Melisa Guerbi — Services
Twenty services. Nine domains. One rule: every engagement ends in a number that moved. This is what a people-centric business strategist actually does.
“Your workforce is 60–70% of your cost base. I make it read like a financial statement.”
Fully-loaded cost per team and role, cost-of-vacancy, overtime and contractor leakage, cost-of-turnover — one page your CFO signs off on.
Moves: visibility that powers every later decision.
“I find the money already walking out the door.”
Anomaly detection across payroll, overtime, absence, and contractor spend — the quiet six figures nobody owns.
Moves: direct cost recovery, often in week two.
“Most companies measure hiring speed. I measure whether the hires were any good.”
Instruments the funnel end-to-end, then regresses source, interviewer, and process data against 90-day ramp and 12-month performance — revealing which channels and interviewers produce keepers.
Moves: cost-per-quality-hire, ramp time.
“Your org chart hides your best and worst managers. Your data doesn't.”
Team-level analysis of retention, engagement, and output by manager — locating the two or three managers driving disproportionate attrition.
Moves: regretted attrition, at its actual source.
“Your ATS is a database. I turn it into a diagnosis.”
Stage-by-stage conversion analytics: where candidates leak, which stages drag, which sources deliver keepers versus volume, and which interviewers' scores actually predict success.
Moves: time-to-fill, cost-per-hire, offer-acceptance.
“How many interviews does your growth plan actually cost? Almost nobody has done this arithmetic.”
Reverse-engineers your hiring plan into required top-of-funnel volume, recruiter capacity, and interviewer hours per quarter — so you staff the machine before the plan slips, not after.
Moves: hiring-plan attainment, recruiter load.
“Every manager interviews differently — and your offer quality shows it.”
Structured loops, scorecards, and inter-rater analytics that reveal which interviewers predict performance and which add noise, with training built from their own data.
Moves: quality-of-hire, candidate experience, bias risk.
“eNPS is a mood ring until you price it.”
Links engagement scores to team-level retention and productivity, producing your company's own number: what one point of engagement is worth, in dollars.
Moves: the budget conversation, permanently.
“Every new hire is unprofitable until they're not. I find the break-even day — and pull it forward.”
Ramp curves by role from real output data; onboarding redesigned against the curve.
Moves: revenue per FTE, ramp cost.
“Spans, layers, and meetings — the physics of why nothing ships.”
Span-of-control and management-layer analysis plus calendar-load data, with redesign recommendations priced in recovered hours.
Moves: manager capacity.
“I tell you who's likely to leave before they know it themselves — and I do it ethically.”
A flight-risk model built on your HRIS signals — tenure cliffs, comp ratio, manager changes, engagement trend — delivering a quarterly risk register with an intervention playbook. Governed under my AI-governance framework: no black boxes, no automated decisions, a human owns every action.
Moves: regretted attrition, before it happens.
“Your growth plan, stress-tested before you spend it.”
Scenario modeling of hiring plans: cost curves, ramp drag, attrition assumptions, comp compression at scale. For founders, your next board slide; for PE, the people chapter of the value-creation model.
Moves: plan accuracy, burn.
“Your best hires left a data trail. I turn it into a hiring instrument.”
Regresses historical candidate and performance data to identify the traits, backgrounds, and interview signals that actually predicted high performance in each role — replacing gut-feel “culture fit” with evidence. Bias-audited by design and governed under my AI framework: the model informs humans, it never decides.
Moves: quality-of-hire, mis-hire rate, bias risk.
“Compensation fairness, proven statistically — before a lawyer or a journalist does it for you.”
Controlled regression across comp data; compression and equity gaps found and priced.
Moves: legal risk, offer-acceptance, trust.
“Your salary bands were built for the company you were. I test them against the one you're becoming.”
Market benchmarking plus compression modeling: simulates your next 50 hires against current bands to show exactly where the structure breaks — and what the fix costs now versus after it breaks.
Moves: offer-acceptance, internal-equity risk, comp budget accuracy.
“Every declined offer is a data point. Most companies throw them away.”
Analyzes lost offers against comp position, speed, level, and competitor — quantifying what your comp positioning costs in missed hires per year, in dollars.
Moves: cost-per-hire, time-to-fill, evidence-based comp strategy.
“You spend 25–35% of payroll on benefits. I'll show you which third nobody uses.”
Utilization analysis across every plan and perk, cross-referenced with retention and engagement by population — reallocating spend from unused benefits to the ones that measurably keep people.
Moves: benefits spend efficiency, retention per benefits dollar.
“What you pay and what people think you pay are two different numbers. The gap is where resignations live.”
Measures perceived versus actual total compensation value, then closes the gap with total-rewards statements and comp communication — often the cheapest retention lever there is, because it costs communication, not cash.
Moves: retention and offer-competitiveness without raising a single salary.
“Compliance as a risk register, not a binder.”
Full audit across classification, wage & hour, leave, and multi-state or international exposure — every finding scored by probability × dollar exposure, sequenced by what would actually hurt. For PE, this is the people section of a clean exit.
Moves: legal exposure, diligence-readiness.
“Point-in-time audits expire the day after. I instrument compliance like uptime.”
Automated monitoring rules on HRIS and payroll data — misclassification patterns, overtime threshold breaches, missed leave accruals, expiring work authorizations — flagged monthly, human-reviewed, before they become findings.
Moves: audit findings toward zero, permanently.
Every service is named. Every one ends in a number. That's the whole philosophy.
Either. Each service runs standalone (typically 2–6 weeks); most clients start with one, then move to a fractional cadence that bundles several. Diagnostics first, systems second.
Read access to your HRIS/payroll/ATS exports and one hour with whoever owns the data. Most services produce their first finding inside two weeks.
The next conversation
Thirty minutes. Bring your hardest people problem — I'll bring a way to measure it.