Skip to main content

Team Analytics

.

Team Analytics

Track how specific groups within your organization are using Biggy with Biggy Teams. The Team Analytics page provides usage and feedback metrics to track the team's engagement.

Use the drop-down menus at the top right of the screen to adjust the selected time period or adjust the Team or Team Group.

biggy_webapp_teamanalytics.png

The following widgets are available in Team Analytics:

Widget

Details

Total Queries

Number of queries the team has sent to Biggy.

Active Users

Number of unique active team users.

Reports Generated

Number of reports generated by the selected team.

Positive Feedback

Percent of positive feedback the team has sent to Biggy.

Daily Usage Trend

How often your team sends queries to Biggy, over time. The green line shows daily active usage. The blue line shows the number of daily active users. 

Top Action Plans

Pie chart displaying the top 5 action plans that the selected team used the most.

Team Feedback Momentum

How sentiment and value are trending across the team.

Feedback Rate shows the number of times the team provided feedback to Biggy. 

Positive Rate shows the number of times the team gave Biggy positive feedback.

Time Saved shows the average amount of time that Biggy saved the team, based on user feedback. 

Feedback Over Time

Daily positive and negative feedback sent to Biggy by the team, over time.

Usage by Platform

Distribution of queries broken down by platform (MS Teams, Slack, or Web Chat).

Action Plan Usage Flow

Flowing visualization of the team's action plan usage trends.

Hove over a section of the graph to view which action plans were used on that date, with a breakdown of the total number of queries per action plan.

User Activity

Breakdown of who on the team is using Biggy the most. The following information about the team is displayed:

  • Number of users

  • Number of active users

  • Number of queries

  • Average number of queries per user

  • Date a user was last active

Click a team name to view additional information about each user. The following information about each user is displayed:

  • User name

  • Number of queries

  • Number of active days

  • Average number of queries per day

  • Number of action plans used

  • Last active date

Click any column to sort the list of users by that facet.

AI Team Report

An on-demand qualitative analysis for the team. Click Generate Report or Generate New to create the report.

See the AI Team Report section for more information about the contents of the report.

Drill-Down

Table displaying recent team activity. The table is separated into two tabs: Recent Queries and Recent Feedback.

For each recent query, the following information is displayed:

  • Platform where the activity took place (MS Teams, Slack, or Web App)

  • User

  • User Query

  • Action Plan

  • Date

  • Biggy's Response

For each recent feedback, the following information is displayed:

  • Platform where the feedback was given (MS Teams, Slack, or Web App)

  • Feedback Sentiment (Positive or Negative)

  • Feedback

  • Query

  • Date

  • Action Plan

  • Context Issue

  • Time Saved

  • Biggy's Response

AI Team Report

The AI Team Report gives you on-demand qualitative analysis for your selected team or team group.

To generate a report, click Generate Report or Generate New.

After the report is generated, you can view previous reports by selecting them from the drop-down menu, or click Export PDF to export the report. 

biggy_webapp_aiteamreport.png

The report is divided into the following sections:

Section

Description

Overall Sentiment

Executive summary of the team's sentiment and adoption of AI Incident Assistant.

Adoption Health

Details about the team's adoption of AI Incident Assistant.

The Signals section shows signs of positive adoption health.

The Risks/Gaps section highlights issues that may hinder the team's adoption of AI Incident Assistant.

Value Insights

Benefits users are getting out of AI Incident Assistant.

AI query quality and coaching

How well users provide actionable context in their queries, and how to improve.

What users are using AI Incident Assistant for

Categories of query types that users on the team are asking AI Incident Assistant, and the number of queries in each category. Categories do not show raw queries.

What the team is trying to do

Jobs that the team is trying to complete, inferred based on query history.

Common themes

Recurring patterns across usage and feedback.

What's working well

User behaviors or AI Incident Assistant features that are working well.

What needs attention

Gaps, friction, or risks to address related to AI Incident Assistant usage.

Feedback themes

Common praises and complaints about AI Incident Assistant, with estimated counts.

Recommended next actions

Recommended next actions and configuration steps.

Each action listed contains a description, impact level, level of effort, completion steps, and success metrics.