Your engagement survey is complete. Response rates are strong. Some scores are high, others are low. Your leadership team is asking: "What should we prioritize?"

Many organizations make decisions based on intuition: fix what scored lowest, celebrate what scored highest. But not all workplace elements influence engagement equally, and prioritizing without understanding impact often means investing in the wrong areas.

Seeing your scores is easy. Knowing where to prioritize is hard. Here's the method that makes it clear.

You have your engagement survey results. Now what?

Your survey measured multiple factors, such as leadership, culture, growth, satisfaction, and more. Each area received its own score. But knowing the scores is only half the picture. The real question is: which of these areas actually drives engagement when you invest in improving them?

Why scores alone don't tell the full story

To dig deeper into what your scores mean, it helps to clarify what your engagement survey actually measures:

The engagement index is the overall measure of employee sentiment captured through survey questions about attitudes and behaviors that define engagement (like pride, commitment, loyalty, and motivation to contribute). This is your headline engagement score.

Engagement factors are the workplace elements measured in your survey that influence the engagement index. These are the specific areas you'll focus on improving, like leadership, recognition, career growth, and culture.

Most surveys give you scores for both the overall engagement index and each individual engagement factor.

But this is where standard analysis stops short. It reveals correlation (things that move together) but not causation or predictive power (what actually makes engagement go up or down). This creates several costly assumptions:

  • The equal weight fallacy: Treating a 5-point improvement in any factor as equally valuable. In reality, some factors have 5 to 10 times more impact on engagement than others.
  • The lowest score trap: Assuming your biggest problems are wherever scores are lowest. Low scores on low-impact factors are less urgent than mediocre scores on high-impact factors.
  • The spray-and-pray approach: Launching initiatives across multiple factors because you can't confidently prioritize. Resources get diluted, nothing moves significantly, and next year's survey looks remarkably similar.
  • The correlation confusion: Mistaking factors that correlate with engagement for factors that drive engagement. Some factors score high because engagement is high, not the other way around.

Without understanding statistical impact, you're guessing.

How to identify what really drives employee engagement

The solution is shifting from descriptive to predictive analysis. This requires employee survey analysis that isolates each engagement factor's unique contribution to the overall engagement index while controlling for everything else.

Think: If we improve this factor while everything else stays constant, how much will overall engagement increase?

This is where regression analysis comes in.

Understanding regression analysis for survey data

An employee survey regression analysis, also called impact analysis or key driver analysis, uses statistical methods to quantify how strongly each engagement factor influences your overall engagement index.

When you run regression analysis on your survey data, you get a ranked list of engagement factors. Each has a coefficient, or a number that shows the strength of its relationship with engagement.

Some factors emerge with strong coefficients, meaning they have a powerful predictive relationship with engagement. Others have weak coefficients, meaning improvements barely move overall engagement. But understanding which factors drive engagement is only valuable if you know what to do with that information.

Turning key driver insights into strategic action

Regression analysis reveals the impact of each engagement factor, but you need to combine that with your current performance to set clear priorities. Here's how it works:

Regression assigns each factor an impact level (high, medium, and low) based on its predictive power. High-impact factors are your leverage points. Low-impact factors are baseline expectations that matter but don't drive significant change.

Next, the analysis will plot these impact levels against your current survey scores. One proven approach for prioritization uses four strategic zones:

A matrix titled "Prioritizing Engagement Initiatives by Zone." It plots Performance (Low to High on the Y-axis) against Impact (Low to High on the X-axis).

  • High Impact + Strong Performance: 
    Reinforce StrengthsThese factors drive engagement and already perform well. Maintain with minimal effort.
  • High Impact + Weak Performance: Accelerate Opportunity
    Your highest ROI zone. These factors predict engagement but currently underperform. This is where to invest.
  • Moderate Impact: Stabilize and Monitor
    Sustained attention without overinvestment.
  • Low Impact: Baseline Expectations
    Maintain quality but avoid major investment.

This framework transforms regression data into clear priorities, moving you from guessing which initiatives matter to knowing which ones will actually change your engagement numbers.

What makes regression analysis results reliable

To ensure your employee survey regression analysis is reliable, it should validate three things:

  1. Factors are measured distinctly. Survey questions group into clear, separate categories. "Employee recognition" stays separate from "compensation," even though both relate to feeling valued, preventing double-counting.
  2. Patterns hold across your organization. The analysis confirms relationships are consistent across different employee segments. For example, if the analysis shows a "Leadership" factor has high impact overall, that finding should remain true whether you look at your sales team, operations team, or corporate staff separately and not just appear strong in one isolated group.
  3. Relationships are genuine, not coincidental. The methodology distinguishes real predictive relationships from random noise, confirming patterns are consistent rather than due to chance. These checks should happen automatically, ensuring the priorities you identify are always statistically sound.
  4. There is a sufficient sample size. Regression analysis requires a good amount of responses per segment (at least 10 responses for each variable like department or line of business) to ensure statistical reliability. Below this threshold, results can be misleading.

How to run a regression analysis on your engagement survey

Regression analysis requires statistical expertise that most HR teams don't have in-house. You could hire data scientists or purchase specialized statistical software, but these options are costly and time-intensive.

The most practical approach is partnering with an employee engagement survey provider that includes regression analysis in your reporting package. This gives you immediate access to sophisticated driver analysis without building internal capabilities from scratch.

When evaluating survey vendors, look for:

  • Clear pricing transparency on what's included in base packages vs. add-on services for regression analysis
  • Options for multiple regressions by department, line of business, or custom segments
  • Methodology that validates factor distinctness, cross-organizational consistency, and statistical reliability
  • Clear visualization of high-impact vs. low-impact factors
  • The four-zone prioritization framework or similar impact mapping
  • Capability to analyze against custom dimensions like cultural values
  • Regular updates to regression models as your organization evolves
  • Support from psychologists or survey methodologists who can interpret results and guide strategy

For a detailed example of regression-based survey methodology, see WorkTango's approach to engagement survey analysis.

The bottom line

Standard engagement survey analysis shows what employees think. Regression analysis shows what actually drives engagement. Without it, you're prioritizing based on intuition, which often leads to investing in low-impact factors while overlooking high-impact opportunities.

With regression-based prioritization, you make strategic decisions backed by statistical evidence. Your engagement improves faster, your initiatives have measurable ROI, and you can confidently answer "what should we prioritize?"

Ready to identify what drives engagement in your organization? Learn more at worktango.com.

About the author

Danielle McEvoy, Professional Services Consultant at WorkTango, specializes in employee engagement survey analysis. With over 14 years of experience in operations, strategy, and program management, she helps organizations translate data into actionable insights that strengthen culture, connection, and performance. 		 Danielle McEvoy, Professional Services Consultant at WorkTango.