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Job reports

Analytics dashboard for one specific job — application volume, pipeline funnel, source breakdowns, time-to-fill. Date-filtered, with a PDF export.

Where to find it
Job detail → Reports tab
Who can use it
Anyone with access to jobs.

The Reports tab on a job profile is a focused analytics dashboard for that one job. Five sections of KPIs and charts cover everything from application volume to where your traffic actually comes from. Most charts react to the date filter at the top; visitor analytics is the only section that stays all-time.

The full Reports tab for one job. Date filter at the top; five sections of charts cover everything from volume to visitor analytics.

The date filter

The date filter. Three quick buttons or a custom range. Filter persists in the URL.

Quick-range buttons at the top: Last 7 days, Last 30 days, Last 90 days. The active button is highlighted in blue. Default when you open the tab: Last 30 days.

For a different range, use the calendar picker right next to the buttons — pick a start and an end date. Whatever you set is stored in the URL, so you can copy and share with a teammate to see the same numbers.

Export the whole tab as a PDF

An Export PDF button at the top right captures the entire Reports tab — every chart, every KPI, every visualization — into a single PDF. Useful when you’re briefing a hiring manager and want to share the data without giving them Nextal access.

Section 1 — Volume & trends

Four KPI cards at the top, two trend charts below.

How many people are applying, and when.

The four KPI cards

KPI

What it counts

Total applications

Every application ever received for this job, no date filter.

Last 365 days

Applications in the past year.

Last 30 days

Applications in the past month.

Last 7 days

Applications in the past week.

Chart — Applications per month

Monthly trend. Spot seasonality and the impact of recruiting campaigns.

A line chart with one data point per month inside your selected date range. Each point is the number of applications received that month.

What to look for: seasonality (Septembers are often spikes), the impact of a sponsored boost, or a sudden drop that may indicate a sourcing problem.

Chart — Applications per day

Daily trend. Useful for spotting the impact of single events — a job board post or a referral campaign.

Same idea but at the day level. One point per day inside your selected date range.

What to look for: the day-by-day rhythm. Mondays and Tuesdays usually peak; weekends usually dip. A single big spike one day suggests something specific happened (a syndication push, a viral post).

Section 2 — Pipeline & flow

How candidates move through your workflow. The funnel shows drop-off at each step; the flow shows transitions.

What happens to applications once they’re in.

Chart — Recruitment funnel

The funnel. The width of each bar shows how many applications reached that step.

A classic funnel showing every step in your application workflow with the count of applications that reached it. The wider the bar, the more applications got there.

What to look for: the biggest drop-off step. If 100 candidates apply but only 20 reach the phone screen, your screening process may be too strict — or your applications are low quality. Either is actionable.

Chart — Status flow charts

Status transitions. Where do candidates actually go between statuses — and how many get rejected at each step?

A flow chart showing how applications transition between statuses over time. Each line connects two statuses; thicker lines = more applications taking that path.

What to look for: unexpected paths. Most flows should go forward — apply → screen → interview → offer → hire. If you see a lot of candidates jumping back to earlier statuses, that suggests a messy process.

Section 3 — Source & sponsorship

Where applications come from vs. where actual hires come from. The mismatch is often more informative than the numbers themselves.

The most useful section for marketing spend decisions.

Chart — Applications by source

Where applications came from. Career page and LinkedIn usually dominate.

One bar per source, showing how many applications came from that source.

Sources include your career page, partner job boards (LinkedIn, Indeed, Jobillico, etc.), referrals, sponsorships, manual imports — anywhere a candidate could enter the system.

What to look for: the long-tail sources you can probably stop spending on, and the unexpected leaders worth doubling down on.

Chart — Hires by source

Hires by source. Compare with the applications chart above — the mismatch tells you where your money goes vs. where your hires come from.

Same chart shape, but counting hires instead of applications. The interesting comparison is between this chart and the previous one.

What to look for: if LinkedIn brings 60% of applications but only 20% of hires, you may be paying to attract the wrong fit. Conversely, if employee referrals are 5% of applications but 30% of hires, the referral program is your highest-ROI channel and deserves more investment.

Sponsorship sub-dashboard

The sponsorship dashboard for this client, reused from the global sponsorship report.

A reused dashboard showing per-client sponsorship campaign performance: cost per click, cost per application, cost per hire (where available). Honest ROI for paid acquisition.

Section 4 — Outcomes & reasons

Why candidates declined offers, and why your team rejected candidates.

Why deals fell through — both candidate-side and team-side. The most useful section for fixing your own process.

Chart — Decline reasons

Why candidates declined your offers. Patterns here suggest where to negotiate or pivot.

One bar per reason, counting how many candidates declined for that reason.

What to look for: if salary is the top reason, you may need to negotiate the band up with the client. If location is the top reason, consider re-listing the role as remote where possible. Each reason has a specific fix.

Chart — Deny reasons

Why your team rejected candidates. Patterns reveal job-description problems and sourcing problems.

One bar per reason, counting how many candidates your team rejected for that reason.

What to look for: if “skills mismatch” is huge, your sourcing is hitting the wrong candidates — sharpen your job description, change the syndication. If “experience too low” dominates, your screening question may not be filtering early enough.

Section 5 — Visitor analytics

Who visited the public job page — and how they found it. Visitor data is all-time, unlike the other sections.

Who actually visited the public job page — and how they got there. Important: this section shows all-time data, regardless of the date filter at the top of the tab.

The eight visitor KPI cards

The first row counts page visits (one count per page load):

  • Visitors total
  • Last 365 days
  • Last 30 days
  • Last 7 days

The second row counts unique visitors (deduplicated by browser fingerprint or session):

  • Unique total
  • Unique last 365 days
  • Unique last 30 days
  • Unique last 7 days

The gap between “visits” and “unique” tells you how often the same person comes back — a high ratio means strong interest, a low ratio means lots of one-off views.

Chart — Visitors by country

Geographic distribution at the country level.

One bar per country. Useful for international roles — see if the people interested in the job are actually in countries the role accepts.

Chart — Visitors by region

Provincial / state-level breakdown.

One bar per region (province / state). More granular than country, less than city.

Chart — Visitors by city

City-level breakdown. Most useful for in-person roles in a specific location.

One bar per city. Most useful for in-person roles where you want to know if the right city is reading the listing.

Chart — Visitors by source

How visitors found the page. Direct = bookmarks or shared links. Organic = search engines. Referral = links from other sites.

One bar per traffic source: direct (typed URL or bookmark), organic search, social media, referrals from other sites, etc.

What to look for: a heavy direct traffic share usually means recruiters and candidates are sharing the URL by word of mouth. A heavy organic share means your SEO is working. A heavy referral share means a partner site (a community, a forum, a job board) is doing well for you.

Who can do what

What you want to do

What you need

View the Reports tab

Access to jobs and reports

Change the date filter

Same

Export to PDF

Same

Tips

  • Use the URL to share snapshots. Custom date range, copy URL, send — your teammate sees the same numbers.
  • Export to PDF for hiring-manager briefings. Takes a few seconds; the result is ready to email.
  • Compare applications-by-source with hires-by-source. The most actionable single insight on this page.
  • Watch the funnel for process problems. Consistent drop at the same step = something’s wrong upstream of that step.
  • Visitor analytics is all-time. The date filter doesn’t touch this section. If you need a date-filtered visitor view, that’s a feature request, not a setting.