Salary & Market Insights show you how to benchmark pay and make smarter pay choices. You learn to use compensation parsing and salary extraction to compare offers and market data. You run salary benchmarking against industry and role averages to set fair pay. You link skill extraction and skills-salary correlation so you know which skills boost pay. You clean and map job data with named entity recognition, job-title normalization, and geographic salary mapping for accurate regional comparisons. You follow a data hygiene checklist and a quick checklist for comparing offers. This guide gives simple steps to track market trends and skills over time so you can ask for a raise or switch jobs with confidence.
How Salary & Market Insights help you benchmark pay with compensation parsing
Salary & Market Insights give you a clear map when you’re setting or checking pay. Feed pay data from offers, job postings, and payroll to turn messy text into neat fields so you can see where you sit versus the market instead of guessing.
Compensation parsing teaches a system to read salaries like a recruiter: it pulls base pay, bonuses, equity, and perks from job descriptions and contracts. Clean salary extraction makes trend lines obvious — which roles rise, which benefits shrink, and which cities pay more for the same job — so you can make fair, competitive decisions.
Use compensation parsing and salary extraction to compare offers and market data
Compensation parsing turns messy offers into structured data. Upload or paste job descriptions and contracts, and the parser pulls pay, bonus type, stock info, and raise cadence. Then line up offers side by side to see total compensation after benefits and taxes.
Salary extraction helps match offers to market listings. If you have two Product Manager offers in different cities, extraction breaks down total compensation so you compare like-for-like and avoid surprises when negotiating.
Run salary benchmarking against industry and role averages to set fair pay
Benchmarking tests pay against real standards. Run parsed data against industry averages and role-specific ranges to see whether you’re below, at, or above market — and by how much — so you can adjust before you lose talent.
Slice by location, seniority, and company size. For example, a mid-level engineer in Austin might sit at the 60th percentile while the same role in San Francisco sits at the 80th. Those slices help craft competitive offers that fit both the person and the market.
Quick checklist for comparing salaries and offers
- Collect base, bonus, equity, and benefits.
- Normalize pay to annual totals and adjust for taxes.
- Adjust for cost of living when comparing regions.
- Compare percentiles for role and location.
- Check raise cadence and review policy.
- Factor non-monetary perks like remote work, training, and flexibility.
Link skills and trends with Salary & Market Insights for better pay outcomes
To get better pay, link your skills to market data. Salary & Market Insights act like a map and compass: they show which skills command a premium and where demand is rising. That helps you choose smart learning and negotiation priorities.
Match your current skills to job ads and salary ranges. Look for repeated mentions of tools, methods, or certifications in higher-paying listings. When the same skills appear in better offers, invest in training or projects that prove you can deliver.
Use that evidence when asking for a raise or switching jobs. Present comparable roles and pay, show how your skills match those roles, and explain the value you add — turning a vague wish for higher pay into a clear, data-backed ask.
Use skill extraction and skills-salary correlation to see which skills raise pay
Skill extraction pulls common skill names from job posts and resumes. Scan listings, save skill phrases, and count how often each appears in high-pay roles — the more a skill shows up in top-paying ads, the more valuable it likely is.
Skills-salary correlation links those frequent skills to listed or reported salaries. If cloud platforms, data visualization, or security terms appear often in high-pay ads, those skills have strong correlation. That guides what to learn, what to highlight on your resume, and what to practice in interviews.
Track market trend analysis to know when to ask for a raise or switch jobs
Market trend analysis watches how demand and pay for skills change over time. Monitor job post volume, salary ranges, and fill rates. If demand jumps for a skill you have, your leverage grows.
When trends show rising pay or steady high demand for your skill set, act. Gather comparable listings, note salary shifts, prepare impact examples, and either ask for a raise or begin applying. If your employer lags the market, you’ll know whether to push for change or move on.
Simple steps to monitor skills and trends over time
- Set alerts for key skills and save matching job posts.
- Note salary bands and review monthly.
- Run a short learning sprint on one top skill.
- Weekly-check your resume and LinkedIn so you’re ready to move.
Clean and map job data so you can use geographic salary mapping accurately
Accurate salary maps need clean data. Standardize titles and locations before mapping so pay data doesn’t scatter across labels. Clean titles like Sr. Eng vs Senior Engineer first, then map to a common taxonomy so numbers line up.
Geocode locations to a standard format (city, state, country, lat/long). Fill missing city info from ZIP codes when possible. For remote roles, capture the employee’s work location if available. Accurate geo-tags let you compare New York to Austin or London to Manchester without guesswork.
Keep source tags and version history with each record. If you add pay adjustments or convert currencies, record when and why. That audit trail keeps your Salary & Market Insights credible when presenting to hiring managers or leaders.
Apply named entity recognition and relation extraction to clean job titles and locations
Use named entity recognition (NER) to extract titles, company names, and places from messy text. NER finds “Software Engineer,” “Acme Corp.,” and “Seattle” even if typed oddly. Run titles through NER first so you can separate role from team or level.
Then use relation extraction to link entities. Relation extraction tells you which location belongs to which job and whether a level word like “Senior” modifies the title or a team. That precision prevents pay data from getting mixed up.
Use job-title normalization and geographic salary mapping to compare pay by region
Normalize job titles to a standard set before any pay math. Map “PM,” “Product Manager I,” and “Product Mgr” to one canonical title, and optionally map to standard codes (like SOC) so comparisons are apples-to-apples.
Once titles are normalized, group pay by geocoded regions and adjust for cost of living or currency. Compare median pay for the same normalized title across cities and highlight outliers — normalization makes those gaps meaningful instead of noisy.
Data hygiene checklist for reliable salary maps
- Run NER on raw text.
- Geocode every location.
- Normalize titles to a canonical taxonomy.
- Link titles to locations with relation extraction.
- Remove duplicates and flag incomplete records.
- Convert currencies, note conversion dates, and tag sources/version history.
Salary & Market Insights: practical tips for negotiation
- Use parsed, benchmarked data to set a target range before negotiating.
- Highlight skills with strong skills-salary correlation in your examples.
- Show local medians with geographic salary mapping to justify regional adjustments.
- Present total compensation (base bonus equity perks) rather than base alone.
- Keep your data hygiene and sources ready to defend numbers during discussions.
Salary & Market Insights give you the tools and evidence to benchmark pay, track market trends, and negotiate confidently — whether you’re asking for a raise or comparing offers in different regions.