Search Analytics Dashboard

The Search Analytics Dashboard within the Antimanual plugin serves as the central intelligence hub for understanding how visitors interact with your AI-powered search blocks. By capturing and visualizing search data, the dashboard transforms raw query logs into actionable insights, allowing site administrators to refine their knowledge base and improve user experience based on real-world demand.

Table of Contents

• Statistical Overview and KPIs
• Visualizing Search Trends
• Analyzing Top Queries and Knowledge Gaps
• Practical Application of Analytics Data
• Frequently Asked Questions

Statistical Overview and KPIs

At the top of the Analytics view, users are presented with a series of high-level Key Performance Indicators (KPIs) that provide an immediate snapshot of the search block’s performance. These metrics are essential for measuring the engagement levels of your audience and the efficacy of the AI responses.

• Total Queries: This represents the lifetime count of every search interaction performed through the AI search block. It is a primary metric for understanding the overall adoption of your search tool.
• This Month: A rolling 30-day count of search activity. This helps administrators identify whether search volume is increasing or decreasing relative to previous periods.
• Helpful Votes: One of the most critical feedback loops in the system. This metric reflects how many times users explicitly marked the AI’s generated response as helpful, providing a direct score for the quality of your .

Visualizing Search Trends

The Search Trends Graph provides a chronological visualization of search volume. By plotting queries across specific dates, the dashboard allows you to identify peak usage times. This data is invaluable for several reasons:

• Marketing Correlation: Identify if a specific newsletter or product launch led to a spike in user questions.
• Support Planning: If searches consistently peak during specific hours, you can better allocate human support resources for those windows.
• Seasonality: Spot recurring patterns in user interest throughout the month or year.

Analyzing Top Queries and Knowledge Gaps

The Top Queries Table is perhaps the most strategically significant feature of the dashboard. It extracts and aggregates the most frequent questions and phrases typed by your users. Rather than looking at individual sessions, this table highlights the collective needs of your audience.

For example, if the query “How to reset my password” appears at the top of the list with a low “Helpful Vote” count, it indicates a critical knowledge gap. Users are looking for specific information that the AI might not be fully equipped to answer yet. This provides a direct roadmap for what new content needs to be added to your Knowledge Base or which existing documents require more detail.

Practical Application of Analytics Data

To maximize the value of the Search Analytics Dashboard, administrators should perform a weekly review of the “Top Queries” and “Helpful Votes.” If a specific query has high volume but low helpfulness, consider the following workflow:

1. Navigate to the to see the exact AI response provided for that query.
2. Evaluate if the source material in the Knowledge Base is outdated or incomplete.
3. Update the relevant WordPress post or upload a more detailed PDF to the system.
4. Monitor the Analytics Dashboard the following week to see if the “Helpful Votes” for that query increase.

Frequently Asked Questions

How often is the analytics data updated?
The dashboard updates in near real-time. As soon as a user completes a search and submits a feedback vote, the data is processed and reflected in the KPI cards and graphs.

Can I export the analytics data for external reporting?
While the dashboard provides a visual overview, detailed exports of raw data can be handled through the section, where you can filter by date ranges before exporting.

Are searches from logged-in administrators counted?
By default, all searches are tracked. However, you can use the detailed logs to filter by user identity to separate internal testing from actual customer searches.