People analytics is “50% cold, hard statistics and 50% common sense”, says Facebook’s Global Head of Workforce Planning, Ross Sparkman. Indeed, there is little doubt that one of the biggest challenges for tech companies today revolves around human capital – improving recruitment processes, retaining workers, increasing productivity and maintaining a high level of motivation. In an era of data-driven insights, People Analytics allows organizations to identify weaknesses and optimize human capital performance based on data and statistics.
According to research by Deloitte, companies that used analytics tools in their HR departments recruited twice as many suitable candidates, retained 3 times as much talent and human capital, and increased their stock value by as much as 30%, as companies whose HR departments didn’t use analytics tools. So it’s no wonder that Forbes stated that in 2017, 67% of American companies started creating databases of people analytics, representing a huge increase from previous years in which only 10-15% of companies saw the need for it.
How can a company collect data to implement People Analytics?
Firstly, HR need to collect and catalog data about their employees on an ongoing basis, in a methodical and organized way, while adhering to GDPR guidelines designed to protect privacy. This might involve saving the data in HR platforms that support these privacy and security standards, password-protecting spreadsheets that contain personal data, etc. NOTE: These tips do not constitute professional legal advice, so you should consult professionals to ensure that your organization’s data collection and storage practices comply with GDPR.
The more data you can collect, the better equipped you will be to understand whether there are any current problems in the company, and even to predict and prevent future issues.
Examples of data you could collect might include:
Employee recruitment, review or departure interviews
Job related data:
- The timeframe between the position opening and being filled
- The number of applications received
- The number of job interviews at each stage of the recruitment funnel
- The number of applicants who withdrew their candidacy
- The number of channels used to recruit for each position.
Employee ‘milestone’ updates: Marital status, sick leave, reviews with the manager or HR, KPI fulfilment, last pay rise, promotion or change in position, etc.
Additional data about employees: Conditions set in the employment agreement, changes in the scope of the position, birthday, attendance, new births in the family, personal studies, participation in company events and activities, etc.
Additional data about the organization: Creation of new departments, senior/executive-level promotions, changes in management, entering into new territories, creating a new office/site, unusual changes in the price of shares/options, salary procedures, and more.
It’s important to gather whatever data is available regardless of whether the organization is large or small. And of course, as more data is accumulated, the clearer the overall picture becomes.
Step 1: Identifying a primary issue
Once an organized ‘people analytics database’ has been established, a company can start using it to check whether there are any potential problems and identify their source. It’s not always possible (or advisable) to attack every potentially problematic aspect of the organization all at once, so the first part of the analysis is defining the primary issue: What is currently the most significant/pressing issue for the company and what do we want to devote most of our attention to?
The data collected can be used to measure internal performance indicators with external benchmarks that might shed some light on how the company is performing compared with the rest of the industry.
For example, if there is a sense that there has been an unusually high number of employees leaving the company recently, you can dive into the data that’s been collected to check whether this ‘sense’ is valid:
Is there really an increase in the attrition rate compared with previous months or years?
Is the rate of attrition in your company higher than in other companies?
If it becomes apparent that there is in fact, an increase in staff departures, then the data might also help to pinpoint the source of the phenomenon:
Are the departing employees mostly of a particular level of seniority/talent?
Is there a department that’s experiencing a higher attrition rate than others?
Where are the departing employees going after they leave?
Is there a pattern where departure might be occurring after a certain number of months/years at the company?
Step 2: In-depth analysis
Once you have identified the primary issue and understand whether there is in fact a problem and whether it is localized or cross-organizational, there are other insights you can use to gain a more holistic view of what’s going on in the organization. In the example above, where there is a high rate of employee departures, there are other details that can be looked at to understand the root of the problem:
1. The recruitment process of the departing employees:
Were there any special difficulties during the process?
Were any doubts raised about their employment or suitability?
Were expectations with their managers set clearly and adequately?
How was their onboarding process?
Answering these questions and identifying certain patterns can help improve the recruitment process and prevent unsuitable hires in the future.
2. The salaries of the departing employees:
Did the employees receive a salary that’s on a par both with industry and company standards?
Does the Compa-Ratio (comparison of the salary paid to employees versus the market midpoint for similar positions at other companies) indicate differences in salary compared with industry standards?
Did the departing employees not receive any pay rises in recent years?
Answering these questions will help to raise any red flags and perhaps help to prevent the future departure of employees that are important for the company to retain.
3. Details about attendance of the departing employees:
Has there been a change in the personal status of these employees?
Have there been any changes in patterns relating to vacation days or sick leave?
Have there been any changes to the work hours or days and if so, why?
Have different patterns and irregularities relating work hours and days been observed in different departments within the organization?
Answers to these questions will also help to predict future employee departures.
4. Departure levels in relation to organizational changes:
Is the attrition rate higher after a ‘wave’ of senior-level departures?
Do most of the departures occur after organization-wide salary changes?
Answers to these questions can also help create preventative measures.
Step 3: Identifying patterns and creating predictive models
Once all the aspects pertaining to the primary issue have been reviewed, you can start looking for a correlation and potential causes by looking at how the data you accumulated influences employees when they leave or join the company, compared with the behavior of current employees at other points in time, where staff turnover was considered more “normal”.
Again, using our example above, you can look at the data to ask the following questions about departing employees:
Did they take more vacations or sick leave prior to leaving?
Was there a change in their personal status a few months prior to leaving?
Were they promoted relatively late compared with other employees?
Were there more departures by employees whose salaries were lower than average?
Did their recruitment process take longer, or was there a higher tendency to complain about mismatched expectations from their managers?
Was there an anticipation of staff turnover due to company-wide changes affecting salary, office environment, etc.?
Is there a correlation between employee attendance at work and any recent company-wide changes?
Were any differences in attrition rate observed between departments and can they be explained?
Answers to these questions can be used to detect patterns and draw conclusions that can help predict changes in the organization’s workforce and update yearly plans accordingly.
Step 4: Planning and preparing relevant programs and strategies
Drawing conclusions based on people analytics, is not a solution in itself. For the resulting insights to have any kind of productive effect you must actively consider them when preparing annual plans and build them into the relevant company strategies. Using our example, the patterns identified can be used to optimize recruitment processes, retention programs, preparation ahead of major organizational changes, etc.
People Analytics isn’t “magic”. Its effectiveness relies on the efficient collection of data and on adhering to the four steps outlined above: Identifying the primary issue/s, holistic research and analysis of the employees’ circumstances and behavior, identifying patterns, building predictive models, and finally, using all of it to create action items, and optimize the relevant policies and processes. If you keep an open mind and are willing to dive deep into the data, you may even find that the answers are already right there in the organization, just waiting to be uncovered.