Overview

The 360 Interview (Modular Interview) Dashboard tracks the same AI-conducted interview lifecycle as the AI Interviewer Dashboard — invitations, completions, feedback generation and review, candidate experience, and downstream hiring outcomes — with added visibility into how modular (multi-section) interview guides are configured and used, plus credit purchase and consumption for your AI Interviewer plan. It adds section-level metrics not found on the original dashboard, covering section adoption, per-section timing, section completion, and credit share by section count and avatar usage.

Metrics

AI Interview - Active Positions

Definition: AI Interview - Active Positions measures the proportion of job positions where AI Interviewer is actively configured and available for use. It's a primary indicator of product adoption, showing how widely AI interviewing has been integrated into the requisition workflow — a higher percentage suggests successful rollout and internal buy-in, while gaps highlight where more enablement may be needed.

How it is calculated: Divides positions with at least one AI interview session by the total count of positions.

Note:

• No date filter — reflects the current snapshot, not a trend.
• Assumes the underlying position list already excludes sourcing-pipeline and draft-only records.

AI Interview Invites Sent

Definition: AI Interview Invites Sent represents the total number of interview invitations distributed through the AI Interviewer platform within the selected period. It's a key indicator of usage volume, product adoption, and expected credit consumption — tracking invitation volume lets teams gauge the scale of AI Interviewer deployment and recruiter engagement with the tool.

How it is calculated: Counts invitations sent to candidates.

Note:

• Filtered by invitation sent date. Reflects the most recent invite if a candidate was re-invited.

AI Interviews Completed

Definition: AI Interviews Completed measures the total number of AI interviews successfully completed by candidates within the selected date range. It helps TA teams track completion volume, understand candidate engagement with the process, and measure throughput feeding into feedback generation and review.

How it is calculated: Counts interviews with a recorded completion event, restricted to interviews that also have a recorded invitation.

Note:

• Filtered by completion date. Interviews missing a valid invitation are excluded from the count.

Weekly Trend of Invites Sent

Definition: Weekly Trend of Invites Sent visualizes the volume of AI interview invitations sent each week, helping TA teams track recruiter outreach activity, identify staffing fluctuations, and measure invitation capacity over time.

How it is calculated: Sums invitations sent per calendar week, based on invitation date.

Note:

• Filtered by invitation sent date.

Weekly Trend of Interviews Completed

Definition: Weekly Trend of Interviews Completed visualizes the volume of AI interviews completed each week, helping TA teams track completion activity and candidate participation over time. Read alongside the Weekly Trend of Invites Sent, divergence between the two series is an early signal of candidate friction or scheduling problems.

How it is calculated: Sums completed interviews per calendar week, based on completion date, limited to interviews with a recorded invitation.

Note:

• Filtered by completion date.

AI Interview Completed — Multi vs Single Section

Definition: This donut shows the share of completed AI interviews billed as multi-section versus single-section. It helps TA teams understand adoption of multi-section (360-style) interview guides against single-section guides, and track how guide design choices evolve. The split is derived from the real billed credit value rather than from guide configuration.

How it is calculated: Groups completed modular interviews by billed credit tier — 3 or more credits counts as more than one live section, 1.5 credits or fewer as exactly one.

Note:

• Filtered by completion date. Restricted to Modular-type interviews.
• Assumes billed credit value is a reliable stand-in for how many live sections an interview had.

Section Adoption by Type

Definition: Section Adoption by Type shows, for each of the eight section types (Pre-screening, Assessments, Screening, Role fit, Conceptual Q&A, Coding exercises, Algorithmic challenges, Case study), the number of completed AI interviews whose guide included that section. It helps TA teams understand which section types are most commonly configured into interview guides and track adoption of newer types. This measures guide configuration, not candidate behaviour — a section counts because it was configured, whether or not the candidate reached it.

How it is calculated: Counts completed modular interviews per section type included in the guide.

Note:

• Filtered by completion date. Two raw section types (hld, product_case) both collapse into "Case Study".

Average Interview Time per Section

Definition: Average Time per Section shows the mean time, in minutes, candidates spend in each live section type. It helps TA teams identify which section types take the longest, spot sections that may need to be shortened or restructured, and understand where interview time is actually spent.

How it is calculated: Averages the time between a section's start and its own completion event, per section type, converted to minutes.

Note:

• Filtered by invitation sent date. Only live sections are included — pre_screening and assessment don't count. A section started but never logged as completed is excluded rather than estimated.

Average Interview Time

Definition: Average Interview Time represents the mean duration, in minutes, of all completed AI interviews within the selected period. It's wall-clock time on the call, so breaks and rejoins inside the session are included.

How it is calculated: Averages the time from call start to interview completion, across all completed interviews.

Note:

• Filtered by completion date.

Interview Completion Rate (%)

Definition: Interview Completion Rate (%) measures the percentage of AI interview invitations sent within the selected date range that resulted in interview completion within that same period. It helps TA teams understand attendance and completion during a specific timeframe and identify where candidates drop off after receiving invitations.

How it is calculated: Divides completed interviews by invited interviews.

Note:

• Both the invitation-sent event and the completion event must fall within the selected date range — an invite sent before the period but completed during it is excluded, and a completion that occurred before the period is excluded.

Section Completion % by Section Type

Definition: Section Completion % by Section Type shows, for each live section type, the share of candidates who started that section and went on to finish it, with the remainder representing drop-off. It pinpoints exactly which section types candidates most often abandon mid-interview.

How it is calculated: Divides sections with a recorded completion event by sections that were started, per section type.

Note:

• Filtered by invitation sent date. Only completed interviews are included. There's no fallback to the interview's end timestamp, so a section started but never logged as completed counts as drop-off.

Hire Rate % - AII

Definition: AI Interview Hire Rate (%) measures the percentage of candidates who completed at least one AI interview and were subsequently hired within the selected date range. It shows the conversion effectiveness of the AI interview process from completion to hiring outcome — a high hire rate indicates candidates completing AI interviews progress successfully through the rest of the hiring process, while a low rate may point to AI interview quality, feedback utilisation, or downstream hiring process efficiency needing attention.

How it is calculated: Divides hired candidate-position combinations by completed-interview candidate-position combinations.

Note:

• Filtered by stage timestamp.

AI Recruitment Funnel

Definition: AI Recruitment Funnel visualizes the end-to-end candidate journey from the initial AI interview invitation through to hire. It's the primary tool for identifying drop-off points across the full recruitment lifecycle — where candidates disengage, where review bottlenecks form, and how effectively AI interviews convert into downstream hiring outcomes. Stages are Invite Sent, Interview Completed, Feedback Reviewed, Offer Extended and Hired.

How it is calculated: Counts distinct candidate-position combinations reaching each stage.

Note:

• Filtered by stage timestamp. A candidate invited before the selected period is still counted if a later stage occurs within it.

Most Prolific Recruiters using AI

Definition: Most Prolific Recruiters Using AI provides a ranked breakdown of recruiter activity across unique applications that were sent at least one AI interview invite within the selected range. Each row shows one recruiter's contribution to the hiring funnel, from initial invite through to hire. Comparing Completion Rate, Review Rate and Hire Rate across recruiters lets TA leads identify top adopters, share best practices, and pinpoint team members who might benefit from additional support.

How it is calculated: For each recruiter, across distinct candidate-position combinations: invites sent, interviews completed, feedback reviewed, offers extended, hires, plus completion rate, review rate, and hire rate as percentages, and total positions worked.

Note:

• Unassigned recruiters are excluded. Filtered by stage timestamp — an invite sent before the period still counts if a later stage occurs within it.
• Hire Rate (%) reflects conversion from offer to hire, not from invite to hire.

Most Prolific Sourcers using AI

Definition: Most Prolific Sourcers Using AI is the sourcer-level counterpart to Most Prolific Recruiters Using AI. Each row shows one sourcer's contribution to the hiring funnel across unique applications sent at least one AI interview invite within the selected range — using an identical calculation and stage mapping, so the two can be read side by side to see where sourcing and recruiting activity diverge.

How it is calculated: Identical to Most Prolific Recruiters using AI, grouped by sourcer instead of recruiter.

Note:

• Unassigned sourcers are excluded. Filtered by stage timestamp.

Time to Screen using AII

Definition: Time to Screen with AI measures the average time, in days, from when a candidate is invited for an AI Screening interview to when they complete it. It shows candidate responsiveness and surfaces delays in the AI screening step. Only completed Screening interviews are included.

How it is calculated: Averages the days between invitation and completion, for Screening-type interviews only.

Note:

• Filtered by completion date.

Time to Hire using AII

Definition: Time to Hire using AII measures the average number of days from application submission to hire for candidates who were screened using AI Interview. It shows how AI-powered screening affects overall hiring speed.

How it is calculated: Averages the days between application and hire, for hired candidates who went through an AI interview of any type.

Note:

• Filtered by hire date (applications table).

Time to Fill using AII

Definition: Time to Fill using AII measures the average of the per-position median days from position creation to hire, for positions that had AI Interview enabled — considering only hires that went through AI Interview screening. It complements Time to Hire using AII: where that metric is candidate-centric, this one is requisition-centric and shows how quickly AI-enabled roles are actually being closed.

How it is calculated: For each AI-enabled position, the median time-to-fill is computed, then averaged across all such positions.

Note:

• Filtered by hire date (applications table). Only positions with at least one qualifying hire are included.

Average Match Score - AII Hires

Definition: Average Match Score - AII Hires measures the average Eightfold match score of hired candidates who were interviewed through AI Interviewer. It assesses the quality of candidate-position fit for hires that went through AI-powered screening.

How it is calculated: Averages the match score for hired candidates who went through an AI interview of any type.

Note:

• Filtered by hire date.

Candidate NPS - AI Interviews

Definition: Candidate NPS measures how likely candidates are to recommend the AI Interview experience to others, using Net Promoter Score methodology adapted to the 1-5 survey scale. NPS ranges from -100 to +100; a positive score indicates more promoters than detractors.

How it is calculated: Subtracts the count of detractors (rating ≤3) from the count of promoters (rating ≥4), divided by all respondents, times 100.

Note:

• Filtered by completion date. Only candidates who provided a rating are included.

Candidate Satisfaction Score

Definition: Candidate Satisfaction Score represents the average rating candidates gave their AI interview experience on a 1 to 5 scale. It's a direct indicator of candidate sentiment and interview quality from the applicant's perspective, and can be segmented by recruiter and interview type to see where experience varies.

How it is calculated: Averages the survey rating across all rated sessions.

Note:

• Filtered by completion date.

Low Satisfaction Scores by Interview Type

Definition: Low Satisfaction Scores by Interview Type shows the count of AI interviews with a candidate survey rating below 3, broken down by interview type. It helps identify which interview formats generate the most negative candidate experiences so improvements can be prioritised.

How it is calculated: Counts rated interviews with a survey rating under 3, grouped by interview type.

Note:

• Filtered by completion date. Only candidates who provided a rating are included.

Low Satisfaction Scores

Definition: Low Satisfaction Scores measures the count of AI Interviews where the candidate gave a survey rating below 3. It flags poor candidate experiences so teams can investigate issues with specific interview types or recruiters.

How it is calculated: Counts rated interviews with a survey rating under 3.

Note:

• Filtered by completion date. Only candidates who provided a rating are included. Also available broken down by interview type (Low Satisfaction Scores by Interview Type).

Feedback Review Time

Definition: Feedback Review Time measures the average number of days a recruiter takes to review a candidate's AI-generated feedback after it becomes available. It identifies bottlenecks in the post-interview evaluation phase and shows whether the hiring process keeps momentum after the interview itself.

How it is calculated: Averages the time between feedback generation and feedback review, converted to days.

Note:

• Filtered by feedback review date — this dataset takes the most recent (MAX) review event when more than one exists.

Trend in Pending Feedback Reviews

Definition: Pending Feedback Reviews Over Time shows the monthly trend of AI interview feedback that has been generated but not yet reviewed. It tracks whether the review backlog is growing or being worked down. Because the pending flag is evaluated at query time, each month's bar shows how much of that month's generated feedback is still unreviewed today, rather than what was pending during that month.

How it is calculated: Counts feedback reports not yet marked as reviewed, grouped by the month feedback was generated.

Note:

• Filtered by feedback generation date.

Pending Feedback Reviews

Definition: Pending Feedback Reviews shows the total number of AI interview feedbacks that have been generated but not yet reviewed by recruiters. It's the headline measure of evaluation backlog and of whether candidates are waiting on a human decision — a live queue depth rather than a cumulative total.

How it is calculated: Counts interviews with generated feedback that hasn't been marked as reviewed.

Note:

• Filtered by feedback generation date. Also available as a monthly trend (Trend in Pending Feedback Reviews).

Time to Action and Experience Metrics by Recruiter

Definition: A per-recruiter view of AI Interview volume, feedback management and candidate satisfaction, so TA leaders can monitor engagement with AI Interview workflows and identify coaching opportunities. Combines throughput columns (invites, completions), backlog columns (reviewed, pending, average review time) and an experience column (candidate satisfaction) against a single recruiter dimension.

How it is calculated: For each recruiter: total invites sent, interviews completed, feedback reviewed, pending feedback reviews, average feedback review time, and average candidate satisfaction score.

Note:

• Unassigned recruiters are excluded. Filtered by invitation sent date, applied to every column in the table. A parallel view exists broken down by sourcer (Time to Action and Experience Metrics by Sourcer).

Time to Action and Experience Metrics by Sourcer

Definition: The sourcer-level counterpart to Time to Action and Experience Metrics by Recruiter, giving a per-sourcer view of AI Interview volume, feedback management and candidate satisfaction so TA leaders can monitor sourcer engagement and identify coaching opportunities. The aggregations, date filter and column set are identical to the recruiter table; only the grouping dimension differs.

How it is calculated: For each sourcer: total invites sent, interviews completed, feedback reviewed, pending feedback reviews, average feedback review time, and average candidate satisfaction score.

Note:

• Unassigned sourcers are excluded. Filtered by invitation sent date, applied to every column in the table.

Overdue Interviews

Definition: Overdue Interviews measures the count of AI Interview invites that have expired without the candidate completing the interview. It identifies candidate drop-off at the AI screening stage so teams can take timely follow-up action.

How it is calculated: Counts sent invites where the invitation link has expired and the interview hasn't been completed.

Note:

• Filtered by invitation sent date.

Interviews with Call Initiation Error

Definition: Interviews with Call Initiation Error measures the count of AI Interviews where the candidate attempted to start the call but could not successfully connect. It identifies technical issues in the interview launch path and helps prioritise infrastructure or UX fixes.

How it is calculated: Counts interviews where a call was initiated but never actually started.

Note:

• Filtered by call-initiated date — the only widget on this dashboard filtered by this column.

Interviews with Retry/Rejoin

Definition: Interviews with Retry/Rejoin measures the count of completed AI Interviews where the candidate had at least one retry or rejoin attempt during the session. It monitors technical disruption and candidate hesitation during AI screening.

How it is calculated: Counts completed interviews with more than one call session recorded.

Note:

• Filtered by completion date.

Retry/Rejoin Rate (%)

Definition: Retry/Rejoin Rate measures the percentage of completed AI Interviews that involved at least one retry or rejoin. It gauges how frequently technical disruption or candidate re-entry occurs during AI screening.

How it is calculated: Divides completed interviews with more than one call session by all completed interviews.

Note:

• Filtered by completion date.

Completion Rate Post Retry (%)

Definition: Completion Rate Post Retry measures the percentage of AI Interviews that were successfully completed after the candidate attempted at least one retry or rejoin. It shows how often candidates who hit a disruption still finish — a direct read on the resilience of the AI Interview experience. Only interviews with at least one retry or rejoin attempt are included in either side of the ratio.

How it is calculated: Divides interviews that had a retry/rejoin and were still completed, by all interviews that had a retry/rejoin.

Human Assistance Requested

Definition: Human Assistance Requested counts the total number of AI interview sessions where a candidate triggered a request for help or support. It identifies areas of candidate confusion or technical friction within the interview flow.

How it is calculated: Counts interview sessions with a recorded assistance-request event.

Note:

• Filtered by invitation sent date. High counts may indicate unclear instructions or technical issues.

Human Assistance Requested By Reason

Definition: Human Assistance Requested by Reason breaks the assistance-request count down by the reason recorded against the request, turning the headline count into something actionable by showing which category of friction is driving contact.

How it is calculated: Counts interview sessions with a recorded assistance-request event, grouped by assistance reason.

Note:

• Filtered by invitation sent date. A session is counted once regardless of how many times help was requested during it.

Feedback Turnaround Time

Definition: Feedback Turnaround Time measures the average duration, in minutes, between a candidate completing their AI interview and the AI feedback being generated. It monitors system performance and confirms recruiters receive timely insights for evaluation.

How it is calculated: Averages the time between interview completion and feedback generation, across interviews where feedback was generated.

Note:

• Filtered by completion date.

Interview Count by Feedback Turnaround Time Bucket

Definition: Interview Count by Feedback Turnaround Time Bucket categorizes completed interviews into time-based groups according to how long the AI took to generate feedback. Using buckets instead of a single average gives an accurate distribution of system performance that isn't distorted by extreme outliers. Buckets: Within 5 mins, 5–15 mins, 15–30 mins, 30–60 mins, 1–4 hours, 4+ hours.

How it is calculated: Counts interviews per feedback-turnaround-time bucket.

Note:

• Only interviews with feedback generated are included. Filtered by completion date.

Feedback Generation Rate (%)

Definition: Feedback Generation Rate (%) measures the percentage of completed AI interviews that resulted in successfully generated feedback within the selected range. It monitors the reliability and coverage of the AI evaluation process and confirms recruiters have the data they need to assess candidates.

How it is calculated: Divides completed interviews that also produced generated feedback by all completed interviews.

Note:

• Filtered by stage timestamp.

AI Interview Funnel

Definition: AI Interview Funnel visualizes the candidate journey through the stages of the AI interview process — Invited, Call Started, Completed, Feedback Generated, Feedback Reviewed, and Human Assistance Requested. It identifies drop-off points, showing where candidates disengage or where technical friction occurs during the evaluation period. Including assistance requests as a stage lets teams see support volume relative to the whole candidate flow.

How it is calculated: Counts distinct candidates reaching each stage.

Note:

• Filtered by stage timestamp.

Total Credits Purchased - All Time

Definition: Total Credits Purchased shows the total number of AI Interviewer credits purchased by the customer across all plans — past, present and future, including plans whose start date has not yet arrived. It's a static snapshot of the overall credit budget allocated, tracked alongside credits used and current balance to help plan future purchases and avoid interruption.

How it is calculated: Reads the tenant's total purchased credits, summed across all plans.

Note:

• No date filter — includes past, present, and future-dated plans.

Total Credits Consumed - All Time

Definition: AI Interview Credits Used - All Time shows cumulative consumption of interview credits from the start of AI Interview usage through today. It's a snapshot of overall credit utilisation and the basis for budget tracking and resource planning.

How it is calculated: Sums credits used across all interview sessions to date.

Note:

• No date filter — reflects all-time cumulative usage.

Total Credit Consumption % - All Time

Definition: Total Credit Consumption % shows the percentage of purchased AI Interviewer credits that have been consumed. It's the quickest read on how much of the allocated budget has been used and how much capacity remains — monitored alongside credits purchased and used to assess burn rate and plan additional purchases.

How it is calculated: Divides all-time credits used by total credits purchased.

Note:

• No date filter — reflects all-time consumption against the total purchased. A value of 100% indicates full consumption of the purchased budget.

Weekly Trend in Credit Consumption

Definition: Weekly Trend in Credit Consumption shows the total AI Interviewer credits consumed each week through completed interviews. It tracks usage patterns over time and highlights weeks with unusually high or low consumption.

How it is calculated: Sums credits used per calendar week, based on interview completion date.

Note:

• Filtered by completion date.

Monthly Trend in Credit Consumption

Definition: Monthly Trend in Credit Consumption shows the total AI Interviewer credits consumed each month through completed interviews. It's the longer-horizon companion to the weekly trend, better suited to quarterly and annual budget forecasting.

How it is calculated: Sums credits used per calendar month, based on interview completion date.

Note:

• Filtered by completion date.

Credits Share — Multi vs Single Section (All Time)

Definition: Credits Share — Multi vs Single Section shows what portion of total credits consumed, across all time, comes from interviews that used more than one live section versus exactly one. It helps TA teams understand where credit spend is concentrated and plan budget allocation between guide types. The tiering here is finer than the Adoption tab's donut — it separates avatar from non-avatar interviews, giving four categories instead of two.

How it is calculated: Sums billed credits for completed modular interviews, grouped by billed credit tier (with-avatar vs without-avatar, multi- vs single-section).

Note:

• No date filter — a lifetime view meant to show overall spend concentration rather than a trend.

Plans Purchased Data

Definition: Plans Purchased Data shows a row-level breakdown of all AI Interviewer credit plans purchased by the customer, including plan details such as opportunity name, product type, purchase date, plan start and end dates, and total credits allocated per plan. This table provides full transparency into the customer's credit purchase history and is useful for auditing plan configurations, verifying contract details, and understanding how total purchased credits are distributed across multiple plans.

How it is calculated: Displays each purchased plan as its own row, along with its total credit allocation.

Note:

• Each row represents one purchased credit plan — no aggregation across plans. No date filter — shows all purchased plans.