket-response activity?
Zia analyzes ticket traffic from the previous 30 days and predicts the trend for the current day. The yellow line in the trend graph shows the predicted pattern, while the blue line shows the actual incoming-ticket or outgoing-response count for that day.

What does a star on the trend chart mean?
A star marks a significant difference between predicted and actual activity, which Zia identifies as an anomaly. It can represent an unexpected surge or dip in incoming or outgoing responses.

For example, a team may see a sharp increase in incoming requests compared with the 30-day pattern after a service disruption. That deviation is flagged so assigned users can investigate rather than discover the volume change only after backlogs accumulate.
Does a detected anomaly explain its own cause?
No. The dashboard identifies a significant deviation from the expected pattern; it does not, in the available documentation, establish the business cause. Investigate related ticket subjects, tags, channels, and operational changes before deciding whether to add staffing, issue customer communication, or escalate a product problem.

How should a support manager respond to a sudden dip in outgoing responses?
Treat the dip as an investigation signal. Compare it with incoming volume and check for staffing changes, routing or assignment problems, channel interruptions, or workflows that may be delaying agent replies. A lower response count can be positive only when it aligns with lower demand or another verified operational improvement.

How does the "Trends Vs Incoming Responses or Outgoing Responses" component in the Prediction Dashboard work, and why is it useful?
This component analyzes ticket traffic over the past 30 days to predict the trend for the current day. It displays this as a line graph, with a yellow line representing the predicted trend and a blue line showing the actual number of responses. Significant deviations between these lines are marked as anomalies. This is useful for identifying unexpected surges or dips in support volume, allowing businesses to adjust staffing proactively or resources.

Notifications

How are users notified about anomalies?
Zia proactively notifies assigned users through the Zia Notification Center when it detects an unusual surge or dip in incoming or outgoing ticket responses. When an anomaly is detected, the notification panel at the bottom of the screen is highlighted with a red button-like icon.
What information appears in Zia notifications?
The notification panel displays anomalies and alerts.
  1. Anomalies show surges or dips in incoming and outgoing responses.
  2. Alerts show the Answer Bot training status.
How long are anomaly notifications retained?
Zia notifications are available for 15 days from the time an anomaly is predicted. They are removed after that period. Teams that need a longer operational record should review and act on material alerts promptly; the documentation does not state that these notifications provide long-term archival reporting.
How are Zia notifications configured for a department?
  1. Navigate to Setup > Zia > Intelligence.
  2. Select the department at the top of the page.
  3. Turn on Zia Notifications.
  4. Under the recipient selection, choose Agents, Teams, Roles, or Roles and Subordinates.
  5. Click Save.

Can notifications be sent to groups instead of individual agents?
Yes. Notification recipients can be selected as Agents, Teams, Roles, or Roles and Subordinates. Select recipients who can own the follow-up action. For example, route alerts about an abnormal billing-ticket surge to the Billing team or its supervisory role, rather than notifying unrelated agents.
What are trending Auto Tags?
Zia Auto Tag automatically generates tags for support tickets based on their content, helping agents quickly understand the nature of each query and group similar tickets. The "Trending Auto Tags" section visualizes the most frequently occurring tags as a word cloud and in a table, also showing the sentiment associated with each tag. This insight helps businesses identify recurring problems or issues customers are facing, allowing them to address the root causes, reduce complaints, and ultimately enhance the overall customer experience.

For example, a frequently occurring tag with negative sentiment can highlight a specific area needing immediate attention.
How should the tag word cloud be interpreted?
Text size indicates the number of occurrences of a tag, while colors distinguish tags visually. Hovering over a tag shows the number of associated tickets and their sentiments. A tabular view provides the ticket count and sentiment information for each tag.
Why should teams consider sentiment alongside tag frequency?
Frequency alone shows what is common; sentiment helps indicate where the customer experience may be deteriorating. For example, if “cash deposit” is both a recurring tag and associated with the highest negative sentiment, the team can analyze those tickets to find and correct the underlying cause before complaints escalate.
Can trending tags be used as proof of the root cause?
No. Tags provide a concise signal about recurring ticket themes and sentiment context. They should be used to prioritize ticket analysis, not as standalone proof that one product, workflow, or team caused the problem.
How can trending tags support operational decisions?
Use them to identify clusters of customer issues, prioritize investigation, and decide whether to update documentation, staff a queue, or address a product problem. For instance, a sudden rise in a tag related to payment failures can prompt a team to review the related tickets, assess sentiment, and determine whether a broader incident response is required.

Decision-Making Boundaries

Is Zia Dashboard a replacement for detailed ticket analysis?
No. Zia Dashboard provides visual patterns and anomaly signals. Use the dashboard to determine where attention is needed, then inspect related tickets and operational context to validate the cause and choose a response.
What business outcomes can dashboard analysis support?
The available documentation positions business-data measurement as a way to improve daily operations, maintain operational continuity, identify bottlenecks, and troubleshoot potential process flaws. In support operations, recognizing channels with higher request volume can help leaders allocate resources accordingly.

What should teams verify before acting on a dashboard trend?
Confirm the relevant date and department context, compare actual activity with the predicted trend, examine ticket themes and associated sentiment, and identify any known changes such as releases, outages, campaigns, staffing shifts, or routing updates. This avoids interpreting a short-term variation as a confirmed business problem.

Does the available documentation specify the contents or configuration of the Field Predictions dashboard?
No. The available documentation identifies Field Predictions as a Zia Dashboard type, but the retrieved content does not describe its fields, training requirements, settings, output, or limitations. Those details should be confirmed in the dedicated Field Predictions documentation.