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Blog›HR / People

Find Where Your Recruiting Funnel Breaks

Your ATS has the data. What it lacks is the ability to answer 'which source produces the best hires at the lowest cost' without a BI team and a two-week wait.

Try AnalityQa AI AI free →See live examples
HR team meeting

The problem

  • →Stage conversion rates vary sharply by role, level, and source, but most ATS reporting shows only aggregate pass-through rates that hide the real bottlenecks.
  • →Time-to-hire figures are reported as a single average that obscures the difference between a 10-day offer for a junior role and a 90-day search for a senior specialist.
  • →Offer-accept rates drop unpredictably at the end of the funnel, but connecting that to source, recruiter, or compensation range requires joining data that rarely lives in one place.
  • →Sourcing spend is allocated by volume of applicants rather than quality of hires, because downstream conversion data is too hard to pull back to the source level.

Why the usual approach breaks down

ATS exports flatten a multi-stage process into awkward row formats

Greenhouse, Lever, and Workable exports often produce one row per application event, not one row per candidate. Pivoting these into a proper funnel with stage-by-stage conversion rates requires reshaping the data before any analysis is possible.

Source attribution is inconsistently recorded

Recruiters apply sources manually, leading to dozens of variants — 'LinkedIn', 'linkedin', 'LI Recruiter', 'LinkedIn Referral' — that collapse into noise unless cleaned. Automated cleaning rules break down when new source names are added without a controlled vocabulary.

Time-to-hire involves multiple date columns that must be handled carefully

Correct time-to-hire requires the right start date (job opening, first application, first screen) and end date (offer accepted, start date). Different definitions produce numbers that vary by weeks, and mixing them in a single average produces a figure that nobody trusts.

Connecting offer outcomes back to source requires a join most teams cannot perform

Offer-accept rates per source require linking the application record to the offer record to the hire outcome — three tables in most ATS schemas. Without SQL access or a data team, this analysis simply does not happen.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload your ATS export and get a funnel in one query

Paste a CSV from Greenhouse, Lever, Workable, or any other ATS. AnalityQa AI AI identifies stage columns, date fields, and candidate IDs, then builds a stage-by-stage conversion funnel without any configuration from you.

02

Source yield analysis in natural language

Ask 'which sources produce the highest offer-accept rate for engineering roles' and get a ranked table that joins application source to offer outcome automatically, with sample sizes so you can judge statistical reliability.

03

Time-to-hire breakdowns by role, level, and recruiter

Specify the start and end dates you want — 'from job opening to offer accepted' — and AnalityQa AI AI applies that definition consistently across every slice you ask for. Median, mean, and percentile breakdowns are all available.

04

Auto-join across multiple ATS files or database tables

Upload your applications file alongside your offers file and a headcount plan and AnalityQa AI AI matches them on candidate ID or email, then makes all three available in the same query session.

05

Scheduled funnel dashboards for weekly pipeline reviews

Set a recurring refresh and have a live funnel snapshot waiting in everyone's inbox before the Monday pipeline review — no one has to pull it manually.

You askedGenerated in 4.2s

"Show me stage-by-stage conversion rates for all roles opened in Q1 2026, broken down by department."

Headcount

284+12

Attrition

8.1%−1.4pp

Time to hire

31d−4d

Funnel chart: stage conversion rates by department, Q1 2026

Last 12 mo

Bar chart: median time-to-hire by role level, last 6 months

Segment ASegment BSegment CSegment DSegment ESegment F

Ranked table: offer-accept rate by source channel, Sales roles

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Show me stage-by-stage conversion rates for all roles opened in Q1 2026, broken down by department.

AI

AnalityQa AI AI pivots the application event rows into a funnel, calculates pass-through rates at each stage, and segments the result by department.

Funnel chart: stage conversion rates by department, Q1 2026
You

What is median time-to-hire by role level for the last 6 months? Use job opening date to offer-accepted date.

AI

It applies the specified date definition, computes median days per role level, and flags levels where time-to-hire has increased more than 20% quarter-over-quarter.

Bar chart: median time-to-hire by role level, last 6 months
You

Which sourcing channels have the highest offer-accept rate for Sales roles?

AI

AnalityQa AI AI joins application source to offer outcome, filters for Sales, calculates accept rate per channel, and ranks them with confidence intervals based on sample size.

Ranked table: offer-accept rate by source channel, Sales roles
You

Show me recruiter-level pipeline velocity — average days per stage — for open requisitions.

AI

It calculates the average days candidates spend at each stage per recruiter for currently open reqs and highlights where candidates are stalling.

Heatmap: avg days per stage by recruiter, open requisitions
You

Are offer-accept rates trending up or down over the last four quarters?

AI

AnalityQa AI AI groups offer outcomes by quarter, computes the accept rate for each, and plots the trend with a regression line and the percentage-point change quarter-over-quarter.

Line chart: offer-accept rate trend, last 4 quarters

What teams get out of it

✓Recruiting teams identify their highest-converting source channels without any SQL or BI support.
✓Time-to-hire breakdowns by role level expose specific bottleneck stages that managers can act on in the same week.
✓Offer-accept rate monitoring catches compensation competitiveness issues before they drain a pipeline.
✓Weekly automated funnel reports replace a recurring manual data pull estimated at 2–3 hours per week.

Frequently asked questions

Which ATS platforms does AnalityQa AI AI support?+

Any ATS that can export a CSV or connect via a PostgreSQL/MySQL database is supported. This includes Greenhouse, Lever, Workable, Teamtailor, SmartRecruiters, and others. There is no native ATS integration required.

How does it handle messy source naming from manual recruiter input?+

AnalityQa AI AI groups visually similar strings automatically — variants like 'LinkedIn', 'linkedin.com', and 'LI Recruiter' are merged before analysis. You can review and adjust the groupings before any report is finalised.

Can it calculate cost-per-hire if I supply sourcing spend data?+

Yes. Upload a spend file alongside your ATS export and ask for cost-per-hire by source or cost-per-qualified-applicant. AnalityQa AI AI joins on the source name and computes the metric at whatever level of granularity the data supports.

Is candidate data safe?+

All data is encrypted at rest and in transit, isolated per tenant, and is never used for model training. Candidate records can be deleted on request.

Can I analyse diversity metrics across funnel stages?+

If your ATS export includes self-reported demographic fields, you can ask for conversion rates broken down by any of those dimensions. AnalityQa AI AI does not infer demographic attributes from names or photos.

What if my pipeline data is split across two systems — one ATS for sourcing and one HRIS for offer outcomes?+

Upload both exports and AnalityQa AI AI will join them on a common key — typically candidate email or employee ID — and make the combined dataset available in the same session.

How much does a scheduled funnel dashboard cost?+

Scheduled dashboards are available on Pro and Business plans. Ad-hoc funnel analysis on uploaded files is available on the free tier with no query limits.

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