HR Analytics for employee engagement: Using DCOVA&I to drive higher engagement


Data scientists at Pexitics are approached for HR Analytics consulting when organizations have an HR problem that they want to solve. So was the case, with an organization that used engagement surveys, to measure employee engagement. The organization wanted to find out how they could improve overall engagement levels at their organization. To work on this project, our data scientists employed the DCOVA&I methodology. This is a methodology that helps plan an HR Analytics project from start to finish. It helps data scientists focus on solving the problem, using a rational and effective process that starts with comprehensively understanding the problem and ends with presenting the solution with suggestions for implementation.

The DCOVA&I process, works by converting an HR problem into a mathematical problem. This significantly reduces the margin of error. How can this work for something as prominently qualitative as employee engagement?

Organizations measure engagement by choosing one of two options. The first, is the traditional employee engagement survey, that encourages employees to answer a questionnaire. There is quite a bit of debate around the effectiveness of traditional surveys to accurately measure engagement. In response to this, several organizations have adopted more modern processes that collect data on a more continuous basis through forums and games. Regardless, quite a large quantity of data is collected using both processes which can then be quantified and effectively used for analysis.

This is how the DCOVA&I process maps out during an HR Analytics project such as this:

HR Analytics for employee engagement

Understanding the problem (DEFINE)

The organization had used an employee engagement survey for its 4000 employees. The questions asked in the survey could be divided into four categories: questions related to work, questions related to the organization, questions related to the manager and questions relating to the team. The problem was to find out how to improve overall employee engagement. The data to be used were the responses of the employee engagement survey.

Identifying data that should be included or excluded from the analyses. (COLLECT AND ORGANIZE)

We used associative statistics to figure out, how different survey responses for different questions were related to each other.  Preliminary analysis showed which questions produced strongly related answers. These are the questions that became our key variables. Other questions were dropped.  In the future when the survey is re-administered the Human Resource Manager can replace the questions that are not measuring engagement effectively and thus improve the survey tool itself.

The analyses also checked for degree of variation in the answers from different respondents. This was done through descriptive statistics that helps identify minimum and maximum values. If there was no variation in the answers to any particular question, the data pertaining to that question could not be used for this analyses and such questions were dropped as well.

So finally, questions were dropped from the analyses for two reasons: either because the questions were largely left unanswered or because the answers did not display significant variation.

Closing in on a solution (VISUALIZE AND ANALYZE)

For the final step of the analyses the overall engagement scores of the employees were mapped against the individual engagement scores of the selected questions.  Conditional formatting was used and a graph was generated to understand how strongly or weakly individual engagement scores were related to the overall engagement score.

The pot of gold (INSIGHTS)

The analyses revealed the key drivers of engagement for the organizations. The organizations could now focus on influencing these drivers to improve overall engagement. They drivers that were already scoring high could be influenced to positively to improve the overall engagement score while the drivers that were scoring low could be influenced to improve. Once drivers are identified they can be used to formulate HR policies while generating a deeper understanding of the culture of the organization.

HR Analytics is wide in its scope of application and works to enable evidence based management that can produce significantly positive results at the organizational level.

On July 21st, Pexitics hosted its second webinar to discuss HR Analytics. The webinar focussed on an HR Analytics case study of an organization that wanted to improve employee engagement. The webinar was led by Subhashini Tripathi, Founder and Chief Data Scientist at Pexitics. This above article focuses on the key points of discussion. Watch the webinar recording below:

If you want to learn how your organization can benefit from HR Analytics through Pexitics connect with Subhashini Tripathi at or Reuben Ray at


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