How Contractors Use Analytics to Make Better Hiring Decisions

Hiring Resources - September 29, 2026

construction manager reviewing contractor hiring analytics on a laptop at a busy building site.

Understanding the ROI of Data-Driven Recruitment

Every year, construction firms hire dozens—sometimes hundreds—of workers across multiple project sites. Yet most contractors make those hiring decisions the same way they did a decade ago: a quick phone screen, a gut feeling about someone’s experience, maybe a reference call if they’re lucky. The result?

Costly turnover, projects delayed by understaffing, and positions that take months to fill. The contractors winning in today’s talent-constrained market aren’t relying on instinct anymore. They’re using data.

Analytics isn’t just for finance teams or tech companies. Construction firms that track hiring performance, measure recruitment outcomes, and use historical data to inform decisions are consistently outpacing their competitors. They fill positions faster, retain talent longer, and build teams that actually perform on site. If you’re still making hiring decisions without looking at the numbers, you’re leaving money on the table.

Why traditional gut-based hiring often fails in construction

Construction is built on experience. A superintendent with 20 years on job sites knows what works. A project manager has instincts honed by dozens of completed projects. That institutional knowledge matters. But when it comes to hiring, those instincts frequently fail because they’re based on anecdote, not pattern.

Here’s the problem: your best people might all come from the same geographic region, the same training program, or the same company you worked for five years ago. You unconsciously hire people who remind you of past successes, even if they lack the specific qualities that actually predict performance on your projects. Two candidates look equally qualified on paper, so you pick the one who went to the same university as your VP. Six months later, one thrives and the other struggles—but you don’t track why.

Without measurement, you can’t see patterns. A firm might consistently hire estimators who look great in interviews but leave after 18 months. Another might notice that field engineers from certain backgrounds perform better under pressure. But if you’re not capturing that data, you’re making the same hiring mistakes repeatedly, each time hoping for a different outcome.

Analytics forces you to ask the hard questions: Which hiring channels actually produce your best performers? What background or certification actually correlates with long-term success? Are you spending recruitment dollars on channels that don’t deliver results? When you measure, you stop guessing.

Measuring cost-per-hire and time-to-productivity metrics

Start with the basics. Cost-per-hire tells you exactly what you’re spending to fill each position. This isn’t just salary and benefits. It’s recruiter time, job posting fees, background checks, onboarding, training hours, and the administrative overhead of managing an open position. For specialized roles like construction staffing through agency partners, it’s the fee they charge. Most contractors never calculate this number. They should.

Time-to-fill matters even more. If a critical position sits open for three months, you’re either overstaffing other roles to compensate or you’re understaffed and grinding through backlog. Project schedules slip.

Experienced workers burn out covering the gap. Then when you finally hire someone, they need time to ramp up. This is where time-to-productivity enters the picture: how long before that new hire reaches full effectiveness on site?

A field engineer might take two weeks to understand your safety protocols and project workflows, or they might take two months if they lack relevant experience. That ramp-up window is invisible unless you measure it. Tracking these metrics reveals which hiring sources deliver people who are productive faster. Maybe experienced candidates require less training. Maybe your best performers come from contractors you’ve worked with before. The data will tell you where to focus recruiting resources.

How analytics reduce turnover and project delays

Turnover is expensive. Losing a skilled worker means restarting the hiring process, losing institutional knowledge, disrupting team dynamics, and potentially missing a project deadline. Construction turnover rates hover around 20-30% annually in many firms—that’s roughly one in every four to five workers leaving each year. But when you analyze which positions have the highest turnover, and which hiring decisions led to those departures, patterns emerge.

Maybe your preconstruction team loses people at double the rate of your field operations. Maybe hires from one staffing channel last twice as long as another. Maybe positions promoted internally stay much longer than external hires for similar roles. Each pattern points to a lever you can pull: change where you recruit from, invest differently in onboarding, adjust compensation for high-turnover roles, or rethink your career development structure.

Project delays from understaffing aren’t just about missing deadlines. They cascade: trades can’t mobilize, clients lose confidence, relationships suffer, and your reputation takes a hit. When hiring analytics show you that certain positions consistently fill slowly, you can staff ahead on future projects or maintain a flexible bench of contractors. Understanding what matters when, for example, helps you identify people faster and make better matches.

Building a business case for recruitment analytics adoption

The hardest part of implementing analytics isn’t the technology—it’s the decision to start. It requires investment in systems, time to clean data, and a shift in how your team thinks about hiring. But the ROI becomes clear quickly. If your average hire costs $8,000 and takes 45 days to fill, and analytics help you cut that to 35 days while improving retention by 10%, you’re talking about thousands in savings annually.

Start small. Pick one position type—maybe your highest-turnover role or your hardest-to-fill position. Track where candidates come from, how long they take to become productive, and whether they stay past year one.

Build the case from there. Share wins with leadership: “Our construction staffing strategy reduced time-to-productivity for field engineers by three weeks this quarter.” That’s real money.

Most construction firms have the data sitting in their systems already—application tracking system records, hiring dates, project assignments, performance reviews. You’re not starting from scratch. You’re just learning to read what you already have.

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Key Metrics Contractors Should Track When Hiring

Performance indicators that predict on-site success

Here’s the thing: a candidate who looks great on paper might bomb on your first day of framing. That’s why contractors need to track metrics that actually correlate with real-world performance.

Start with time-to-productivity. This is how long it takes a new hire to reach full efficiency on their specific role. Field engineers typically need 2-4 weeks, while experienced project managers might hit stride in days. When you track this metric across dozens of hires, patterns emerge. Maybe candidates from a certain background consistently ramp faster. Maybe those with specific certifications or prior experience on similar project types outpace others. That’s actionable intelligence.

Next, watch quality of work output. For field roles, this means rework rates, punch list items attributed to their work, and defect frequency. For office-based staff like estimators and BIM managers, track estimate accuracy and clash detection rates. The contractors winning in talent-constrained markets measure these consistently across projects and tie them back to individual hires. When you discover which candidates from which recruitment channels produce fewer defects, you’ve found a hiring advantage.

Project completion rates matter too. Did the candidate stay through project close-out? Did they meet milestones without delays tied to their performance? Some contractors focus so heavily on getting bodies on-site that they ignore whether those bodies actually deliver. Using skills and competencies as a framework helps separate candidates who check boxes from those who genuinely execute.

Evaluating candidate quality beyond credentials

Credentials are the floor, not the ceiling. A PE stamp matters, but it doesn’t tell you if someone communicates effectively with field crews or if they adapt when site conditions change.

Smart contractors now track soft skill performance indicators. This includes collaboration scores from project teams, supervisor feedback on problem-solving ability, and how well someone handles pressure during schedule crunches. When you’re evaluating candidates for roles like construction superintendent positions, these intangibles predict success better than years of experience alone.

Create a simple performance rubric for key competencies. If you’re hiring field engineers, maybe you rate candidates on technical knowledge, safety awareness, communication, and adaptability. After six months on-site, score them against that same rubric. Over time, you’ll see which candidate profiles consistently score highest. That’s your hiring template.

Another metric that separates high performers: adaptability across different project types. A candidate who thrives on commercial office buildings might struggle with heavy civil work. By tracking which hires excel across diverse projects versus those who need specific conditions, you build a more resilient workforce. This matters enormously when project schedules shift or market conditions change.

Tracking safety records and compliance history

Safety data is non-negotiable. It’s both a moral imperative and a hard business metric that impacts insurance premiums, bonding capacity, and client relationships.

Track these specific indicators: OSHA recordables, near-miss reports, safety training completion rates, and compliance violations. But here’s what most contractors miss: tie safety performance back to individual hires and hiring sources. If one staffing partner consistently places candidates who maintain zero incidents, that’s your benchmark. If a particular skill set correlates with higher safety compliance, you’ve identified a talent profile worth pursuing.

Measure safety culture indicators too. Does the candidate suggest improvements? Do coworkers cite them during safety toolbox talks? High-performing contractors use crew feedback to identify safety champions, then use that profile for future hiring decisions. This creates a flywheel effect where your safest projects become your most attractive to talent.

Document compliance training completion by hire and by hiring source. When you work with staffing providers, verify they’re screening for this. A candidate with current certifications and completed training saves you weeks in onboarding.

Assessing retention rates and long-term workforce stability

Turnover is expensive. The true cost includes recruiting time, lost productivity during ramp-up, training investment, and knowledge gaps. Contractors who build sustainable competitive advantage track retention religiously.

Measure tenure by hire source and candidate profile. How long do people from different staffing providers stay? Which backgrounds show better stickiness? Maybe candidates with strong local roots retain longer. Maybe experienced professionals value flexible scheduling more than steady employment. These patterns are gold for future recruiting.

Calculate cost-per-retained-hire by dividing total recruitment and onboarding costs by the number of people who make it through your first year. When you compare this metric across different hiring strategies, you quickly see which investments actually build stable teams versus which just cycle bodies through positions.

Track why people leave too. Exit interviews reveal whether turnover stems from workload, management style, pay misalignment, or better opportunities elsewhere. When you identify departures tied to specific projects or managers, that’s a signal to adjust your staffing strategy or leadership approach. Most contractors who excel at retention treat this data like a leading indicator of project success, not an HR administrative task.

Identifying Skills Gaps Before They Impact Projects

Using historical project data to forecast skill requirements

You already know what happened on your last three projects. You have timesheets, completion dates, change orders, and staffing lists. So why aren’t you using that data to predict what you’ll need next?

Historical project data is a goldmine for forecasting. When you examine completed projects side by side, patterns emerge. A 50,000 sq ft commercial build needed 12 electricians for 18 weeks.

A similar 48,000 sq ft project down the road will follow a comparable timeline and staffing curve. But if that second project has more complex systems or tighter scheduling, you can adjust the forecast upward before you’re scrambling to find bodies mid-phase.

Start by cataloging your project types: renovation work, new construction, heavy civil, mixed-use. Within each category, track which trades you deployed, how long they stayed on site, and when you needed them most. Electrical rough-in peaks at month three? That’s not a surprise if you’ve mapped it across five projects already.

The real insight comes from comparing actual staffing to planned staffing. Were you understaffed? Overstaffed? Did you bring in contractors to fill gaps? Document it all. When you can see that you consistently underestimated concrete finishers in the final six weeks, you stop making that mistake. You staff accordingly, and you avoid the premium pricing that comes with emergency recruitment.

Creating skills matrices for different trade roles

A skills matrix isn’t fancy. It’s a grid that maps specific competencies against your workforce, trade by trade.

For your electrical crews, list the core skills: conduit bending, safety compliance, load calculations, blueprint reading, troubleshooting. Score your current team members (novice, intermediate, expert). Do the same for your concrete crews: formwork, finish grading, strength testing, scheduling pour sequences. And for your project managers: critical path tracking, budget management, stakeholder communication, subcontractor coordination.

This matrix does two things immediately. First, it shows you what you have. You’re not guessing whether your team can handle a data center fit-out that requires specialized electrical work.

You check the matrix. Second, it shows you gaps. If nobody on your 40-person roster has BIM/VDC experience and your next three projects demand it, you know right now (not in month two) that you need to hire or train.

When working with specialized skills, the matrix becomes even more critical. You’re not just counting heads; you’re matching capabilities to project demands with precision.

Comparing current workforce capabilities against pipeline demands

You have a pipeline. Maybe it’s a 18-month forecast with four projects queued. Each has different scope, timeline, and complexity. Your job is to compare what those projects need against what you actually have on hand.

Take your skills matrix and lay it against your project pipeline. Project A needs three experienced formwork supervisors, five finishers, and two concrete pump operators starting in month four. You check your matrix: you have one supervisor who’s ready now, two finishers with intermediate skills, zero pump operators.

That’s a gap. A significant one. And you’ve identified it while you still have time to recruit, train, or contract.

Now compare this across all projects simultaneously. Your pipeline shows that in month six, you’ll need 15 electricians. In month seven, 22.

In month eight, 18. If your current roster peaks at 12 electricians, you’re looking at major external hiring or delayed phases. Neither is ideal.

But knowing this now lets you build a talent strategy. Maybe you start recruiting in month three. Maybe you negotiate longer timelines with clients.

Maybe you partner with a staffing firm to access experienced workers on demand.

This comparison isn’t one-time analysis. It’s a rolling quarterly exercise. Every time your pipeline shifts, you re-map capabilities against demands and adjust your hiring strategy accordingly.

Planning training and upskilling based on future project needs

Not every gap requires external hiring. Some gaps are opportunities to develop your existing workforce.

If your pipeline shows you’ll need BIM/VDC expertise in 14 months and you have two younger project coordinators with strong technical aptitude, you have a path. Invest in training now. They’re ready when your project kicks off. You’ve built capability within your team, increased retention, and saved the recruitment costs of bringing in external talent with that skillset.

The same applies to safety certifications, equipment qualifications, or leadership development. Your matrix showed you have good field engineers but weak supervisory bench strength. A mentorship program or formal management training filled over six months turns experienced field talent into your future project leaders.

Use your historical data and pipeline projections to drive this planning. If every project in the next year requires Spanish-speaking coordinators and you currently have none, language training becomes a priority now, not an afterthought. If crane operation is a consistent bottleneck and you have equipment operators willing to certify, that’s investment with clear ROI.

The contractors who stop reacting to hiring crises and start planning for them are the ones building stronger teams and hitting project timelines consistently. Analytics make that possible.

Leveraging Past Performance Data in the Hiring Process

Mining internal records for high-performer profiles

Every contractor has a goldmine of hiring data sitting in spreadsheets, project files, and performance reviews. The trick is actually mining it. Start by pulling records on your top performers across the last three to five years.

Look beyond just job titles and salary bands. What certifications do they hold? How long did they work on average projects before moving to the next role?

What was their safety record? Which supervisors requested them for repeat assignments?

This isn’t busywork. When you systematically extract this information, patterns emerge. You might discover that your best site superintendents all came from specific trade backgrounds, or that your highest-retention estimators shared a particular type of prior experience. Many contractors find that using data-driven recruiting approaches helps uncover these hidden connections that gut instinct alone would miss.

Document everything methodically. Create a spreadsheet with columns for hire date, years of experience, educational background, previous employers, certifications, projects completed, performance ratings, and tenure. The more granular your data capture, the clearer your high-performer profile becomes.

And yes, this takes time upfront. But you’re building the blueprint for every hiring decision moving forward.

Benchmarking candidate backgrounds against successful employees

Once you’ve mapped your high-performer profile, use it as a benchmark for evaluating new candidates. When a resume lands on your desk, compare it against the characteristics of your best people. Did your top field engineers typically have two to four years in a specific specialty before leading teams?

Did they come from certain regions or contractor backgrounds? These details matter tremendously in construction, where specialized experience directly impacts project outcomes.

Benchmarking also reveals gaps in your current recruitment approach. Maybe your best project managers all had prior experience with design-build delivery, but you’ve been recruiting from general contracting backgrounds. That’s actionable intelligence.

It tells you where to focus your sourcing efforts and what skills gaps exist before they sabotage a project. When evaluating candidates, look beyond credentials alone. Examine the trajectory of their career.

Steady progression through increasingly complex projects suggests someone who builds expertise intentionally, not accidentally.

This comparative analysis works especially well for specialized roles. When hiring for positions like estimators or BIM/VDC managers, the difference between a qualified candidate and a truly exceptional fit often comes down to specific project experience and technical depth. Benchmarking prevents you from accepting “close enough” when your historical data shows exactly what excellence looks like in your organization.

Identifying patterns in employees who exceed expectations

Not all high performers are created equal. Some consistently hit targets. Others exceed them dramatically. That distinction matters. Dig into the records of your stars who consistently over-deliver. What separates them? Sometimes it’s technical skill depth. Sometimes it’s soft skills like communication or problem-solving under pressure. Often it’s a combination of both.

Look for patterns across different roles and projects. Did your best safety coordinators share a background in training or compliance? Do your top superintendents all have experience managing teams in specific project types?

Have your strongest project managers worked on similar-sized projects before succeeding at larger scales? These patterns reveal the actual prerequisites for exceptional performance in your organization.

Many contractors discover that employees who exceed expectations share unexpected commonalities. Maybe they all came from companies known for lean operations. Maybe they all had mentorship relationships early in their tenure with your firm. Maybe they consistently completed additional skills training beyond what was required. Once you identify these patterns, you can actively recruit for them. You’re no longer just hoping for excellence. You’re deliberately sourcing it.

Using project outcomes to refine hiring criteria

Your hiring decisions have consequences measured in budget variance, schedule performance, safety incidents, and client satisfaction. Connect those outcomes back to the people you hired. Which team compositions delivered projects under budget? Which hires contributed to scope creep? Track projects where specific individuals played key roles, then correlate their characteristics to those results.

This feedback loop refines your hiring criteria continuously. If a particular hire consistently solved problem-solving challenges on complex jobs, emphasize that trait in future recruitment. If someone struggled with a specific skill set despite having the right title, stop hiring for that title alone. Start hiring for the actual competencies that drive success on your projects.

The data you gather becomes your competitive advantage. Over time, you’re building a predictive model for hiring success specific to your organization, your project types, and your market. That level of precision beats industry best practices every time because it reflects your actual operational reality.

Tools and Platforms for Construction Recruitment Analytics

HRIS Systems and Their Role in Workforce Intelligence

Your HRIS (Human Resources Information System) is the backbone of workforce intelligence. It holds everything: hire dates, project assignments, tenure, compensation, performance reviews, and termination reasons. But most contractors aren’t mining this data for recruitment insights. They’re using it as a filing cabinet.

The real value emerges when you extract hiring patterns from your HRIS. Which positions have the highest turnover? Where do your best performers come from? What’s the average time-to-productivity for hires from different sources? These questions reveal where your recruitment strategy is working and where it’s bleeding talent.

Modern HRIS platforms now include basic analytics dashboards. You can track hire-to-fire timelines, identify which projects retain staff longest, and spot roles where onboarding consistently falls short. Some systems flag retention risks automatically, alerting managers when an experienced employee’s engagement metrics dip or when they’re being actively recruited elsewhere.

The challenge? Data quality. If your HRIS contains incomplete job descriptions, vague termination notes, or inconsistent project coding, your analytics will be garbage. Contractors serious about data-driven hiring invest time standardizing how information gets entered. It’s tedious, but it transforms your HRIS from a compliance tool into a strategic asset.

Applicant Tracking Systems with Built-In Reporting Capabilities

Your ATS (Applicant Tracking System) captures the hiring funnel: applications received, candidates screened, interviews conducted, offers extended, acceptances. The older generation of ATS platforms treated reporting as an afterthought. Modern systems make analytics central.

Smart ATS dashboards show you exactly where candidates drop off. Are construction managers clicking away during the initial screening? Are estimators ghosting after the phone interview? Are electricians accepting offers but failing to show up on day one? Each bottleneck tells you something about your recruitment messaging, interview process, or offer competitiveness.

Time-to-fill metrics are especially valuable. If your average time-to-fill for a structural engineer is 45 days but your competitor fills the same role in 20 days, something in your process is slowing you down. It might be your job description, your screening criteria, your interview scheduling, or your salary positioning. An ATS with solid reporting helps you isolate which variable needs fixing.

Some ATS platforms now include AI-powered candidate ranking, flagging which applicants match your historical high performers. Others integrate with your HRIS, showing you how candidates from different sources eventually perform on projects. This feedback loop transforms hiring from a guessing game into a measurable process.

Third-Party Analytics Platforms Designed for Construction

General HR analytics platforms exist by the dozens. But they weren’t built for construction’s unique challenges: seasonal demand spikes, project-based workforce needs, specialized skill sets, and safety-critical hiring decisions.

Construction-specific analytics platforms fill that gap. These tools are designed to track construction staffing metrics that matter: safety incident rates by hire source, productivity variance across different crews, project completion rates when staffed with internal versus external talent, and crew stability (how often workers rotate between projects). They understand that your “turnover” on one project might just be that crew moving to another project in your portfolio.

These platforms also integrate labor market data, showing you historical hiring trends, wage movements, and skills scarcity in your region. If your market intelligence tells you that experienced BIM/VDC managers will be 20% harder to find next quarter, you can start recruiting now instead of panicking in three months.

The downside is cost. Purpose-built construction analytics platforms are pricier than generic HR software. But for firms managing multiple projects across different locations, the ROI typically materializes within a year through faster hiring cycles and fewer bad placements.

Integrating Data Sources for a Complete Hiring Picture

Your best hiring intelligence lives across fragmented systems. Your HRIS knows who you hired and how long they stayed. Your ATS knows how they applied and what their initial screening scores were. Your project management software knows how they performed on actual work. Your safety system knows their incident history. Your accounting system knows their cost per dollar of revenue generated.

Connecting these data sources creates a complete picture. When you integrate these systems, you can ask sophisticated questions: “Which recruiting source produces the lowest safety incident rates?” or “Do hires from staffing partners have higher retention on infrastructure versus commercial projects?” or “Which screening interview question best predicts project performance?”

Integration doesn’t require replacing all your systems. APIs and data warehouses let you pull information from existing tools into a central analytics hub. Some contractors build custom dashboards using their existing data. Others work with staffing partners like K2 Staffing who help choose the right based on your historical performance data.

The technical lift is real, but the payoff is significant: recruitment decisions informed by complete workforce intelligence rather than educated guesses.

Implementing Analytics Without Disrupting Your Hiring Pipeline

Starting small with pilot programs and one or two metrics

The biggest mistake contractors make when adopting analytics is trying to do everything at once. You don’t need a full suite of dashboards, predictive models, or enterprise-level systems to start seeing value. Instead, pick one specific hiring challenge and measure it properly.

Maybe your team struggles with time-to-fill for specialized roles like estimators or project managers. Or perhaps you’re seeing high turnover in field positions within the first six months. Choose that one problem, select one or two metrics that directly measure it, and build your pilot around those.

A pilot program typically runs for two to three months on a single project type or location. Track the metric manually if you need to. The goal isn’t perfection yet, it’s learning.

You’ll discover which data points actually matter to your workflow, which sources are reliable, and where the gaps in your current tracking systems live. Once you’ve validated the approach on a small scale, scaling across multiple teams becomes far less risky.

Training your recruitment and management teams on data interpretation

Here’s what separates contractors who benefit from analytics from those who get frustrated with it: your teams actually understand what the numbers mean. Raw data sitting in a spreadsheet doesn’t change hiring decisions. Interpreted insights do.

Your recruiters need to understand why you’re tracking application-to-interview ratio or candidate source performance. Your project managers need to know how historical performance data informs hiring quality. Your executives need context on how staffing metrics connect to project profitability and schedule adherence. Without this shared understanding, analytics becomes someone else’s project, not part of how you actually make decisions.

Start with basic training focused on the specific metrics you’re measuring in your pilot. Show teams what the data looks like, how you’re collecting it, what healthy benchmarks look like, and most importantly, what actions they should take based on what the numbers reveal. Include scenario-based examples from your own projects.

When a recruiter sees that candidates from LinkedIn have a 40% better retention rate than those from another source, that becomes actionable. They immediately adjust sourcing strategy.

Establishing realistic timelines for data collection and analysis

Construction cycles are long. Project timelines stretch across quarters or years. If you’re measuring hiring quality by tracking on-the-job performance, you can’t expect meaningful results in four weeks. Conversely, if you’re measuring time-to-fill or cost-per-hire, you’ll have useful data within 60 days.

Match your analysis timeline to the metric. Time-to-fill and source quality can be evaluated monthly. On-the-job performance, retention rates, and project-specific outcomes need 6 to 12 months of data to spot real patterns versus noise. Safety incidents and productivity correlations to hiring decisions require even longer observation periods, sometimes pulling from multiple project cycles.

Set expectations with leadership upfront. Don’t promise ROI improvements in month two if you’re measuring retention. Do commit to identifying quick wins in sourcing and screening processes within 60 days.

The realistic timeline builds credibility. When you say “we’ll have meaningful insights by Q3” and you actually deliver them, stakeholders trust the process. They’ll fund the next phase without question.

Scaling analytics across multiple projects and locations

Once your pilot succeeds, scaling means standardizing what worked. The same metrics your team validated on one project should apply to similar projects in different locations, but the implementation needs consistency. This is where most contractors stumble.

Create simple standardized definitions for your key metrics. What exactly counts as a “qualified candidate”? How do you measure performance consistently across different project types or regions? Document these. When your recruitment and management, data becomes comparable. You can now safely compare hiring metrics between your San Diego operations and your Santa Clarita team.

Integrate your analytics into existing workflows rather than creating parallel systems. If your project managers use a specific project management tool, build your hiring metrics into reports they already access. If your ATS already captures certain data, leverage that instead of asking teams to manually track separately. The easier you make it to collect and report on data, the more likely adoption sticks across multiple locations.

Scaling also means gradually expanding beyond your initial metrics. Once time-to-fill is standardized across projects, add source quality. Once that’s solid, layer in performance correlation analysis.

This gradual expansion prevents overwhelming your teams while continuously improving hiring decisions. The contractors who succeed with analytics think long-term. They understand that building a data-informed hiring culture takes time, but the payoff compounds across every project and every hire you make going forward.

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