Businesses commonly track:

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.

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.

The Zia Dashboard is a feature that provides visual representations of data specifically related to 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.

The Prediction Dashboard in Zia offers a visual overview of current trends and Zia's prediction capabilities. It includes components like

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.

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.

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.

The Field Prediction Dashboard compares Zia's predicted values for ticket fields (e.g., issue type, priority) with the actual values. It shows metrics like:

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.