Recruitment Marketing Analytics: Moving Beyond the Numbers to Real Intelligence

Joveo’s December webinar, From Insights to Intelligence, explores the real reasons hiring is broken, why traditional talent analytics are failing, and how AI is changing the rules

If you’re working in talent acquisition (TA) today, chances are you’ve found yourself saying some version of: “We’re drowning in applicants, but nobody is qualified.”

(If so, congratulations, you’re in excellent company. As you already know.)

Because, this is the paradox that’s exhausting recruiters and frustrating stakeholders across the hiring landscape — which is why Joveo’s recent webinar resonated so strongly.

In From Insights to Intelligence, Matt Charney, executive editor at Media Bistro & ERE Media, and Carrie Corbin, co-founder & managing partner at Hope Leigh Marketing Group, unpacked why recruitment continues to feel like an expensive guessing game and what it really takes to build predictable, scalable, profitable hiring pipelines.

Their central message was clear: the way we think about recruitment marketing analytics is fundamentally outdated. 

Most organizations are measuring the wrong things, optimizing the wrong decisions, and relying on metrics that no longer reflect how candidates actually behave or how hiring decisions are made. If that were not enough, AI adoption is accelerating faster than operational maturity, and as a result, talent teams are spending more money than ever while seeing less return.

As Matt summarized bluntly:

“It feels like you’re lighting your budget on fire and staring at it.”

And let’s be honest: no one in TA needs another lecture about the importance of data. This industry has never suffered from a lack of dashboards. What it lacks is the ability to turn data into meaningful action; the kind that drives business outcomes instead of report formatting.

Which brings us to the hard truth.

The reality check: Recruitment is at a breaking point

Talent acquisition has hit a critical inflection point. The strategies that worked even a few years ago simply don’t work anymore.

Let’s consider the numbers:

• Companies now receive an average of 262 applicants per job, so volume is not the problem.
• Yet hiring managers are still begging for qualified candidates.
• According to Joveo research, roughly 70% of recruitment marketing spend is wasted and the inefficiencies tied to talent attraction represent a staggering $4.4 trillion global economic impact.
• Only 27% of enterprise organizations use advanced analytics to manage paid recruitment media.

If that does not qualify as a structural failure hiding in plain sight, what does?

So, essentially, the problem is not pipeline size, or brand visibility, or budget levels. The real problem is waste: wasted money, wasted time, wasted effort, and wasted opportunity.

And, the conversation now has to move beyond dashboards and data exports and start understanding where waste occurs inside the hiring funnel and how to eliminate it. 

Matt and Carrie made this point crystal clear: the biggest errors in recruitment are rarely dramatic. They are slow leaks that are invisible at first, but continue out resources a little more every day.

To fix the problem, we first need to know where the funnel is breaking.

Diagnosing inefficiency in the recruitment funnel 

Deconstructing the recruitment funnel is the starting point for rebuilding it into something stronger, smarter, and more profitable. When most organizations look at the hiring funnel, they assume the problem is either too few applicants or too little budget. 

However, the reality is more complex. 

The majority of wasted spend can be traced to three value leaks. These are systemic breakpoints where budget and candidates evaporate long before they ever reach a hiring manager.

These leaks are the reason why paid media performance collapses, even when traffic and application volume look strong on paper.

Let’s break them down.

Value leak #1: Top-of-funnel waste

This is the place where the largest percentage of money disappears. TA teams spend aggressively on paid media, programmatic campaigns, and job board distribution, but most of that spend generates nothing of value.

Why it happens:

• Mistargeting and oversourcing: Job ads are pushed broadly without audience precision
• Flat-rate job boards continue to siphon spend even after performance drops
• Chasing cheaper cost-per-click instead of hiring outcomes, resulting in volume without quality.
• Traditional programmatic platforms optimize for traffic instead of qualification

Matt summed it up perfectly:

“You’re paying for trash applicants and trash clicks.”

The industry average cost-per-click (CPC) is $16.92. Think about that for a second. That’s not the CPC for a candidate or for an application, that’s just for a click. Multiply that by tens of thousands of clicks per campaign, and you’re left with a metric that looks impressive and is financially disastrous.

Lesson: Optimize for qualified candidates, not clicks or traffic volume.

Value leak #2: Mid-funnel friction

Even when you finally attract the right person, they often never make it through the application.

Why?

Because the hiring experience is so painful that they simply give up.

For context, 93% of candidates drop off between career site visit and submitted application.

Where is the friction coming from?

• Broken applicant tracking system (ATS) workflows
• Redundant form fields and excessive data requests
• Mobile-unfriendly apply experiences 
• Slow load times
• “Upload your resume—now retype your resume” black hole UX

As Matt joked:

“Our application flows ask for their blood type, their mother’s maiden name, and a resume upload after parsing their resume.”

Essentially, candidates are quitting because the process communicates disrespect for their time.

Every abandoned application represents wasted paid media spend. The marketing dollars that brought that candidate to the page are erased in seconds if the experience makes them walk away. 

Lesson: Every improvement here puts money back into the business.

Value Leak #3: Bottom-funnel bottlenecks

The final leak exists inside the organization itself. 

Even when a candidate is sourced, engaged, screened, and submitted, hiring stalls because:

• Hiring managers take 1–2+ weeks to review resumes. Result-best candidates disappear
• Scheduling delays stretch to weeks. Result-competing offers win
• Decision cycles drag on. Result-spend from earlier funnel stages is wasted.

Matt described this dynamic as organizational gravity — a slow weight that drags down everything that touches it. 

Or as Carrie said:

“Recruiting always gets blamed, but hiring managers own half the funnel and nobody holds them accountable.”

Carrie also shared how American Airlines solved this issue for entry-level roles:

• It removed hiring managers from early evaluation
• Used structured hiring events and campaign-driven funnel acceleration
• Moved candidates from apply → hire in a single controlled process

The result was a faster time-to-fill, reduced cost, less waste, higher hiring manager satisfaction.

Lesson: No level of optimized paid media can compensate for internal hesitation. 

So when organizations say:

• “We need more applicants”
• “We need cheaper traffic”
• “We need better job boards”

What they actually need is: Less waste, less friction, more accountability, and better intelligence.

Where talent acquisition analytics gets it wrong

One of the most eye-opening moments in the discussion was the realization that even when the funnel is working, most teams still don’t know what success actually looks like. Not because they lack data, but because they’re measuring the wrong things.

Recruitment has become overloaded with numbers that feel impressive but don’t help anyone hire better. In fact, dashboards are full, reports are long, and yet decisions still feel like guesswork.

Here’s the real issue with the way performance is measured today:

• Cost per click (CPC) only tells you someone looked, not whether they were the right person
• Cost per application (CPA) rewards volume instead of quality
• Application totals create more work for recruiters without improving outcomes
• Aggregate cost-per-hire hides which channels actually performed
• Last-click attribution oversimplifies a multi-step journey into a misleading single data point

And as Matt put it:

“If you’re reporting to a toddler, CPC is great. Everyone else wants qualified results.”

These metrics produce activity, but not insight. Which is why the conversation has to change. If we want better outcomes, we need measurements that reflect reality

And that brings us to the real shift happening in the market: the evolution of talent acquisition analytics from reporting the past to predicting the future.

The talent acquisition analytics maturity model: A pathway to predictive power

Once you understand where the funnel is leaking, the next question is obvious: what would it take to actually fix it?

To answer that, Matt and Carrie introduced a clear roadmap that shows how talent teams can evolve from basic reporting to real intelligence that predicts outcomes instead of reacting to them.

You can think of it as a maturity curve. Every organization sits somewhere on it, and moving up the curve is where the real transformation begins.

The model contains four stages:

1. No automation (Manual chaos. Post-it-note ATS): 11% of organizations

These teams are running recruiting almost entirely by hand. Think shared spreadsheets, endless email threads, scattered notes, and processes held together with hope and willpower. 

Matt even joked that Spotify once managed recruiting using literal Post-it notes. Sure, it gets a laugh, but the truth is uncomfortable: far more companies are closer to that reality than they want to admit.

At this stage, there is no real way to measure performance or track progress with accuracy. Everything is reactive and scaling feels like a pipe dream.

2. Diagnostics (basic dashboards, rear-view analytics): the majority of organizations

This is the stage many companies believe is “advanced,” because they have dashboards and reporting tools. But dashboards do not automatically create intelligence. At this level, teams track basic historical metrics like total hires, drop-off rates, cost-per-hire, and top-line performance summaries.

These numbers provide visibility into what happened in the past. They rarely tell you why it happened or what to do next. As Matt reminded the audience, most HR tech was built primarily for compliance, not for optimization or marketing precision. That is why so many teams get stuck here and feel like they are drowning in reports that are technically correct but strategically useless.

3. Prescriptive analytics (AI-guided optimization): 25% of organizations

This is the stage where the power of AI recruitment analytics begins to become real. Teams start to let machine learning guide decision making rather than relying entirely on instinct or manual spreadsheet modeling.

Instead of only reporting results, the system starts suggesting what to do:

• Where should budget be allocated? 
• Which channels are worth scaling? 
• When is a job likely to underperform? 
• What campaign adjustments will improve conversion rates?

This is the first real step from insight into intelligence.

4. Predictive intelligence (the dream state): 2–3% of organizations

This is the summit. The Mount Everest of talent acquisition analytics.

Almost no one gets here, but for the ones that do, the view is completely different.

At this level, organizations can:

• Accurately forecast hiring needs
• Simulate sourcing strategies before spending a dollar
• Model outcomes based on market conditions

They also know when headcount will open six to twelve months in advance and they can predict cost per hire, time to fill, applicant volume, and resource needs with precision.

So, why does this model matter?

Across every stage, one theme keeps resurfacing: maturity drives ROI. As Matt noted, organizations with advanced talent acquisition analytics capability see dramatically higher financial returns that are independent of budget size.

Put simply: the smarter the model, the stronger the outcomes.

Turning insight into intelligence: Where to start in the next 90 days

Once you understand the funnel leaks and where your organization sits in the maturity curve, the obvious questions are:

Where do we go from here?

How do TA leaders take real steps toward predictable hiring rather than simply reporting historical numbers and hoping for the best?

As Carrie said:  

“You can’t optimize chaos. So before you chase technology, you have to fix the foundation.”

Let’s dive into the first 90-day roadmap that turns insight into intelligence and transforms recruitment marketing analytics into something that actually moves the business.

1. Start with data integrity 

Carrie framed it perfectly:

“Trash in, trash out. If you don’t have data integrity, nothing else matters.” 

Before implementing automation, AI tools or advanced measurement models, you need to:

• Standardize how data is captured in the ATS and human resource information system (HRIS)
• Clean up source tagging and naming conventions
• Ensure status codes and outcomes are applied consistently
• Remove outdated automation and redundant fields

This step alone reveals all kinds of surprising inefficiencies, because messy data has a funny way of hiding the real story of what is working and what is falling apart.

And this is also the moment where AI recruitment analytics tends to fall flat for most organizations. That’s because AI cannot magically fix broken input. It simply amplifies whatever you feed it — the good, the bad, and the embarrassing.

2. Move from volume to qualification

Here is where the biggest mental shift must happen.

For years, TA has been conditioned to optimize for:

• clicks
• applications
• impressions
• traffic

In other words: vanity numbers.

The new baseline metric, and the one that changes everything, is:

Cost Per Qualified Applicant (CPQA)

This is where recruitment marketing analytics becomes meaningful, because it finally aligns spend with quality instead of noise.

To adopt CPQA, you must must define:

• What counts as qualified?
• At which stage is the candidate considered qualified?
  • When the recruiter screens them?
  • When they reach the hiring manager?
  • When they are accepted to interview?

This shared definition instantly aligns TA, hiring managers, finance and performance expectations.

Once this metric is in place, decisions become easier, faster, and more strategic.

3. Measure the real funnel, not the theoretical one

As Matt said: 

“It’s not the version your ATS tells you. It’s the version that actually happens.” 

The real funnel looks like this:

Clicks → Applies → Qualified Applicants → Interviews → Offers → Hires

The power is in the conversion rates between those stages and that is where talent acquisition analytics should live.

• Where does the drop-off occur?
• Where are candidates stalling?
• Where are hiring managers bottlenecking performance?

Every percentage improvement represents real money and real time returned to the business.

4. Adopt multi-touch attribution

A candidate may touch five to seven channels before applying:

• Employer brand content
• Job board visibility
• Career site
• Social networks
• Review platforms
• Employee referrals
• Retargeting

So giving full credit to a single source is misleading.

Multi-touch attribution matters because it:

• Reveals what truly influences conversion
• Reduces wasted spending on channels that look good but perform terribly
• Enables smarter allocation decisions

Carrie acknowledged it isn’t easy: 

“It’s a little bit of a pipe dream in TA because of tech fragmentation, but it’s the direction we have to go.”

5. Build a closed-loop feedback system

This is where the real journey into AI recruitment analytics begins. Instead of static reporting, performance data should continuously update targeting rules, media buying, segmentation and optimization.

As Matt said: 

“You feed outcomes back into the system, and it keeps getting better.”

When the loop is functioning properly:

• Best sources automatically receive more investment
• Poor performers are cut without emotional bias
• Hiring models become predictable instead of reactive

Why this Roadmap matters

Once intelligence replaces instinct, the business impact is unmistakable. The ROI numbers prove it, too. 

• 340% ROI within 18 months
• 50% increase in relevant applicants
• 33% reduction in time-to-fill
• Lower cost-per-hire overall

As Matt said plain and simple: 

“AI isn’t magic. It’s math, powered by structure.” 

The business impact also goes far beyond hiring speed:

• Forecasting becomes sharpened
• Workforce planning becomes proactive
• TA becomes an intelligence function rather than a service function
• Finance begins partnering instead of questioning
• Media spend becomes a strategic investment instead of a gamble

And all of this begins with one thing — clarity. 

Because, as Carrie reminded the audience: 

“You can’t optimize what you can’t see.”

This is why recruitment marketing analytics cannot be a reporting exercise anymore. It must be a performance engine.

Final takeaway

Carrie ended with a simple but powerful question — one that every TA leader should tattoo somewhere visible:

“What is your so what?”

So what if traffic increased? So what if CPC went down? So what if applications went up 40%?

If it doesn’t deliver:

• Qualified applicants
• Predictable hiring
• Reduced waste
• Revenue impact

…then it doesn’t matter.

The future of TA belongs to teams who:

• Measure value, not volume
• Fix the internal engine before adding horsepower
• Align recruiter and hiring manager accountability
• Treat candidates like consumers, not commodities
• Build systems that forecast rather than react
• Use AI to optimize, not automate chaos

AI won’t replace recruiters. AI will replace recruiters who don’t know how to use data.

And right now, with only 27% of enterprise employers using advanced talent acquisition analytics, the competitive advantage is still wide open.

But it will not stay open for long.

This is the moment to decide whether TA remains an operational function or becomes an intelligence function.

Because the organizations that act now will:

• Spend smarter
• Hire faster
• Waste less
• And win the talent markets everyone else is struggling in

The opportunity is real, the results are proven, and the window is shrinking.

So, it’s not about “do we need this?” anymore. 

The real question you need to be asking yourself is, “What are we waiting for?”