Salary Research Tools: Knowing Your Worth Before Negotiating

"What are your salary expectations?"

You panic. Too low and you leave money on the table. Too high and they might move on.

Here's the truth: Whoever names a number first usually loses. But if you're forced to answer, you better know your market value—not guess.

The cost of bad salary research:

• Accept $10K below market → Lose $470K over 30 years (compounding)
• Ask for $20K above market → Employer moves to next candidate
• No data to back up your ask → Employer lowballs you

The benefit of good salary research:

• Walk into negotiations confident
• Anchor discussions at the right range
• Back up your ask with data
• Spot lowball offers before accepting

This guide shows you the best tools for salary research, how to use them effectively, and how to triangulate data for accuracy.

Why Salary Research Matters

1. Employers Have Data—You Should Too

Companies use compensation surveys and market data. Don't negotiate blind.

2. Salaries Vary Wildly by Company, Location, and Level

"Software Engineer" at a startup in Austin ≠ Google in San Francisco

3. It Gives You Confidence

"Based on my research, market rate for this role is $120-140K" sounds much stronger than "Um, I was hoping for around $100K?"

💡 Pro Tip: Spend 2-3 hours researching before any salary conversation. It's the highest-ROI prep you can do.

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The Best Salary Research Tools

1. Levels.fyi (Tech Industry)

Best for: Software engineering, product, design, data roles at tech companies

What it shows:

• Total compensation (base + bonus + equity)
• Breakdowns by company, level, location
• Real data submitted by employees

How to use it:

1. Search your role (e.g., "Product Manager")
2. Filter by company, location, years of experience
3. Look at median and range (not just average)
4. Note equity vs. cash breakdown

Example:

Product Manager, L5 at Google, San Francisco

• Base: $180K
• Bonus: $30K
• Equity: $120K/year (vesting)
• Total comp: $330K

Pros:

• Most accurate for tech
• Shows total comp, not just salary
• Updated frequently

Cons:

• Limited to tech industry
• Skews toward big tech companies

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2. Glassdoor Salaries

Best for: All industries, wide range of roles

What it shows:

• Base salary estimates
• Salaries by company, location, job title
• Employee-reported data

How to use it:

1. Search "[Job Title] salary at [Company]"
2. Review salary range and distribution
3. Filter by location and years of experience
4. Read employee reviews for context

Example:

Marketing Manager at HubSpot, Boston

• Base salary range: $85K - $115K
• Average: $98K

Pros:

• Covers all industries
• Company-specific data
• Free

Cons:

• Self-reported (can be inaccurate)
• Doesn't always show total comp
• Sample sizes can be small

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3. Payscale

Best for: Detailed compensation reports across industries

What it shows:

• Base salary + benefits value
• Adjusted for location, experience, skills
• Percentile distributions (25th, 50th, 75th)

How to use it:

1. Enter job title, location, years of experience, skills
2. Review personalized salary report
3. Note where you fall in the distribution
4. Use filters to refine (company size, industry)

Example:

Data Analyst, 3 years experience, Chicago

• 25th percentile: $62K
• Median: $72K
• 75th percentile: $85K

Pros:

• Highly customizable
• Adjusts for your specific profile
• Good for non-tech roles

Cons:

• Full report requires paid account
• Can be conservative (lower than market)

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4. LinkedIn Salary

Best for: Benchmarking by location and company

What it shows:

• Median base salary
• Salary by location, experience, company
• Insights from LinkedIn's member data

How to use it:

1. Go to LinkedIn Salary tool
2. Search job title and location
3. Review median and distribution
4. Compare across cities or companies

Pros:

• Large dataset (LinkedIn members)
• Easy to compare locations
• Free

Cons:

• Base salary only (no bonus/equity)
• Less detail than Levels.fyi

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5. Blind (Tech Anonymous Forum)

Best for: Real, unfiltered comp conversations

What it shows:

• Employees sharing exact offers/comp
• Negotiation experiences
• Company-specific insights

How to use it:

1. Download Blind app (requires work email)
2. Search "[Company] salary" or "[Role] offer"
3. Read recent threads about compensation
4. Ask questions anonymously

Pros:

• Brutally honest
• Real-time negotiation advice
• Company culture insights

Cons:

• Requires work email
• Can be pessimistic/negative
• Mostly tech-focused

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6. Industry Salary Surveys (Specialized)

Best for: Specific industries (finance, consulting, healthcare, etc.)

Examples:

• Robert Half Salary Guide: Finance, accounting, admin
• Dice Tech Salary Report: Technology roles
• SHRM Compensation Data: HR roles
• Mercer Compensation Surveys: Various industries

How to find them:

• Google "[Your industry] salary survey 2025"
• Check professional associations (AMA, IEEE, etc.)

Pros:

• Industry-specific
• Often includes benefits data
• Credible sources

Cons:

• May require membership
• Published annually (can lag)

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How to Triangulate Data for Accuracy

Don't rely on one source. Use 3-5 tools and find the overlap.

Step 1: Gather Data from Multiple Sources

Example: Product Manager, 5 years, Austin, TX

Source	Base Salary	Total Comp
Levels.fyi	$130K	$180K
Glassdoor	$110-140K	N/A
Payscale	$115K	N/A
LinkedIn	$125K	N/A
Blind (threads)	$120-135K	$170-190K

Step 2: Identify the Range

Look for where most data overlaps.

• Base salary range: $120K - $135K
• Total comp range: $170K - $190K

Step 3: Consider Context

• Company size: Startups pay less base, more equity
• Location: SF/NYC pay 20-30% more than Austin
• Company stage: Public companies pay more cash, less equity risk

Step 4: Position Yourself in the Range

• Below market (25th percentile): If you're stretching for the role
• Market rate (50th percentile): Standard fit
• Above market (75th percentile): If you're a strong candidate with competing offers

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Adjusting for Location, Experience, and Company

Location Multipliers

Salaries vary by cost of living and market demand.

Rough multipliers (relative to national average):

• San Francisco / NYC: 1.3-1.5x
• Seattle / Boston / LA: 1.2-1.3x
• Austin / Denver / Chicago: 1.0-1.1x
• Mid-size cities: 0.9-1.0x
• Small cities / rural: 0.7-0.9x

Example:

National avg for role: $100K SF adjustment: $130-150K

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Experience Adjustments

Entry-level (0-2 years): Lower end of range Mid-level (3-5 years): Middle of range Senior (6-10 years): Upper end of range Lead/Principal (10+ years): Top of range or above

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Company Size & Stage

• Startups (<50 employees): Lower cash, higher equity risk
• Growth-stage (50-500): Moderate cash + equity
• Large public companies (500+): Higher cash, lower equity upside
• FAANG/top tier: Top of market for everything

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Common Mistakes to Avoid

❌ Mistake #1: Using Only One Source

Why it fails: Single data point can be misleading.

Do this instead: Use 3-5 sources and triangulate.

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❌ Mistake #2: Comparing Base Salary Only

Why it fails: Ignores bonus, equity, benefits.

Do this instead: Calculate total compensation for accurate comparison.

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❌ Mistake #3: Not Adjusting for Location

Why it fails: $120K in SF ≠ $120K in Austin.

Do this instead: Use location-adjusted data or apply cost-of-living multipliers.

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❌ Mistake #4: Anchoring on Outdated Data

Why it fails: Salaries change, especially in hot markets.

Do this instead: Use data from last 6-12 months. Check "Date posted" on salary reports.

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❌ Mistake #5: Ignoring Your Unique Value

Why it fails: You might be worth more if you bring rare skills/experience.

Do this instead: Use market data as baseline, then adjust up for unique strengths.

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Using Your Research in Negotiations

When Asked: "What are your salary expectations?"

Deflect (if possible):

"I'm more focused on finding the right fit. What's the budgeted range for this role?"

If pressed, give a range based on research:

"Based on my research for [role] in [location] with my experience, I'm targeting $120-140K base. But I'm flexible depending on total comp, equity, and growth opportunities."

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When You Receive an Offer

If it's below your research:

"Thank you for the offer. Based on market data for this role [cite source], I was expecting closer to $[X]. Is there room to adjust the base or total comp package?"

If it's at market:

"The offer is aligned with my research. I'd love to discuss [equity/bonus/signing bonus] to close the gap."

If it's above market:

"This is a strong offer. I'd like to review the full package and get back to you within 2 days."

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Key Takeaways

1. Use multiple tools: Levels.fyi, Glassdoor, Payscale, LinkedIn, Blind
2. Triangulate data: Don't rely on one source; find the overlap
3. Total comp matters: Base + bonus + equity + benefits
4. Adjust for context: Location, experience, company size/stage
5. Stay current: Use data from last 6-12 months
6. Position yourself: 25th/50th/75th percentile based on your fit
7. Use data in negotiations: "Based on my research..." gives you credibility

Next Steps

1. Spend 2-3 hours researching using 3-5 tools
2. Create a comp table with data from each source
3. Identify your target range (base + total comp)
4. Adjust for your context (location, experience, unique skills)
5. Read our guide on Offer Negotiation to use this data effectively

Remember: Employers expect you to negotiate. They respect candidates who know their worth. Do your research, know your range, and enter salary conversations with confidence. The 2-3 hours you invest now could yield tens of thousands of dollars—or more—over your career.