• Eduardo Valencia

HR & People Analytics: Last 3 reasons to embrace Natural Language Processing in HR.

Updated: Jul 1


In the previous post, I gathered the first 3 reasons to embrace NLP in Human Resources.


Reason number four: Listening to the voice of the employee is a must



Have you heard about the Voice of the Employee? And, if so, are you already listening to the Voice of the Employee and leveraging its value?


For HR professionals, it is essential to listen to what employees say. Voice of the Employee programs systematically collect, manage, and act on employee feedback. The voice of the Employee, or VoE, collects the needs, wishes, hopes, and preferences of the employees of a given company. VoE considers specific needs, such as salaries, career, health, and retirement. It considers implicit requirements too. You will want to understand employees better.


Text-based data sources are a key factor for your organization to understand the "whys" and act on them to make improvements. For example, a survey like the Employee Net Promoter Score may tell you that the degree of satisfaction of an employee is 6 out of ten. The open-text question can reveal that she is not happy with the extra-hours, or with the fact that professional training is not sufficient.


Apart from the open-ended questions we mentioned in Reason number three above, there are other effective ways to gather employee opinions, suggestions, and innovative ideas. Think about the value you can gather from in-company web forums, or from public social networks, employee panels, or even emails, whenever it is possible.



Reason Number Five. Exit interviews point out to necessary improvements.


An exit interview is defined as: "A survey conducted with an individual who is separating from an organization".Most often, this interview occurs between an employee and an HP professional. The HP professional either records or simply writes down the answers. T


The interview is very likely to include questions, such as:" "What are your main reasons for leaving?" "What did you like most, or least, about the organization?" "What, if improved, would have caused you to stay at the organization?" "Would you recommend the organization to others as a good place to work, study, or join?"


An organization can use the information distilled from an exit interview to assess what should be improved, changed, or, what should remain intact. Sometimes, no change is necessary.


More so, an organization can use the results from exit interviews to reduce employee turnover. Or, to increase productivity and engagement. Some examples of the values that can be derived from the analysis of exit interviews include: -shortening the recruiting and hiring process, -reducing absenteeism,-improving innovation, -sustaining performance-And, also, reducing possible litigation.


Reason number six: Automation of resume reading saves a lot of work.

A nifty application of NLP is about the extraction of relevant information from resumes. It definitely saves a lot of work as a recruiter. However, resumes can have many different formats and sometimes it may not be technically possible to fully extract all the relevant information from all these different types of formats.


But one can get started with simple steps and at least extract whatever is possible from some of the known formats. Even this will save a lot of work.


The approach we regularly take with resumes combines Machine Learning and linguistic engineering approaches.



Any AI solution of people analytics should serve to support decision making in human resources, especially in those areas where there is uncertainty or incomplete information. You could build and test your own AI prototype for Human Resources in 3 weeks by leveraging the AI expertise of our team. Test, before you leap!

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