Compensation Benchmarking for B2B SaaS | Maya by Clozure

Replacing a senior engineer costs 1.5x their salary. Maya runs sourcing, scheduling, and screening end-to-end — with a 4.2-week median time-to-hire. But compensation benchmarking is where she really changes the game: she scrubs 47 data sources, normalizes roles by level, and surfaces pay gaps before they cost you a top candidate.

The Compensation Benchmarking problem most teams have

You’re running compensation benchmarking manually — and it’s bleeding you dry. Here’s what that looks like:

• 14 hours per cycle spent pulling data from Radford, Payscale, and LinkedIn — then reconciling mismatched job titles and levels. That’s $2,100 in People team salary per cycle, wasted.
• 23% of offers get rejected because your comp band was 12% below market — and you didn’t know until the candidate walked. Each rejection costs you an average of $4,200 in recruiter time and lost momentum.
• 1 in 3 employees who leave in their first year cite “pay not competitive” as a factor. Replacement cost: 1.5x salary — $150,000 for a $100k role.

Manual benchmarking isn’t just slow. It’s actively eroding your margins and retention.

How Maya owns Compensation Benchmarking end-to-end

Maya doesn’t just run reports. She owns the full cycle — from data ingestion to offer recommendation — so your People team can focus on strategy, not spreadsheets.

• Sourcing — Maya ingests live market data from 47+ sources (Radford, Pave, Levels.fyi, public filings, and job boards). She maps every role to a canonical title and level, so you’re comparing apples to apples.
• Candidate scoring — When a candidate enters the pipeline, Maya cross-references their expected comp against your benchmarked bands. She flags any mismatch before the first call, and suggests a range that’s competitive and within budget.
• Performance check-ins — Maya tracks actual comp vs. performance data from your quarterly reviews. If a high-performer is below the 50th percentile for their role, she surfaces a retention risk alert — with a suggested adjustment.
• Exit interviews — When someone leaves, Maya analyzes their comp history against market. She reports back: “This person was 8% below market for their level — and 3 peers are in the same band.” You act before the next resignation.

A concrete Maya workflow

Scenario: Acme SaaS (200 employees) is hiring a Senior Product Manager in Austin. The manual benchmark from Q1 showed a band of $140k–$160k.

BEFORE: The People team spent 3 days pulling data, manually adjusting for Austin cost-of-living, and emailing three different data vendors. They missed that the market had shifted +9% in Q2. Final offer: $150k. Candidate declined for a competitor paying $168k. Cost of re-opening the search: $12k in recruiter time + 5 weeks delay.

MAYA'S ACTIONS:

1. Maya sources live data — Radford, Levels.fyi, and 5 competitor job posts for the same role in Austin.
2. She normalizes the level: “Senior PM II” at Acme = “Product Manager III” at market.
3. She calculates a new band: $158k–$172k, with a recommended offer of $165k.
4. Maya cross-references the candidate’s expected comp ($170k) and flags a potential gap. She suggests a sign-on bonus of $10k to close the gap.
5. She auto-generates the offer letter with the approved band. No back-and-forth.

AFTER: Offer accepted in 3 days. Time-to-hire: 4.1 weeks. Maya saved 14 hours of manual benchmarking work and prevented a $12k re-open cost.

Why Maya wins vs. hiring

Hiring a human VP of People is the right move for strategy and culture. But for the benchmarking work? Maya augments them — hard.

Maya doesn’t replace your VP of People. She gives them a tireless analyst who never sleeps, never forgets, and never asks for a raise.

What would Maya save your team?

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