Sentiment as a KPI: Gauging Employee Sentiment with Regression Analysis

Quantifying Emotion: Building Index Scores for Human Experience

Employee sentiment analysis, which involves gathering and interpreting qualitative and quantitative data on how employees feel about their work and workplace, provides critical insights that can enhance the effectiveness of employee management. Sentiment analysis tools, such as surveys, text analytics, and natural language processing (NLP), categorize employee feedback into positive, negative, or neutral sentiment.

Regression performance models and employee sentiment analysis together form a robust approach to understanding the factors that influence employee performance and overall organizational success. Regression models, which identify the relationships between variables, can be used to analyze how various aspects of employee sentiment—such as job satisfaction, engagement, and work-life balance—impact performance metrics like productivity, absenteeism, and turnover rates.

These insights help organizations identify which sentiment factors have the most significant impact on performance. By leveraging both regression performance models and sentiment analysis, companies can create more targeted strategies to enhance employee satisfaction and, ultimately, drive better performance across the board.

Goals of Measuring Employee Sentiment

By incorporating sentiment data into regression models, organizations can quantify the effects of employee attitudes and emotions on their performance outcomes. For instance, a positive relationship between high job satisfaction and increased productivity can guide management to invest more in initiatives that boost employee morale. If a regression analysis reveals that negative sentiment regarding leadership correlates strongly with high turnover rates, the organization can focus on improving leadership practices.

By integrating regression models, our goal is to enhance the effectiveness of our people analytics outcomes. Among the most actionable insights:

· Improved Employee Engagement

· Enhanced Decision-Making

· Better Workplace Culture

· Increased Productivity

· Early Detection of Problems

· Stronger Employee-Leadership Relationship

· Targeted Employee Development

· Alignment with Organizational Goals

· Improved Customer Satisfaction

Gathering the Data: Common Survey Methods for Measuring Employee Sentiment

Survey methods are among the most popular and effective ways of measuring employee sentiment. They allow organizations to gather quantitative and qualitative data directly from employees, providing insights into their feelings, attitudes, and experiences.

Here are some of the most common types of employee surveys that can be used to gather data for developing an employee sentiment model.

· Pulse Surveys

· Annual Engagement Surveys

· Employee Net Promoter Score (eNPS)

· Onboarding and New Hire Surveys

· Exit Surveys

· Continuous Feedback Surveys

The Basic of Regression Models

Aspire & Blake, our mock corporation and case study for the employee sentiment performance model, is an American multinational consumer goods company. As of now, Aspire & Blake employs a total of 108,000 people globally.

Below is the output from Aspire & Blake corporate-wide regression presenting data compiled from over 80,000 records.

Table 1 – Regression Beta Scores Using Pulse Surveys with eNPS as Depend Variable

Examining Table 1, we see that compensation and engagement are the most highly weighted attributes, which should not be a surprise. Now we want to synthesize these results into one score for Aspire & Blake.

We achieve this by multiplying each beta by the employees’ combined overall rating and summing the results. Then, we calculate an aggregate mean by combining the regression scores from different corporate offices for the Aspire & Blake organization as a whole.

We now have a score for each corporate office which can be directly compared to the Aspire & Blake corporate aggregate mean to easily see each corporate office relative performance, resulting in the value called the Index. This individual corporate office ratings performance Index is calculated by dividing a corporate office’s regression calculated score by the aggregate mean and multiplying it by 100.

In Table 2, a comparison of corporate office ratings performance, a look at Blue Bell, PA’s employee sentiment index shows that it is doing better than average with an Index of 112.

Table 2 – Aspire & Blake Corporate Location Employee Sentiment Score by Location

To calculate percentiles, Aspire & Blake corporate offices in the sample are ranked from highest to lowest. The top office score is assigned a value of 100, and the bottom office score is given a value of 0. Percentiles are then calculated for all the offices in between.

The percentile shows, at a glance, the relative position of each corporate office. The percentile is particularly useful because it gives the human resource professionals clear, actionable results without them having to know anything about math.

Interpreting the percentile is simple. Chesterfield, MO has a percentile of 44, indicating that 43% of Aspire & Blake's scores are lower, while 56% are higher. It's likely that the human resources department at the Chesterfield, MO branch is not satisfied with this performance.

Application – Employee Sentiment in Aspire & Blake Retail Outlets— NaturaLuxe Cosmetics

High turnover among retail workers is a common challenge for many companies, including Aspire & Blake. Factors contributing to this include low wages, limited career advancement opportunities, unpredictable schedules, and the demanding nature of the job.

Of course, Aspire & Blake is eager to keep employee satisfaction high. Among the reasons:

· Cost Reduction: High turnover leads to increased costs for hiring, training, and onboarding new employees.

· Customer Experience: Experienced retail workers are more knowledgeable about products and services, leading to better customer interactions and satisfaction and continuity in staffing can build stronger relationships with regular customers.

· Productivity and Efficiency: Long-term employees are generally more efficient and productive because they are familiar with store operations, processes, and expectations.

· Team Cohesion: Retaining workers fosters a stable team environment, enhancing collaboration and overall workplace morale. A cohesive team often results in smoother day-to-day operations.

· Brand Reputation: Companies known for treating their employees well and offering opportunities for growth tend to attract better talent and maintain a positive public image.

Aspire & Blake is now focusing on employee sentiment at various direct retail outlets for its custom cosmetics line, NaturaLuxe. In this initiative, Aspire & Blake conducted pulse surveys with retail employees at 150 NaturaLuxe mall locations across the United States. The data from these employee surveys were collected and inputted into the regression model for analysis. Once the scores are calculated, Aspire & Blake examined 6 locations to see how they compare with employee sentiment across all NaturaLuxe employees in the United States

The benefit of this approach is that it allows Aspire & Blake to put the focus on key locations where there is a risk of higher turnover, and thus higher operating costs. Table 3 shows the output of retail employee sentiment at major mall outlets in the eastern United States. All of these among the highest yielding yet employee vulnerable NaturaLuxe locations.

Table 3 –Employee Sentiment Aggregate Scores for Six Major Naturaluxe Mall Outlets in the Eastern United States

Table 4 shows a different application of the same employee sentiment model. Instead of cutting the indices by location, Aspire & Blake is filtering the results by regional managers, each with multiple NaturalLuxe locations within their territory.

Table 4 Employee Sentiment Aggregate Scores for Six Major Naturaluxe Regional Managers in the United States

Overall, Kaplan stands out as the star manager, while Patton-Montgomery has a relatively satisfied team. Gonzalez-Barona is also performing well. The Aspire & Blake corporate office is now aware of the three underperforming managers and can take action to retrain, improve, or replace them as needed.

Employee Sentiment Score – Graphical Display

The Employee Sentiment Score can be effectively communicated through a visual presentation, making it easier for the human resources department and senior management to assess employee satisfaction across different job level tiers within Aspire & Blake. Figure 1 illustrates the sentiment levels across six employee tiers, providing a clear and concise overview.

Figure 1 – Aspire & Blake Indices of Organizational Level for Employee Sentiment Aggregate Means

It's not surprising that the very senior employees are the most content. Considering their smaller sample sizes compared to other tiers and the significant differences in responsibility and compensation, it's expected that they would generally be more satisfied. Reaching the C-suite demands sharp elbows and finely tuned corporate political skills, but the view is wonderful from the top.

At Aspire & Blake, new employees are most likely happy to have a job and start their career. Longer-term employees who have not reached levels of serious corporate muscle have lower sentiment scores. At a glance, the human resources executives should understand that in order to retain people who have been at the company for a period of time, but not advanced up the corporate hierarchy, are at risk to be poached.

Regression modeling offers a powerful tool for measuring employee sentiment, providing valuable insights into the factors that influence workforce satisfaction and engagement. By analyzing data through this method, organizations can identify trends, predict outcomes, and make informed decisions that enhance employee well-being. This approach not only allows companies to address concerns proactively but also helps in tailoring strategies to improve overall employee morale. As businesses continue to prioritize employee experience, leveraging regression modeling