Businesses track a range of data to monitor their overall performance, make informed decisions, and improve their operations. While the type of data and its usefulness varies from business to business and their goals, some of the most common data measured across industries include:
- Customer Data, which primarily includes the detail about their customer demographic, purchase history, and even customer satisfaction and feedback.
- Marketing Matrix from website traffic to social media engagement, campaign outreach, and ROI.
- Sales and Revenue Data, along with total sales, data on revenue by product or service.
- Employee Performance, including individual's performance, productivity, efficiency of training programs, employee satisfaction, and so on.
Why is it important to measure this data?
Measuring this provides your business with important insights, which in turn allows you to make data driven decisions for:
- Improving daily operations
- Maintaining continuity of various business operations.
- Identifying bottlenecks
- Troubleshooting potential flaws in the process
For example, by knowing which marketing technique is fetching better ROI, your business can allocate more funds for them. Knowing which support channel receives more requests can lead to allocating resources accordingly. Insight on what type of service or product is trending helps stocking inventory .
Understanding data is the key to making decisions for any business. However, the biggest challenge for any business is to tap this vast information, break them down into comprehensible pieces of information, and eventually process the underlying information.
This is where a dashboard is useful. A dashboard is a visual representation of data. It takes the available data and represents it in pictorial or graphical format for a comprehensive understanding.
Zia provides various dashboards, that can help businesses understand and analyze their support operations.
Configuring Zia Dashboard
Admins can enable Zia for the organization, or for a particular department for which they want to monitor and analyze performance.
Availability
Permission Required
Admins can enable Zia for the organization, and profiles with permission enabled to access Reports and Dashboards can access Zia Dashboard
Check Feature Availability and LimitsTo configure Zia
- Navigate to Set up page > Zia.
- Toggle Zia option on top, to enable Zia.
To access Zia Dashboard
- Navigate to the Analytics module.
- On the left panel, under Dashboards, click Zia Dashboard.
Prediction Dashboard
The Prediction dashboard provides a visual representation of the current trends. The dashboard displays the following components:
- Trends Vs Incoming Responses or Outgoing Responses
- Trending Auto Tags
- Sentiment Analysis
- Sentiment Trend Analysis
Trends Vs incoming or outgoing responses
Zia analyzes the ticket traffic for the last 30 days and predicts the trend for current day. When there is a surge or dip in the predicted pattern, it marks it as an anomaly and notifies the assigned agent via Zia Notification Center
The line graph displays the time versus the number of responses in the x and y axis respectively.

- The yellow line shows the predicted trend as observed for the last 30 days.
The tabular format displays more details. It shows the number of tickets and sentiments associated with each tag.
This insight helps the user to identify recurring problems or issues faced by their customers and allows them to address causes, reduce complaints, and ultimately enhance the overall customer experience.
For example, if "cash deposit" is a recurring tag that is associated with the highest negative sentiment, it might indicate that customers are facing problems with cash deposit. A quick analysis of the tickets can help understand the cause and rectify the issue before it escalates.
Sentiment Analysis
Having insight into each customer response gives agents an advantage in handling tickets on a case-by-case basis. Zia’s sentiment analysis evaluates the tone of incoming responses in the past 24 hours, categorizing them as positive, neutral, or negative.
For example, a sudden surge in tickets with negative sentiment after introduction of new pricing plan can indicate dissatisfaction and businesses can prepare a contingency plan by offering a limited time discount on the new plan to retain customers.
Sentiment trend analysis
Sentiment trend analysis displays the sentiment of responses in a bar graph format. This detailed view shows sentiment analysis on an hourly basis for the past 24 hours and on a daily basis.
Having access to insight of both hourly and day-wise breakdown of sentiment analysis helps the team identify patterns and rework on resource distribution and support strategy accordingly.
For example, during a promotional sale, the team notices an increase in negative sentiment related to time of delivery.
The customer service manager can resolve it by identifying the peak hours and increasing the number of field agents to reduce delivery time. They also monitor the sentiments in real time to gauge the effectiveness of their plans.
Note: Sentiment analysis provides an overview for the past 24 hours, while sentiment trend analysis gives hourly data.Field prediction dashboard
Zia can predict values of certain fields based on its training. It can predict the ticket category, priority, issue type, ticket owner, and more, improving assignment and automations.
The Field Prediction Dashboard provides an overview of Zia's prediction capabilities by comparing real-time data with predicted values.
For example, if Zia predicts incorrect value for the field "Issue Type" in about 500 tickets out of a total of 800 tickets, it indicates that Zia needs to be retrained using the new set of tickets as its analyses needs to be widened with more variety.
The dashboard shows a comparison of predicted and actual response.
Incoming vs. Predicted response
By comparing incoming responses with Zia's predicted responses, you can monitor how well the system’s prediction is aligned with the real-time data .
Incoming vs. Missed predicted responses
This bar graph shows the number of incoming responses versus the number of tickets for which Zia skipped the predictions.
In this case, Zia missed 300 incoming responses that were supposed to have their issue type field updated as Bug, but were instead misclassified as Feature Request.
All predicted responses vs. Overridden responses
Zia's field prediction capability or efficiency is based on the pattern it understands by training from previously available data, so there may be instances where the predicted field value is not relevant. In such cases, the agent can manually change the field value.
For example, if all high-priority tickets were previously handled by senior support agents, Zia may predict the senior agent as the ticket owner for all high-priority tickets. However, if a high-priority ticket is escalated by a customer and needs to be handled by a manager, the agent or manager can update the ticket owner. In this case, the value predicted by Zia is overridden by the agent.
To access Predictions Dashboard
- Navigate to the Analytics module.
- In the left panel, under Dashboards, click Zia Dashboard.
- Click Field Predictions Dashboard.