The 4Spot Consulting team built a four-scenario Make.com pipeline connecting Greenhouse, LinkedIn Recruiter, and a Google Sheets cost tracker into a single Airtable executive dashboard, updated automatically every Monday morning. The result: real-time visibility into cost-per-hire, time-to-fill, and source effectiveness, replacing monthly manual reporting that previously consumed four to six hours.

What recruiting metrics problem existed before the pipeline was built?

The team managed recruiting for multiple clients simultaneously, pulling metrics from Greenhouse ATS, LinkedIn Recruiter, and a Google Sheets cost tracker. Monthly recruiting reports took four to six hours to compile manually – aggregating data from three systems, calculating derived metrics, and formatting the final document. Reports were accurate on the day they were produced and stale within the week. Leadership made decisions based on 30-day-old data in a market that changed week to week.

The core problem was not missing data – it was inaccessible data. A metrics pipeline was not a reporting luxury. It was the infrastructure required to make data-driven recruiting decisions at the speed recruiting actually demands.

How was the Make.com pipeline built to pull data automatically?

Three Make.com™ scenarios run every Sunday at 10 p.m. The first pulls Greenhouse job and candidate data via API, writing stage counts, time-in-stage, and offer data to a dedicated Airtable table. The second pulls LinkedIn Recruiter analytics via a Make.com HTTP module, extracting InMail response rates and source attribution data. The third reads the Google Sheets cost tracker and writes spend data to Airtable by requisition.

A fourth scenario runs Monday at 6 a.m., joining the three data tables in Airtable, calculating derived metrics – cost-per-hire by source, time-to-fill by department, and pipeline conversion rates by stage – then updating the executive dashboard view. By 8 a.m. Monday, leadership sees last week’s complete recruiting metrics without anyone on the team working the weekend.

Expert Take

The four hours we reclaimed from monthly report compilation went directly into analysis – asking why the metrics looked the way they did and what to do about it. That is the leverage automation creates: it converts data production time into data interpretation time, which is the part that actually changes outcomes.

What insights changed recruiting decisions after the pipeline launched?

Three insights emerged within the first 60 days that changed active decisions. Source attribution data showed LinkedIn InMail producing three times the qualified-to-offer rate of job board applications at twice the cost – a cost-per-qualified-candidate calculation that justified reallocating 40% of the job board budget to LinkedIn. Stage conversion analysis identified a 68% drop-off between phone screen and hiring manager interview – a scheduling delay problem, not a candidate quality problem. Time-in-stage data showed one department averaging 22 days in the offer approval stage, traced to a single approver bottleneck that a delegation policy change resolved.

None of these insights required sophisticated analytics. They required consistent, timely data. The pipeline provided the data; the team provided the interpretation.

Key Takeaways

• Four Make.com scenarios pull weekly data from Greenhouse, LinkedIn, and Google Sheets automatically into Airtable.
• A weekly derived-metrics calculation scenario produces a live executive dashboard by 8 a.m. Monday – no manual work required.
• Four hours of monthly report time converted to analysis time, changing three active recruiting decisions within 60 days.
• Source attribution, stage conversion, and time-in-stage data produced the highest-impact recruiting insights.

Recruiting Metrics Pipeline FAQ

Does this pipeline work with ATS platforms other than Greenhouse?

Yes. Lever, iCIMS, SmartRecruiters, and Workday all offer APIs that Make.com can connect to via native connectors or HTTP modules. The pipeline architecture is platform-agnostic – the specific API endpoints and field names change per ATS, but the four-scenario structure transfers directly.

How do you handle data discrepancies when the same metric appears in multiple source systems?

Designate a single source of truth for each metric before building the pipeline. Time-to-fill comes from the ATS; cost data comes from the finance tracker; source data comes from LinkedIn and the ATS combined. Document the source-of-truth decision for each metric so everyone references the same number.

What Airtable plan supports this level of data automation?

Airtable’s Team plan supports the API connections and automation runs this pipeline requires. Organizations tracking more than 50 active requisitions simultaneously benefit from the Pro plan’s advanced filtering and expanded record limits. Check Airtable’s current pricing page for the latest plan details.

For a closer look at how automation investments produce measurable returns in talent acquisition, see 10 essential metrics for AI talent acquisition ROI.