What Hiring Metrics Your Resume Screening Dashboard Should Track

Here's the uncomfortable truth: most recruiting teams are flying blind. The average time-to-hire is 36-44 days, but without a proper dashboard, you have no idea if you're faster or slower than that. Quality of hire is the #1 recruiting priority for 2025, yet most companies can't actually measure it. You're screening hundreds of resumes, but do you know your applicant-to-interview conversion rate? (Spoiler: the industry average is 3%—97% of applicants don't even make it to an interview.) Your interview-to-hire rate is probably around 27%, meaning you're burning time interviewing candidates who won't get offers. And cost-per-hire? That's averaging $4,700 globally, but companies with strong employer brands cut it in half. You need a recruiting dashboard that shows you: where your best candidates come from, where they're dropping off in your funnel, how long screening actually takes (not how long you think it takes), and whether your hires are any good after 90 days. Let's break down the exact metrics your screening dashboard needs to track.

Why do you need a resume screening dashboard in the first place?

Because gut feeling and anecdotal evidence don't scale—and they definitely don't help you improve.

Here's what happens without a dashboard:

You can't identify bottlenecks. You think screening is fast, but candidates are actually sitting in your pipeline for 2 weeks before anyone reviews their resume. You think interviews are the slow part, but actually, it's the 10 days between resume submission and initial screening that's killing you. Without data, you're guessing. With a dashboard, you see exactly where time is being wasted—and where to focus your improvements.

You don't know which sourcing channels work. You're spending $5,000/month on LinkedIn Recruiter, but 80% of your actual hires come from employee referrals and your careers page. Or maybe you think referrals are your best source, but they actually have terrible retention rates compared to candidates from industry job boards. Source-of-hire tracking shows you where to invest recruiting budget and where to cut waste.

You can't prove ROI on recruiting investments. Your CFO asks "Was that ATS worth $10K/year?" and you say "Uh, I think so?" A dashboard shows: time-to-hire dropped 15 days, cost-per-hire decreased by $800, recruiter productivity increased (more hires per recruiter). Now you have data to justify (or kill) recruiting tools and vendors.

You repeat the same mistakes. Last quarter, you screened 500 candidates for a role that required SQL skills, but 400 didn't actually have SQL experience. Why? Because your job posting was vague, and you didn't pre-screen for technical skills. A dashboard would show you: 80% rejection rate at screening stage, primary rejection reason: missing required skill. You'd fix the posting or add a knockout question, instead of manually rejecting 400 unqualified people every time.

You don't know if your hires are any good. You filled 10 positions last quarter. How many of those people are still employed 90 days later? How many are meeting performance expectations? If you're not tracking quality-of-hire metrics, you have no idea if you're hiring well or just hiring fast. Speed without quality is just expensive turnover.

The research backs this up: 86% of businesses using an ATS (with built-in dashboards) reported reduced time-to-hire. AI-driven recruiting dashboards cut time-to-hire by 35%. Teams that track recruiting metrics make better hiring decisions, hold stakeholders accountable, and actually improve over time. Without metrics, you're stuck doing the same inefficient processes forever.

Bottom line: A recruiting dashboard isn't a nice-to-have for data nerds. It's how you spot problems, optimize processes, justify budgets, and prove that recruiting is driving business value—not just filling seats.

What's the difference between screening metrics and overall hiring metrics?

Screening metrics zoom in on the top of your funnel—the resume review stage specifically—while overall hiring metrics track the entire candidate journey.

Screening metrics (top-of-funnel): These measure how efficiently you're processing applications and identifying qualified candidates: number of applications received, time from application to initial screen, number of resumes screened per day/week, screening-to-interview conversion rate (what % of screened candidates advance), primary rejection reasons during screening, cost per screen (if using paid tools), screener productivity (resumes reviewed per hour). These metrics tell you if your screening process is fast, accurate, and efficient.

Overall hiring metrics (full-funnel): These track the complete hiring process from job posting to offer acceptance: time-to-hire (application to offer acceptance), time-to-fill (job posting to offer acceptance), applicant-to-hire ratio, cost-per-hire, offer acceptance rate, quality of hire (90-day retention, performance ratings, manager satisfaction), source of hire (which channels produce the best hires). These tell you if your entire recruiting process is working.

Why you need both: Let's say your overall time-to-hire is 50 days, and you want to improve it. Without screening metrics, you don't know if the problem is: slow initial screening (10 days to review resumes), interview scheduling delays (2 weeks to book first interview), lengthy interview processes (multiple rounds taking a month), or slow offer approvals (executives taking weeks to decide). Screening metrics pinpoint where the bottleneck actually is. You might discover that screening only takes 3 days, but interview scheduling takes 14 days. Now you know where to focus.

Your dashboard should track both: A section for screening-specific metrics (how's your top-of-funnel performing?), and a section for end-to-end metrics (how's the whole hiring process doing?). This gives you a complete picture: are you screening efficiently, and are you ultimately making good hires?

Example: Your screening metrics look great: 2-day time-to-screen, 10% screening-to-interview conversion rate (above the 3% industry average), clear rejection reasons documented. But your overall hiring metrics show: 60-day time-to-hire (too slow), 30% offer decline rate (candidates dropping out), 50% of hires leave within 6 months (terrible retention). The problem isn't screening—it's your interview process, offer competitiveness, or onboarding. The dashboard helps you diagnose precisely where things are breaking.

What are the must-have screening efficiency metrics?

These metrics tell you if your screening process is fast, consistent, and productive:

Metric #1: Time-to-screen (application to initial screening decision)

This is the time from when a candidate applies to when someone makes an initial screening decision (advance to interview or reject). Industry reality: candidates expect response within 1-2 weeks, but many companies take 2-4 weeks just for initial screening. Target: 2-5 days maximum. If you're taking longer, top candidates are accepting other offers before you even review their resume. Track this weekly. If it's creeping up, you have a capacity problem (too many applications, not enough screeners) or a process problem (manual review is too slow—time to automate).

Metric #2: Number of resumes screened per recruiter/day

How many resumes can one person screen in a day? Manual screening: 20-40 resumes per hour (if experienced). AI-assisted screening: 200-500 resumes per day per recruiter (AI handles initial pass, recruiter reviews flagged candidates). Why it matters: if you're getting 300 applications per role and each recruiter can only manually screen 200 resumes per week, you're backlogged. This metric helps you determine if you need more recruiters, better tools, or both. Track productivity per recruiter. If someone is consistently slower, they might need training—or they might be spending too much time on candidates who should have been auto-rejected.

Metric #3: Screening-to-interview conversion rate

What percentage of candidates who pass initial resume screening get invited to interview? Industry benchmark: 3% (out of 100 applicants, 3 get interviewed). If yours is much lower (1%), you're either getting terrible applicants or your job posting is attracting the wrong people. If it's much higher (10%+), you might be screening too loosely—interviewing unqualified candidates wastes everyone's time. Target: 5-8% is healthy. It means your screening is selective but not overly harsh. Track this by role and recruiter to ensure consistency.

Metric #4: Primary rejection reasons at screening stage

Why are you rejecting candidates during screening? Track categories: lacks required experience (e.g., "needs 5 years, has 2"), missing required skills (e.g., no coding experience for developer role), location mismatch (remote role, they require relocation), salary expectations too high, incomplete or poorly formatted application. Why it matters: if 60% of rejections are "missing required skill," your job posting isn't clear about requirements. Add a knockout question. If rejections are spread evenly across reasons, your screening criteria are working. If one person rejects 80% for "culture fit" while another rejects for objective reasons, you have an inconsistency problem.

Metric #5: Applicant drop-off rate during screening

How many candidates start an application but don't finish it? Research shows 60% of candidates abandon applications taking over 15 minutes. Track: applications started vs. applications completed. If you have 1,000 starts but only 400 completions, your application process is too long or too complicated. Simplify it. Every field you remove increases completion rate by 5-10%.

Metric #6: Cost per screen

If you're using paid screening tools (AI resume parsers, assessment platforms, background check services), calculate cost per resume screened. Formula: Total screening tool costs per month ÷ Number of resumes screened that month. Example: $500/month for AI screening tool, 1,000 resumes screened = $0.50 per screen. Compare this to manual screening cost (recruiter salary ÷ resumes they can screen). If manual screening costs $2 per resume in recruiter time, and automation costs $0.50, automation saves $1.50 per resume. At 1,000 resumes/month, that's $1,500/month in savings—pays for the tool instantly.

Metric #7: Screener consistency score

If you have multiple people screening resumes, are they using the same standards? Test this by having two screeners review the same batch of 20 resumes. Agreement rate: how often they make the same decision (advance or reject). Target: 80%+ agreement. If it's lower, your screening criteria aren't clear enough, or screeners need calibration training. Inconsistent screening leads to bias, legal risk, and missed talent.

Which quality metrics actually predict good hires?

Speed is useless if you're hiring the wrong people. These metrics measure whether your screening identifies top performers:

Metric #1: Quality of hire (the #1 metric for 2025)

This is the holy grail of recruiting metrics—but also the hardest to measure. Quality of hire = how well new hires perform and contribute to the company. How to measure it: 90-day retention rate (what % of hires stay past 90 days?), 1-year retention rate (long-term success), performance ratings (manager evaluations at 3 months, 6 months, 1 year), time to productivity (how quickly they're fully effective in the role), manager satisfaction surveys ("Would you hire this person again?"). Industry data: AI-powered assessments improve quality-of-hire by 32%. Skills assessments improve it by 36%. Companies focused on quality of hire have better retention, higher employee satisfaction, and lower turnover costs. Track this by source of hire (do LinkedIn candidates perform better than job board candidates?) and by screener (is one recruiter consistently identifying better performers?).

Metric #2: Screening accuracy (false positives vs. false negatives)

False positive: Someone who passed screening but failed in interviews or on the job (you wasted time). False negative: Someone you rejected during screening who would have been a great hire (you missed talent). You can only measure false negatives if you track rejected candidates who get hired elsewhere and succeed—hard to do. But you can track false positives: of candidates who passed screening and got interviewed, what % resulted in hires? If only 5% of screened candidates get hired after interviews, you're passing too many false positives through screening. Target: 20-30% of screened candidates should convert to hires. This means your screening is selective and accurate.

Metric #3: Offer acceptance rate by source

Do candidates from certain sources accept offers more often? Industry average: 70% offer acceptance rate. If candidates from employee referrals accept at 85% but LinkedIn candidates only accept at 50%, referrals are not only a good source—they're more likely to actually join. Use this to prioritize sourcing channels. Low offer acceptance from a source might mean: candidates aren't serious (they're just browsing), your employer brand doesn't resonate with that audience, salary expectations don't align. Either improve your pitch to that audience or stop investing in that channel.

Metric #4: New hire performance ratings at 90 days and 1 year

Have managers rate new hires at 90 days and 1 year: exceeds expectations, meets expectations, below expectations. Track these ratings back to: source of hire (which channels produce top performers?), screener (is one recruiter better at spotting talent?), screening method (did skills assessments predict performance better than resume reviews?). This closes the feedback loop. If 80% of hires from a specific job board are "below expectations" performers, stop using that board. If candidates who completed a skills assessment during screening perform better than those who didn't, make assessments mandatory.

Metric #5: Time to productivity

How long until a new hire is fully effective in their role? This varies by role: entry-level might be 30 days, senior roles might be 90-180 days. Track actual time to productivity (ask managers: "When was this person fully ramped?"). Compare to expected time. If new hires consistently take longer to ramp than expected, you might be: hiring people who are technically qualified but lack specific skills, not assessing for relevant experience during screening, onboarding poorly (not a screening issue, but the data helps diagnose). Candidates who had relevant experience in similar roles ramp faster. If your screening isn't prioritizing relevant experience, this metric will show it.

Metric #6: Diversity of candidate pool vs. diversity of hires

Track demographics at each stage (if legally permissible in your region): applicant pool diversity, screened candidate diversity, interviewed candidate diversity, hired candidate diversity. If your applicant pool is 40% women but only 10% of hires are women, something is filtering them out during screening or interviews. This might indicate: biased screening criteria (e.g., requiring 10 years experience when 5 would suffice—disproportionately filters out women who took career breaks), biased screeners (unconscious bias in resume review), biased interview process. Diversity metrics help you identify and fix these issues. And diverse teams perform better—studies show diverse companies are 35% more likely to outperform competitors.

What source-of-hire metrics should your dashboard show?

Knowing where your best candidates come from helps you allocate recruiting budget and effort effectively:

Metric #1: Applications by source

How many applications are you getting from each source? Track: job boards (Indeed, LinkedIn, industry-specific boards), company careers page, employee referrals, social media (LinkedIn, Twitter, Facebook), recruiting agencies, university/campus recruiting, career fairs and events. Industry data: Job boards produce 60% of applications on average. But volume ≠ quality. Your careers page might generate only 15% of applications but produce 40% of hires. Track volume first, then filter by quality (next metrics).

Metric #2: Source-of-hire (where your actual hires come from)

Of your final hires, what % came from each source? This is the most important source metric. Calculate: (Hires from Source X ÷ Total Hires) × 100. Example: you made 20 hires last quarter. 8 came from employee referrals (40%), 6 from your careers page (30%), 4 from LinkedIn (20%), 2 from Indeed (10%). Now you know where to focus effort. Referrals and careers page are driving 70% of hires—invest there. If you're spending thousands on Indeed but it only produces 10% of hires, reconsider that spend.

Metric #3: Source quality (performance and retention by source)

Some sources produce high volumes but low-quality hires. Others produce fewer candidates but better performers. Track for each source: 90-day retention rate, 1-year retention rate, average performance rating at 90 days, time to productivity. Example: Employee referrals might have 95% retention at 1 year and high performance ratings. Job boards might have 60% retention and mixed performance. Even if job boards produce more hires, referrals are higher quality—worth the investment in a referral bonus program.

Metric #4: Cost per hire by source

How much does each source cost to generate a hire? Formula: Total cost of source ÷ Number of hires from that source. Example: LinkedIn Recruiter costs $10,000/year, produced 15 hires = $667 per hire. Employee referral bonuses cost $5,000 total (10 hires × $500 bonus) = $500 per hire. Company careers page costs ~$0 (already have the website), produced 12 hires = essentially free. Now compare cost per hire against quality. If referrals cost $500 per hire and have 95% retention, that's a better investment than LinkedIn at $667 per hire with 70% retention.

Metric #5: Conversion rates by source

Track each source through your funnel: Application-to-screen rate (what % of applicants from this source pass screening?), Screen-to-interview rate (what % get interviewed?), Interview-to-offer rate (what % get offers?), Offer-to-hire rate (what % accept offers?). High-quality sources have high conversion rates at every stage. Low-quality sources have high drop-off during screening (they send lots of unqualified candidates). Example: Referrals might have 50% application-to-interview conversion (half of referred candidates get interviewed). Indeed might have 2% conversion (98% rejected at screening). This tells you Indeed candidates are less qualified—adjust your Indeed job posting or stop using it.

Metric #6: Time-to-hire by source

Some sources are faster than others. Employee referrals often have shorter time-to-hire because: referred candidates are pre-vetted by the referrer, they're more engaged and responsive, they often know someone at the company (culture fit). Recruiting agencies might be slower because: they're managing multiple candidates across multiple clients, there's a middleman slowing communication. Track time-to-hire by source. If one source consistently hires 20 days faster, prioritize it for urgent roles.

Which cost and ROI metrics prove recruiting value to leadership?

CFOs and executives care about dollars. These metrics show recruiting's financial impact:

Metric #1: Cost-per-hire

The total cost of filling a position. Global average: $4,700. Formula: (Internal recruiting costs + External recruiting costs) ÷ Number of hires. Internal costs: recruiter salaries, ATS subscription, job board fees, employee referral bonuses. External costs: recruiting agency fees, job ads, career fair expenses, background checks. Track cost-per-hire by: role level (entry-level vs. senior), department (engineering vs. sales), source (LinkedIn vs. referrals). Senior roles cost more to hire (higher agency fees, longer search). But if your entry-level cost-per-hire is $6,000 vs. the $4,700 average, you're overspending somewhere. Break down costs to find waste. Research shows: a strong employer brand reduces cost-per-hire by up to 50%. Investing in your brand (careers page, social presence, employee advocacy) pays off.

Metric #2: Cost-per-screen

How much does it cost to screen one resume? Manual screening: (Recruiter hourly rate × Hours spent screening) ÷ Resumes screened. Example: Recruiter earns $30/hour, screens 30 resumes/hour = $1 per screen. AI screening: (Monthly tool cost) ÷ Resumes screened per month. Example: $500/month tool, 1,000 resumes = $0.50 per screen. Compare costs. If AI is cheaper and faster, it's an easy ROI case. If manual screening is higher quality (fewer false positives), justify the higher cost with quality-of-hire data.

Metric #3: Time-to-fill and time-to-hire (in dollars)

Every day a position sits open costs money: lost productivity, overtime for existing team, delayed projects. Calculate cost of vacancy: (Annual salary for position ÷ 365 days) × Number of days vacant. Example: $100K role vacant for 45 days = $12,329 in lost productivity. If you reduce time-to-fill from 45 to 30 days, you save $4,110 per hire. Track time-to-fill and time-to-hire: Time-to-fill: job posting to offer acceptance (industry average: 36-47 days). Time-to-hire: application to offer acceptance (faster metric, shows efficiency). Use these to calculate cost of slow hiring and ROI of improvements. If investing in AI screening cuts time-to-hire by 15 days, calculate the savings ($4,110+ per role) and show it to your CFO.

Metric #4: Quality-of-hire ROI

High-quality hires produce more value and stay longer. Calculate: Revenue/productivity per employee, Cost of turnover (replacement cost + lost productivity). Example: High performer produces $200K value/year. Average performer produces $150K. Low performer produces $100K or leaves within 6 months (turnover cost = $50K). If improving screening increases quality-of-hire and you hire 10 more high performers, that's +$500K in value/year. Show this to leadership. Quality of hire isn't just warm and fuzzy—it's dollars.

Metric #5: Recruiter productivity (hires per recruiter)

How many hires does each recruiter make per month/quarter? US benchmark: 55 hires per recruiter per month (this seems very high—likely includes TA coordinators handling volume roles). More realistic for corporate recruiting: 3-5 hires per recruiter per month. Track: hires per recruiter, time spent per hire, cost per hire by recruiter. If one recruiter consistently makes 8 hires/month while another makes 2, either one is superhuman or one needs support/training. Or they're working on different role types (volume vs. specialized). Use this to: identify top performers and learn their processes, identify struggling recruiters and provide coaching, determine optimal recruiter headcount (if each recruiter can handle 5 hires/month, and you need 20 hires/month, you need 4 recruiters).

Metric #6: ROI of recruiting tools

For every tool you pay for (ATS, AI screening, job boards, assessments), calculate ROI: (Value generated - Cost) ÷ Cost × 100. Value generated: time saved (in $), improved quality-of-hire (in $), cost-per-hire reduction. Example: AI screening tool costs $6,000/year. It saves 10 hours of recruiter time per week (520 hours/year × $30/hour = $15,600 in time savings). ROI: ($15,600 - $6,000) ÷ $6,000 × 100 = 160% ROI. Show this to leadership when they question recruiting budgets. Every dollar spent on the right tools returns $2.60.

How should you visualize these metrics on a dashboard?

A good dashboard is visual, real-time, and actionable. Here's how to design it:

Dashboard structure (top to bottom):

Section 1: High-level KPIs (top of dashboard—the headlines)

Show your most important metrics at a glance: Total open positions, Total applications this month, Average time-to-hire (current + trend), Average cost-per-hire (current + trend), Quality-of-hire score (composite metric), Offers extended vs. offers accepted. Use big numbers with color coding: Green = hitting targets, Yellow = slightly off target, Red = major issues. Executives can glance at this and understand recruiting health in 10 seconds.

Section 2: Funnel visualization (where candidates drop off)

Visual funnel showing: Applications received → Screened → Interviewed → Offered → Hired. Show conversion rates between each stage (e.g., "3% of applicants get interviewed"). Highlight where the biggest drop-offs happen. If 1,000 applications → 800 screened → 30 interviewed, the bottleneck is screening-to-interview conversion. Focus there.

Section 3: Source-of-hire breakdown (where your talent comes from)

Pie chart or bar chart showing: % of hires by source (referrals, job boards, careers page, agencies, etc.). Include: cost per hire by source, quality metrics by source (retention, performance). This helps you reallocate budget. If 40% of hires come from referrals but you're only spending 10% of budget on referral bonuses, increase it.

Section 4: Time-based trends (are you getting better or worse?)

Line graphs showing trends over time (last 6-12 months): Time-to-hire trend, Cost-per-hire trend, Application volume trend, Quality-of-hire trend. Trends reveal if changes are working. If you implemented AI screening 3 months ago, you should see time-to-hire decreasing. If not, the tool isn't helping.

Section 5: Role-specific or recruiter-specific drill-downs

Filters to view metrics by: specific role or department, individual recruiter, hiring manager. This helps diagnose specific problems. If engineering roles have 60-day time-to-hire but marketing roles have 30-day, something's different about engineering hiring (harder roles, slower hiring managers, or pickier requirements).

Best practices for dashboard design:

• Keep it simple: Don't cram 50 metrics onto one screen. Show the 8-10 that matter most.
• Use visuals: Humans process charts faster than tables. Use graphs, not spreadsheets.
• Make it real-time: Update daily or weekly, not monthly. Stale data = useless data.
• Add context: Show industry benchmarks alongside your metrics (e.g., "Your time-to-hire: 42 days. Industry average: 36 days.")
• Enable drill-downs: Let users click on a metric to see underlying data (e.g., click "time-to-hire" to see breakdown by role)
• Automate it: Don't manually update dashboards. Pull data automatically from your ATS, HRIS, and other systems

Tools to build your dashboard: ATS with built-in analytics (Greenhouse, Lever, Workday have dashboards), Business intelligence tools (Tableau, Power BI, Looker for custom dashboards), Spreadsheets (Google Sheets or Excel if budget is tight—not ideal but functional). Most modern ATS platforms include dashboards out of the box. If yours doesn't, it's time to upgrade.

Related reading

• The Complete Guide to AI Resume Screening in 2025
• Skills-Based Hiring: A Practical Guide for Modern Recruiters
• How AI Reduces Unconscious Bias in Resume Screening

Join the conversation

What metrics are you tracking on your recruiting dashboard? Share your experiences and get advice from other talent acquisition professionals:

• Talent Acquisition & Recruitment Professionals - LinkedIn Group
• r/recruiting - Reddit community
• ERE - Recruiting and talent acquisition community