Interpret Humind metrics with the correct period, scope, and customer journey context This guide keeps the work focused on Analytics and provides a repeatable path that a new Humind user can follow without changing unrelated configuration.

Humind analytics helps teams inspect conversations, questions, pages, topics, and Agent performance. Each view answers a different question and depends on its selected period and available data. Analytics supports investigation and prioritization, but a chart should be checked against conversations and business context before it drives a change.

Before you begin

Access: Analytics read access is required. Conversation evidence may also require Inbox access.

• Write the business question before opening a dashboard.
• Choose the Agent, channel, and period that should answer it.
• Know whether recent configuration or deployment changes occurred.

Work in the smallest owning area described below and keep the current customer-facing state available while you prepare the change. Before clicking any final action, confirm the active company, Agent, store, language, and market shown in Humind. A missing control can indicate read-only access or a capability that is not configured for this company. In that case, record the intended task and ask an administrator to review the exact permission or dependency. Do not bypass the boundary by sharing an account, copying data into another area, or promising a capability that the workspace does not expose.

Step-by-step workflow

1. Confirm the scope and current state

   Open the Analytics overview and confirm filters, timezone, comparison period, and data scope before reading a headline value.

   • Confirm the active company and Agent before editing.
   • Record the current state so the result can be compared after the change.
   • Stop if the screen or permission does not match the intended task.

2. Prepare the change

   Read each metric according to its label and available definition, separating conversations, visitors, outcomes, ratings, and attributed events.

   • Use the smallest change that completes the customer task.
   • Keep authoritative information in its owning source.
   • Review labels, dates, language, and customer-visible wording before saving.

3. Save and allow required processing to finish

   Use specialized views and representative conversations to test whether the aggregate pattern describes real customer behavior.

   • Wait for the interface to confirm that the change is saved.
   • If synchronization, indexing, or publication is required, wait for its final state.
   • Reload the area and confirm the saved values persist.

4. Test the complete customer journey

   Record the observation, evidence, uncertainty, and one focused follow-up instead of changing several systems from a single chart.

   • Use a fresh session and a realistic customer scenario.
   • Check desktop and mobile when the result appears on a storefront.
   • Capture the exact failing step if the outcome differs from the expectation.

Important limits and operating notes

• A successful save confirms persistence, not every downstream synchronization or public update.
• Workspace permissions can hide an area or allow reading without allowing changes.
• Do not copy volatile catalog, account, or customer facts into narrative content as a workaround.
• Test only supported capabilities that are visible and configured for the current company.
• Keep changes in Analytics separate from unrelated Agent, Knowledge, catalog, Inbox, or Helpdesk work.

Verify the result

• The selected scope matches the business question.
• Counts and rates are not mixed in the conclusion.
• Representative conversations support or challenge the aggregate interpretation.
• The proposed action names a measurable follow-up period.

Keep a short record of what you tested, which customer scenario you used, and what changed. This makes later troubleshooting more precise and helps another teammate reproduce the result without relying on memory.

Troubleshooting

A metric is blank or unexpectedly zero

Check permissions, selected period, Agent, channel, filters, and data freshness. A valid zero and unavailable data require different conclusions.

Analytics and a conversation appear inconsistent

Confirm attribution window, event time, timezone, and whether the conversation belongs to the same filtered scope. Use several examples before treating one record as a dashboard defect.

Related guides

• use analytics to improve conversations
• how to see analytics
• choose reporting periods and check data freshness
• review and rate batch test results