Purpose

This article introduces you to the Eightfold (EF) AI Interviewer Dashboard, providing a comprehensive overview of the dashboard functionality, detailed information about each dashboard feature, and the benefits of leveraging AI-powered interview automation for recruitment optimization.

Audience

This document is designed for recruiters, hiring managers, and TA leaders who need to monitor AI interviewer performance, track candidate engagement, and make data-driven decisions to improve the automated screening process.

Introduction

The AI Interviewer Dashboard provides visibility into AI Interview activities and performance metrics. It comprises key performance indicators (KPIs) that measure the effectiveness of automated candidate screening within your organization.

Accessing the Dashboard

1. Open a supported browser and navigate to https://app.eightfold.ai
   
2. Enter your email address and password for your EF account
   
3. Click Sign In
   
4. Navigate to the Insights tab and select AI Interviewer Dashboard under Talent Acquisition

First-Time Users

If you are accessing the AI Interviewer Dashboard for the first time, you will need proper provisioning and permissions. Users should have the permission perm_manage_ai_interviewer_dashboard mapped to their role.

Provisioning Steps:

1. After signing in, go to More → Admin Console → Data Warehouse → Provision Dashboards
   
2. In the AI Interviewer dashboard row, select the Actions icon
   
3. Select Create and then Submit
   
4. Once provisioned, the dashboard will appear in the Dashboards tab

Note: You can view the dashboard only if the relevant permission is added to your role

Data Refresh Frequency

The information on this dashboard is updated every 24 hours.

Dashboard Features

Main Tabs

• Summary - High-level metrics and weekly activity trends
  
• Details - Detailed breakdowns by recruiter and position

Control Filters

Global Filters (Applied to All Sheets)

• Date Range Filters

  • Start & End Date - Date range filter for all AI interviewer activities
    
  • Default Start: April 1, 2025
    
  • Default End: Yesterday (rolling date)
    

• Parameter-Based Filters

  • Position Title - Filter by job title
    
  • Recruiter - Filter by assigned recruiter for the position
    
  • Position Job Function - Filter by job function category associated with the position
    
  • Position Country - Filter by geographic location associated with the position
    
  • Position ID - Filter by unique position identifier
    
  • Hiring Manager - Filter by assigned hiring manager associated with the position
    
  • Breakdown Variable - Segment data by position or candidate attributes
    
    • Default: 'Recruiter'
      
    • Options: Business Unit, Candidate Current company, Candidate Location, Candidate Seniority, Hiring Manager, Job Function, Job Location, Job title, Recruiter

Summary Tab

The Summary tab provides an at-a-glance view of AI interviewer performance through key sections:

Efficiency

Visual funnel showing the progression of candidates through the AI interview process:

Invitations Sent

• Definition: Total number of AI interview invitations sent during the selected time period
  
  • Formula: distinct_countIf({entity_id} , {is_invite_sent} = 1 )
    
  • Event Filter: ['interview_session_candidate_contacted', 'interview_session_feedback_generated']
    
  • Use Case: Track overall AI interviewer utilization and reach

Interviews Completed

• Definition: Average number of interviews conducted by AI interviewer per day for the selected period of time
  
  • Formula: distinct_countIf({entity_id}, {is_interview_completed} = 1)
    
  • Event Filter: ['interview_session_feedback_generated', 'interview_session_interview_completed', 'interview_session_candidate_contacted']
    
  • Use Case: Monitor candidate engagement and completion rates

Effectiveness

Avg Interviews per day

• Definition: Total number of interviews completed by candidates
  
  • Formula: distinct_countIf({unique number of days}, event = 'interview_session_interview_completed')
    
  • where {unique number of days} = extract('DD',{event_date})
    
  • Use Case: Monitor the effectiveness of the AI interviewer

Experience

Metrics focused on candidate and user experience:

Avg Candidate Satisfaction Score

• Definition: Average rating from post-interview surveys (1-5 scale)
  
• Formula: avgIf({interview_survey_rating}, isNotNull({interview_survey_rating}))
  
• Supporting Metrics:
  
  • Interviews with survey rating: distinct_countIf({entity_id}, isNotNull({interview_survey_rating}))
    
• Use Case: Monitor candidate experience quality

Weekly Activity Trend

Vertical bar chart showing invitations sent and interviews completed by week

• Blue bars: Invitations sent per week
  
• Teal bars: Interviews completed per week
  
• Event Filter: ['interview_session_candidate_contacted', 'interview_session_interview_completed']

Key Insights:

• Track seasonal patterns in recruiting activity
  
• Identify weeks with high completion rates
  
• Monitor review backlog accumulation

Invitations sent and interviews completed by ‘Position/Candidate attribute’ by week

It shows the invitations sent and interviews completed by a ‘position or candidate attribute’ which can be selected from the drop down (‘View the data below by’) right above the table.

Details Tab

AI Interview Funnel Formula

The AI Interview Funnel uses a calculated field to categorize events:

ifelse(
    event = 'interview_session_candidate_contacted', 'Invite sent',
    event = 'interview_session_interview_completed', 'Interview completed',
    event = 'interview_session_feedback_reviewed', 'Feedback reviewed',
    'Others'
)

Breakdown by Selected Variable

Dynamic table that segments metrics by the selected breakdown variable (default: Recruiter):

View By Variable Formula:

ifelse(
${ViewByVariable} = 'Job Function', {job_function},
${ViewByVariable} = 'Business Unit', {business_unit},
${ViewByVariable} = 'Job Location', {location_country},
${ViewByVariable} = 'Recruiter', {recruiter_name},
${ViewByVariable} = 'Hiring Manager', {hiring_manager_name},
${ViewByVariable} = 'Job title', {position_name},
${ViewByVariable} = 'Candidate Location', {candidate_location_country},
${ViewByVariable} = 'Candidate Seniority', {candidate_seniority_level},
${ViewByVariable} = 'Candidate Current company', {current_company},
{recruiter_name}
)

Additional Calculated Fields:

• Interviews per day:
  
  • Formula: distinct_countIf({unique number of days}, event = 'interview_session_interview_completed')
    
  • where {unique number of days} = extract('DD',{event_date})
    
  • Use Case: Monitor the effectiveness of the AI interviewer

Columns Displayed:

• Invitations Sent
  
• Interviews Completed
  
• Feedback Review Completed

Details Tab

The Details tab provides granular analysis of AI interviewer performance:

1. Core Interview Metrics by [Breakdown Variable]

Series of horizontal bar charts showing:

Invitations Sent

• Definition: Volume comparison across segments
  
• Event Filter: ['interview_session_candidate_contacted', 'interview_session_call_in_progress']

Interviews Completed

• Definition: Engagement rate comparison
  
• Event Filter: ['interview_session_feedback_generated', 'interview_session_interview_completed', 'interview_session_candidate_contacted']

Feedback Review Completed

• Definition: Review engagement by segment
  
• Event Filter: [event = 'interview_session_feedback_reviewed', 'interview_session_feedback_generated']

Invites with Candidate Assistance Requested

• Definition: Support needs by segment

2. Time to Action by [Breakdown Variable]

Metrics measuring the speed and efficiency of the AI interviewer process:

Avg Time to Interview

• Definition: Average days between invitation sent and interview completion
  
• Formula: avgIf({time_to_interview_days}, IN(event, ['interview_session_candidate_contacted','interview_session_call_in_progress']))
  
• Event Filter: ['interview_session_candidate_contacted', 'interview_session_call_in_progress']
  
• Use Case: Optimize candidate engagement timing

Avg Time to Review Feedback

• Definition: Average days to complete feedback review after generation
  
• Formula: avgIf({time_to_review_feedback_days}, IN(event, ['interview_session_feedback_generated','interview_session_feedback_reviewed']))
  
• Event Filter: ['ai_feedback_reviewed', 'interview_session_feedback_generated']
  
• Note: May show as unavailable if no reviews have been completed
  
• Use Case: Identify bottlenecks in the review process

Total Time Saved for Recruiters

• Definition: Hours saved through AI automation vs. manual interviewing
  
• Formula: ${RecruiterSalaryPerHour} * sum({time_saved_hours})
  
• Alternative Formula: sumIf({time_saved_days}, IN(event, ['interview_session_interview_completed','interview_session_call_in_progress']))
  
• Event Filter: ['interview_session_call_in_progress', 'interview_session_interview_completed']
  
• Default RecruiterSalaryPerHour: 30.0
  
• Use Case: Demonstrate ROI and efficiency gains

3. Effectiveness by [Breakdown Variable]

Value metrics segmented by selected variable:

Total Time Saved for Recruiters (hours)

• Definition: Efficiency gains by segment
  
• Event Filter: ['interview_session_call_in_progress', 'interview_session_interview_completed']

Avg Candidate Satisfaction Score

• Definition: Experience quality variations
  
• Filter: Excludes records where interviews with survey rating is 0

Interviews with Candidate Satisfaction Score

• Definition: Survey response rates
  
• Filter: Excludes records where interviews with survey rating is 0

4. AI Screenings Current Status

Visual breakdown of interview statuses with two views:

Days Since Latest Status Formula

dateDiff({created_at}, now(),'DD')

Status Categories:

• No Show - Candidates who did not complete the interview and their AI interview link has expired (default: 14 days, customizable)
  
• Feedback to be Reviewed - Completed interviews awaiting recruiter review
  
• Invited but Yet to Schedule - Candidates who received invitations but have not scheduled their interview
  
• Feedback to be Generated - Completed interviews currently being processed by AI to generate feedback
  
• Call Disconnected - Interview calls disconnected due to technical issues or candidate choice (if candidates rejoin and complete, status will update)
  
• Call Initiation Error - Candidates who attempted but were unable to start their interview due to technical issues
  
• Call in Progress - Active interview sessions
  
• Feedback Review Completed - Interviews with completed feedback reviews

5. Position & Candidate Details with Latest Status of the AI Interview

Comprehensive table showing:

• Recruiter - Assigned recruiter name
  
• Hiring Manager - Assigned hiring manager name
  
• Job Title - Position being filled
  
• Profile ID - Unique candidate identifier
  
• Candidate Name - Full name of the candidate
  
• Latest Status of AI Screening - Current interview state
  
• Days Since Latest Status - Time in current state (calculated using formula above)
  
• Link to Candidate Profile - Direct profile access
  
• Current Interview Stage - The latest interview stage associated with the candidate if advanced to any stage
  
• Match Score - If the candidate has applied for the position, the match score would get populated accordingly
  
• Candidate Seniority Level - The seniority level associated with the candidate based on their current role
  
• Candidate Location - The location associated with the candidate
  
• Position Location Country - The location associated with the position
  
• Job Function - The job function associated with the position
  
• Business Unit - The business unit associated with the position
  
• Feedback Requester Name - The name of the person who has requested for feedback
  
• Feedback Reviewer Name - The name of the person who has been requested the feedback
  
• Feedback Template Name - The template associated with the feedback
  
• Overall Thumbs Up/Down Rating - The thumbs up or down rating linked to the feedback
  
• Application ID - The application ID associated with the candidate if the candidate has applied for the role

Key Metrics Definitions

Completion Rate Calculations

Interviews Completed

• Point-in-time calculation comparing interviews completed vs. invitations sent during the selected period
  
• Can exceed 100% when completing interviews from previous periods

Feedback Generated

• Compares feedback generated vs. interviews completed during the selected period
  
• Should approach 100% for optimal AI performance

Feedback Review Completed

• Tracks recruiter engagement with AI-generated insights
  
• Lower numbers indicate review backlog

Time-Based Metrics

All time calculations use the actual timestamp of events:

• Interview Time: When candidate completes the AI interview
  
• Feedback Generation: When AI produces the feedback report
  
• Review Completion: When recruiter marks feedback as reviewed

Additional Calculated Metrics (Backend)

Dataset: position_stats_ai_interviewer

1. Positions utilizing AI interviewer: distinct_countIf({position_id},{ai_interviewer_utilization_indicator} = 1)
   
2. Total 4+ match score applicants: sum({four_plus_match_score_applicants})
   
3. Total 4+ match score applicants contacted via AI interviewer: sum({four_plus_match_score_applicants_contacted_via_ai_interviewer})
   
4. Total applicants across all positions: sum({total_applicants})
   
5. Total applicants contacted via AI interviewer: sum({applicants_contacted_via_ai_interviewer})
   
6. Total positions: distinct_count({position_id})

Dataset: user analytics with positions

1. Number of overdue interviews: distinct_countIf( {entity_id},{is_overdue_interview}=1)
   
2. Candidates who completed interviewing: distinct_countIf({profile_id}, event = 'ai_interview_completed')
   
3. Candidates who completed interviews: distinct_countIf({profile_id}, event = 'interview_session_interview_completed')
   
4. Interviews with survey rating: distinct_countIf({entity_id}, isNotNull({interview_survey_rating}))
   
5. Cost saved: ${RecruiterSalaryPerHour} * sum({time_saved_hours})

Insight Interview Session Dataset

1. Days since latest status: dateDiff({created_at}, now(),'DD')
   
2. View by variable: Same formula as above, applied to insight_interview_session dataset

Best Practices

Optimizing Interview Completion Rates

• Send invitations early in the week for higher engagement
  
• Follow up within 48 hours for candidates who don't schedule quickly
  
• Monitor "Invited but yet to schedule" status for messaging issues

Managing Feedback Reviews

• Set review SLAs and aim to review feedback within 2 business days
  
• Prioritize by position urgency and focus on critical roles first
  
• Use batch review by position for efficiency

Improving Candidate Experience

• Monitor satisfaction scores by recruiter to identify training opportunities
  
• Track "No show" rates to identify potential scheduling issues
  
• Review assistance requests to understand common pain points

Troubleshooting

Common Issues and Solutions

• High "No Show" Rate - Review invitation messaging and ensure calendar links work properly
  
• Low Feedback Generation Rate - Check for technical issues and verify AI interviewer configuration
  
• Feedback Review Backlog - Set up daily review reminders and consider team delegation

Frequently Asked Questions

When do metrics update? Metrics update once every 24 hours. The data should be complete until yesterday.

Why might completion rates exceed 100%? This occurs when candidates complete interviews in the current period that were invited in previous periods.

How is recruiter time saved calculated? Based on the formula ${RecruiterSalaryPerHour} * sum({time_saved_hours}) with a default hourly rate of $30.

Can I export the data? Yes, each chart and table has export options. Look for the menu icon (three dots) in the top-right corner of each visualization.

What determines candidate satisfaction? Candidates rate their experience on a 1-5 scale immediately after completing the AI interview. This is optional and response rates vary.

Related Resources

• AI Interviewer Guide
  
• Understand AI Interviewer score
  
• Dataset definitions