People Analytics Segmentation
Segmentation is a fundamental and essential part of people analytics — or any analytics, for that matter. Segments are crucial when it comes to helping you understand and derive insight from your data.
A segment is a grouping of people who share common characteristics. A segment of people can be thought of as one whole unit or as a portion of another unit. For example, the people who work together in an industry form a segment of the total job market. The people who work together in a company form a segment of an industry, and the people in that particular company can be grouped into many much smaller segments.
Some simple examples of segments within a company are division, business unit, location, and job function (such as sales or engineering). People can also be segmented by characteristics that have nothing to do with the company and everything to do with the person — gender, ethnicity, socio-economic status, age, work experience, educational achievement and personality type, for example. People can be described by things that stay the same or things that change — some examples of things that change are years of work experience, company tenure, job tenure, pay, or attitude. There are nearly an infinite number of potential segments for any dataset about people because people can be described in so many different ways.
Using data to observe a group of people with data is like looking at a diamond in the light: The brilliance of a diamond is determined by its number of cuts (or facets) and the clarity of the stone. Like diamonds, companies can be viewed in many different ways. The job of people analytics is to increase perspective and clarity.
Segmenting Based on Basic Employee Facts
As you explore your company’s various systems, such as enterprise resource planning (ERP), human resource information system (HRIS), and applicant tracking system (ATS), you will find many hundreds of different facts about people stored in relational tables. In some cases, administrators input these facts. In other cases, individuals input the facts themselves — self-service, in other words. In other cases, the company actively seeks new facts by distributing surveys or forms. Finally, some facts are generated with no deliberate planning — data is just coincidentally collected by way of other activities or processes — for example email and meeting metadata.
Metadata is simply data that describes other data. Email or Meeting metadata refers to facts that can be observed from the use of company systems — facts that may be useful for analysis. For example, you could potentially learn a lot by analyzing the number of emails sent, number of words in emails, number of unique social connections, number of meetings, average meeting time, concentration of social connections by job function or location, and so on.
In their primary form, the facts collected as data can seem useless; however, in informed hands, the facts can be transformed into useful information.
If you can get beyond the people data basics, you will someday understand that new insights about people are driven primarily by new and richer types of data about those people. People are cognitively advanced social animals who have minds of their own. To understand and predict their behavior, you have to “see” inside their minds — and in order to do this, you have to ask these animals some questions.
Here are a few examples of the many characteristics you can measure by using survey instruments or tests that can open up a whole new world of important insights to you:
- Personality types: Some common personality instruments are the Big Five, the Myers-Briggs Type Indicator (MBTI), and StrengthsFinder.
- Attitudes: Some common employee-survey measures are satisfaction, commitment, motivation, and engagement.
- Preferences: A range of topics can be determined using basic questionnaires or advanced survey analysis tools.
- Technology adoption profile: These factors include innovators, early adopters, early majority, late majority, and laggards.
- Opinions: Survey questions might be designed to measure the likelihood to recommend the company to friends and colleagues or to exit the company for a better opportunity.
Look to survey instruments and tests to help you find important differences between people that help you understand, predict, and influence behavior. These types of instruments can help you develop segmentation that will unlock new insight.
Visualizing headcount by analytics segment
In its most basic use, counting people by segment can help you see the company in new ways. For example, the numbers in the graphs shown here add up to a company’s total head count of 3,100; however, each graph paints a different picture of the company based on the segmentation dimension. These are just six segmentations among hundreds of possibilities.
Analyzing metrics by segment
One of the main reasons to bother with segmentation is to provide a finer-grained (and thus more convincing) analysis of the data you’re using to get to the root of a problem. Here’s an example that illustrates the power of segmentation: Exit Rate % is a metric that measures the percentage of employees at your company who left to go work elsewhere over some specified period.
Exit rate is synonymous with attrition rate, termination rate, and turnover rate.
The formula for Company Exit Rate % is calculated this way:
(Total # Company Exits / Company Average Headcount) × 100
Company average headcount is calculated by counting the number of employees at the beginning and end of a period and averaging, or by counting the number of employees each day of a period and averaging, or by using any other consistent period sampling methodology. For example, you can average headcount over a year by weekly, monthly or quarterly snapshots. The reason to use an average headcount is that if the company is changing (either increasing or decreasing headcount) you will get a different answer depending on what day you count — average headcount standardizes.
If your company’s Exit Rate % is 10 percent, it means that 10 percent of your employees left to go work elsewhere in the time frame of analysis.
When viewing Exit Rate % by segment, you calculate it this way:
= ((Segment # Exits / Segment Average Headcount) × 100)
Segment average headcount is calculated by averaging the number of employees in the segment over the period. You are not dividing segment exits by the total number of employees. You are dividing segment exits by segment average headcount.
For example, if a segment called Segment A had an average head count of 100 people over a year and 20 people left in that year, then the Segment A Exit Rate % = 20%, or ((20 / 100) × 100 = 20). In this example, Segment A has double the exit rate of the average, which is, as I mentioned, 10 percent. This tells you that something may be going on in Segment A.
The following figure shows quite clearly the explanatory power of moving beyond mere Company Exit Rate % and looking at specific segments within a company — Region, for example, or Business Function or Last Performance Rating. It lets you see the percentage of people within that particular segment who left the company during that period, not the percentage of the total population of exits.
The reason for calculating segment exits as a percentage of segment headcount is that it allows a fair and consistent comparison between segments, regardless of the segment’s size. If the calculation is not done as a percentage of segment average headcount, then the larger groups will always show a higher percentage of overall exits — which would tell you only that these were larger groups, not that there was something wrong with them.
When you report Exit Rate % per segment, you can see how much each segment’s exit rate varies from the company average. Clearly, you want to know where each segment is in the range of values. You can use segmentation to identify the segments that require more attention, which helps you move the overall company average the most with the least amount of effort.