You spend hours benchmarking salaries, running compa-ratios, and aligning with market data. The numbers are solid. The analysis is thorough. But when you share it with your team, something falls flat.

People look confused. Managers field frustrated questions. Employees walk away unsure if they should feel good or worried about their pay.

Here's the thing: it's probably not your data. It's how you're talking about it.

Why Data Alone Doesn't Work

Numbers don't speak for themselves. Without context, even accurate compensation data can create confusion or mistrust.

Think about who's reading your reports:

• Leaders want to know what it means for retention, budgets, and business goals
• Employees want to understand why they're paid what they're paid
• Managers are stuck in the middle, often without the language to explain decisions clearly

If any of these groups walk away frustrated, your compensation work - no matter how good - misses the mark.

Turn Data Into Clear Language

Your analysis can be complex. Your explanation shouldn't be.

Instead of: "You're at a 90% compa-ratio compared to the 50th percentile."

Try: "You're paid slightly below the midpoint of your range, which is still competitive for the market."

See the difference? Same information, but one version actually helps someone understand their situation.

A Real Example: From Confusion to Clarity

Meet Toni, a senior analyst who just got her annual comp review. Her manager sent this email:

"Your new base salary is $95,000. Your compa-ratio is 93% against the market 50th percentile. The range midpoint for your role is $102,000.”

Toni read it three times. She still wasn't sure if this was good news or bad. She worried about being at "93%" and wondered why she wasn't at 100% of the midpoint. She sent a frustrated Slack to her manager asking what it all meant.

Now try this version:

"Your new base salary is $95,000 - a 5% increase from last year. You're paid competitively for a senior analyst with your experience level. You're slightly below the midpoint right now, which is normal for someone two years into the role. As you continue developing more advanced SQL skills in data modeling and presenting to executives independently, you'll have a clear path toward the higher end of the range."

Same data. Completely different experience.

The second version tells Toni:

• Her pay went up (and by how much)
• She's competitively positioned based on her experience and skill level
• Where she sits and why that makes sense
• What she can do to progress

Now Toni feels valued and understands her position. Her manager avoids a difficult follow-up conversation. And your compensation work lands the way you intended.

Other Ways to Simplify the Message

• Lead with the "why" before the "what" - Context first, numbers second.
• Use ranges instead of just percentiles - "Within the competitive range" is clearer than "X percentile".
• Explain what the number means practically - Don't make people translate comp-speak.
• Connect to growth - Show the path forward, not just the current state.

Give Managers the Right Tools

Employees go to their managers with pay questions. If managers don't understand the compensation system, they can't explain it.

Support them with:

• Talking points for common pay conversations.
• Clear explanations of how pay bands work.
• Examples they can personalize.
• Live prep or resources before comp cycles.

When every manager delivers a consistent message, employees trust that the system is fair.

⚠️ Common Pitfalls to Avoid

Dumping data without context. If people don't understand what they're looking at, they'll fill in the blanks - and usually get it wrong.

Overloading your audience. Keep it simple and focused. Less is more.

Forgetting the emotional side. Even when pay is fair, people want to feel valued. Connect data to recognition and growth.

Letting managers wing it. Inconsistent messaging creates doubt. Give them the tools to stay clear and consistent.

Make Your Data Matter

The data is solid. Now it’s time to make the message clear.

Start your next compensation cycle by asking what story the data tells — and if it will be understood.