Job application analytics report
The deepest report in Nextal — five sections covering volume, pipeline flow, source attribution, decline/deny reasons, and process productivity.
Every chart on this page respects the date filter at the top right (Last 365 days / Last 30 days / Last 7 days / custom range). KPI cards that mention a fixed window like “365d” keep their own window regardless — they’re always-on benchmarks. Click Export PDF at the top to download the current view.
Section 1 — Volume & trends
Tracks application volume over time, the same way the Candidate report tracks candidate creation.
KPI cards: Total / 365d / 30d / 7d
Application counts in each time window. Comparing 30d vs 7d × 4 gives you a sanity check on whether the current week is on pace.
Chart: Applications per month (line)
What it shows. Monthly application volume as a line chart. Smoother than a bar chart for spotting trends.
How to read it. Look for slope (growing/declining) and seasonality (e.g. every January spike).
Chart: Applications per day (bar)
What it shows. One bar per day of the recent period.
How to read it. Weekday-vs-weekend pattern is normal. A flat zero on a weekday is unusual — check if your career page is up.
Section 2 — Pipeline & flow
Where applicants are right now, how they move between stages, and the conversion rate of each stage.
Widget: Recruitment funnel
What it shows. A two-part widget: (1) a counter grid showing the current count at every workflow stage, (2) a horizontal bar funnel where each bar’s length is the share of the original “New” applicants who reached that stage. Stage names come live from your workflow configuration — never hardcoded.
Question it answers. “Where are applicants dropping off?”
How to read it. A steep drop between two adjacent stages (e.g. New → Phone screen) points to a bottleneck. A funnel that’s wider at the bottom than it should be is suspicious — usually a data issue.
Chart: Status transitions
What it shows. A bar chart of how many applications moved from each “From” stage to each “To” stage during the date range. Stage names are dynamic from the backend.
Question it answers. “What’s the actual flow this period — and where do candidates skip stages or jump backwards?”
Table: Conversion rates
What it shows. A table with one row per stage pair (From → To). Columns: applicants who moved, applicants who didn’t, conversion rate %.
How to read it. Low conversion in your first stage (New → Screened) is normal. Low conversion at the offer stage is a red flag — it means candidates are declining offers, which is much more expensive than declining earlier.
Section 3 — Source & sponsorship
Where applications come from, and whether paid (sponsored) sources outperform organic ones.
Sponsorship dashboard
What it shows. Four KPI cards (Sponsored apps / Non-sponsored apps / Sponsored hires / Hire rate by sponsorship) and two stacked bar charts (apps by source and hires by source, each split into sponsored vs non-sponsored slices). See the Sponsorship performance report for the full standalone view.
How to read it. Comparing the four KPIs gives you cost-per-hire intuition: if sponsored hires are 80% of total but sponsored apps are only 30%, paid sources convert better. If apps are 80% sponsored but hires are 50% sponsored, you’re overpaying.
Chart: Job applications by source
Bar chart, one bar per source, length = number of applications.
Chart: Hired by source
Bar chart, one bar per source, length = number of hired applications from that source. The hire-rate sanity-checked against this lets you spot high-volume / low-quality channels.
Section 4 — Outcomes & reasons
When applications close (not hired), why?
Chart: Decline reasons
What it shows. Bar chart of candidate-initiated closures by reason (e.g. “Salary mismatch”, “Took another offer”, “Withdrew”).
Why it matters. Patterns here are actionable — if “Salary mismatch” is your top reason, you have a comp problem before you have a sourcing problem.
Chart: Deny reasons
What it shows. Bar chart of recruiter-initiated closures by reason (e.g. “Skills mismatch”, “Background check failed”, “Not enough experience”).
Why it matters. Top reasons reveal where your sourcing screens need to be tighter.
Section 5 — Productivity & process
How efficient is the team at moving applications through?
KPI row: Total / Processing time / Hire rate / Interview conv.
What it shows. Four headline KPIs covering volume, speed and quality.
- Total applications — total processed in the range; context anchor for the others.
- Average processing time — mean days an application spends between “New” and final closure.
- Hire rate — percentage of closed applications that ended in a hire. Single most important quality metric.
- Interview conversion — percentage of applications that reached an interview stage. Measures sourcing quality + screening efficiency.
Chart: Interview types distribution
What it shows. A donut. Each slice is one interview type (Phone / Video / In-person), sized by how many of that type happened in the range.
How to read it. A heavy phone-screen slice with a thin in-person slice suggests strong top-funnel filtering. The opposite suggests you’re bringing too many people in person without enough pre-qualification.
Chart: Job application per month (analytics)
A second per-month chart sourced from the analytics summary (different shape than Section 1’s line — kept for compatibility with the legacy report).