Data-Driven Approaches to Talent Acquisition

The most common phrase I hear in university recruiting meetings? "But we've always recruited from these schools." This mentality costs companies millions. Here's why: · Markets evolve. Your talent sources should too. · They are not using internal and external data insights to inform their strategy. · Top candidates aren't where they were 5 years ago. They're exploring new programs, new majors, new paths. · Traditional target schools are oversaturated with recruiters. You're fighting the same battle as everyone else. · Emerging programs at lesser-known schools often produce hungry, high-performing talent. Our solution? Build an analysis tool that combines internal and external data to pinpoint the best campus sources. Track: - Historical offer acceptances - Intern-to-full-time conversions - Employee engagement levels - Promotion rates - Demographic and diversity metrics The tool transforms your strategy. Recruiters input the business forecasts for hiring needs: - Projected hiring numbers - Target locations - Role types The system generates a targeted list of campuses, backed by real data. The results: • Increase in diversity hiring • Jump in offer acceptances • Higher intern-to-full-time conversion rates The reality is simple: Yesterday's recruiting playbook won't win tomorrow's talent. We need to question our assumptions. Challenge our "tried and true" methods. Look where others aren't looking. Ask yourself: When was the last time you revisited your data and sourcing strategy?

Recruiters: data & KPIs are your friend, not your enemy. I get it… sometimes it can feel overbearing to feel like your work is being scrutinized or reduced to a set of numbers/KPIs. And data never tells the full story, but I’ve come to learn just how key it is… I used to work closely with Mike Moriarty at Dropbox in the early days of Gem. And one of the things I was most impressed by was how robust his Recruiter & Sourcer scorecards were. I still remember many of the KPIs, which included activity metrics across key steps of the funnel: # Reach Outs + # Follow-ups Sent + # Recruiter Phone Screens + # Offer Accepts. Mike had worked with each of his recruiters to reverse-engineer how many activities they needed at every step of the funnel to hit their # Offer Accept goals for the quarter. And they had detailed targets broken down by quarter / month / week. They also tracked conversion metrics across key steps to keep a close eye on quality: response rates (quality of outreach), ph screen -> onsite rates (quality of candidate). At first glance, I thought some recruiters might resist the detailed tracking, but Mike had instilled this unique culture where the team embraced a data-driven approach. And as I talked to recruiters across his team, it became clear why…  → having detailed weekly/monthly/quarterly KPIs across every step of the funnel allowed each person to know whether they were on track to hit their goals.  → and if not, they could very quickly see where they needed to focus their efforts to get back on track.  → there were never surprises EOQ and the recruiters on Mike’s team were super high-performing. Mike & team’s data-driven approach allowed each recruiter to operate their open reqs like a business.

Workforce planning has always been an incredibly complex and difficult task. Despite valiant efforts to improve these models, they have remained relatively static and simplistic, relying predominantly on small teams crunching data or on predictions from the hiring manager community. In an ideal world, we would shift from a static, once-a-year exercise to a dynamic, more proactive model. We would stop reacting to what's happening now and start anticipating what's likely to happen next. Last week, I had the pleasure of spending time with our enterprise data and analytics team, a group that services over 800 customers. The most exciting topic we discussed was three pilots we're running with customers right now that aim to make this a reality: using a digital twin for work planning. It works by connecting vast amounts of external market data with a company's many internal data sources, some they typically wouldn't consider, such as ERP, CRM (sales), LMS, and Time and Attendance systems. This allows us to run scenarios and model future talent needs. Here’s a concrete example: By analyzing Salesforce, HRIS, and ATS data, we can predict that when multiple prospect opportunities reach a specific stage in our customer’s sales cycle, there is a high likelihood of winning at least one of them. We can then analyze the consistent skill sets across all of those prospect opportunities, allowing us to confidently and proactively start a recruitment process for those skills. The goal being that we have candidates at the final stages of the process, before an official requisition has been raised, positively impacting time to hire. We’ve also been able to replicate a similar model based on website sales activity. The question to ask is: what data is generated in what system that allows you to get ahead of the hiring process today. 

Your hiring data’s a mess. Here’s how to fix it. HR loves to toss around “data-driven decisions” like it’s a badge of honor. I’ve seen it everywhere, companies hiring across dozens of countries, leaning on stats that don’t deliver. And the reality is that most of your data’s half-baked. I’ve worked with enough businesses to spot the pattern. Only 31% of HR leaders actually use external talent market data well. Half don’t trust their own recruitment metrics. And just 45% think their workforce planning numbers hold up. That’s not a strategy. It’s a coin toss. But here’s where it gets interesting. The issue isn’t the tools. It’s not about buying the latest tech or stacking dashboards. It’s simpler: your data’s only as good as what you do with it. The companies I’ve helped turn it around do three things right. • First, they dig into global talent market data—real stuff, not just internal guesses. Where’s the talent? What’s moving the needle locally? • Second, they measure what matters. Not vanity stats, but outcomes—quality of hire, retention, performance post-onboarding. • Third, they plan with data that’s fresh and borderless, not some stale spreadsheet from last year. In my experience, that’s the difference. I built Lundi on this: aggregate the messy global talent puzzle, cut the noise, and make it actionable. One client went from 60-day hiring cycles to 35. Another tapped a talent pool in Eastern Europe they’d ignored—doubled their engineering hires in six months. Data didn’t just sit there. It worked. You’ve got data already. Stop drowning in it and start using it. Ask: Is it current?  Does it reflect the real talent landscape?  Can it scale with you? If not, you’re not data-driven, you’re data-distracted. Fix that, and you’re not just hiring, you’re winning! Want more of this? I share what I’ve learned with 3,000+ leaders who get it. Newsletter link’s in the 📌 comment below.