Finding committed employees is one of the top priorities for public and private organizations. Voice of the Employee is essential in that sense. They collect, manage and systematically act on the employees’ feedback on a variety of valuable topics for the company.
The relationship between Engagement and Voice of the Employee is so similar than the existing one between Voice of the Customer and Customer Experience. VoC provides information to improve customer experience. Voice of the Employee promotes employees’ engagement. See: Voice of the Employee, Voice of Customer and NPS
The Voice of the Employee collects the needs, wishes, hopes, and preferences of the employees of a given company. The VoE takes into consideration specific needs, such as salaries, career, health, and retirement, as well as implied requirements to satisfy the employee and gain the respect of colleagues and managers.
Voice of the Employee Resources
VoE programs typically use a variety of methods to collect and analyze employees’ feedback, including surveys like eNPS, exit interviews, performance evaluations, employees’ forum, social networks, or focus groups.
Both, numerical and quantitative studies are essential to conducting a rigorous study, but people usually do not talk in numbers. In order to listen to the Voice of tje Employee, we need to pay attention to what they actually say with their own words.
Thus, it is important to convert the VoE into important ideas and practices so that managers could motivate employees’ engagement, increase productivity and improve benefits.
Organizations often have data overload. That’s why VoE needs to use analysis platforms like MeaningCloud to extract the real and hidden value of the employees’ words. For this purpose, VoE analysis uses text analytics, sentiment analysis, and user profiling APIs. These technologies allow us to listen, analyze and measure information to improve the work environment.
VoE Data Analytics in MeaningCloud
Natural language processing is a real challenge. Natural language is full of ambiguity, polysemy, and synonyms that involve subtle connotations, irony, etc.
Even a text analysis performed by human experts is not perfect because of language ambiguity. Percentage of coincidence between human does not exceed 85-95%.
The quality of a text analysis system depends on the technology and algorithms used as linguistic resources (dictionaries, ontologies, morphosyntactic analysis) that are attached to the discovery process.
Data mining processes involve several tasks such as classification or categorization. It means assigning a text to one or more categories within a defined taxonomy and taking into account the whole content of the source. Moreover, it requires a previous and accurate classification model training of the taxonomy that you’d like to use. Classification is used to identify possible topics in the text.
A classification model contains a list of categories, as well as resources needed to classify the documents within the defined classes. For example, a model could sort the reasons that employees mention in exit interviews.
The quality or accuracy of the analysis is evaluated concerning accuracy (number of detected elements that are relevant) and exhaustiveness (number of relevant elements that are detected). In general, given a particular analytic technology, accuracy and exhaustiveness are antagonists: improvements might worsen the other and vice versa. That’s why the key is to find a balance between both aspects, which is optimal for the application.
MeaningCloud Classification Model for VoE
A classification model contains a list of the categories (taxonomy), as well as the resources needed to classify documents in the defined classes.
The classification process is based on a hybrid process that combines classification algorithms such as Support Vector Machines, which use training texts with linguistic rules to obtain the highest accuracy and best results.
Process workflow is shown in the following figure:
Taxonomy: categories for Voice of the Employee
Reward Reward>Benefits Reward>Salary Reward>Holidays Opportunity Opportunity>Growth&Development Opportunity>Training Opportunity>Feedback&Advice Reward>Benefits Reward>Salary Reward>Holidays Opportunity Opportunity>Growth&Development Opportunity>Training Opportunity>Feedback&Advice Work Work>Collaboration&Cooperation Work>WorkEnvironment Work>Adaptability Work>Autonomy Work>Resource Work>Equipment Work>Process Work>Technology Work>Premises Policies&Practices Policies&Practices>Communication Policies&Practices>CustomerFocus Policies&Practices>Ethics Policies&Practices>CorporateCulture Policies&Practices>Innovation Policies&Practices>ContinuousImprovement Policies&Practices>EmployeeMotivation Policies&Practices>Work/LifeBalance Policies&Practices>CompanyCommitment 35 Policies&Practices>StaffManagement Management Management>DecisionMaking Management>BusinessFocus People People>EmployeeSatisfaction People>Engagement People>Loyalty People>Expertise People>Supervisor People>Co-Worker People>Teamwork People>InformationTechnology People>Department
Thus, each category, for example, Reward>Salary, includes additional training documents that make the algorithm learn for that particular category. Besides, the model includes rules based on linguistic resources that improve accuracy for Reward>Salary.
Many different machine learning algorithms have been applied to Natural Language Processing (NLP). Systems based on machine learning algorithms have many advantages over the hand-made rules. Systems based on machine learning can be more precise by just providing more input data. Research is increasingly focused on statistic models, which take probabilistic decisions based on assigning real weight to each input characteristic. The advantage of these models is that they can express the relative certainty of all possible answers instead of just one, generating more reliable results.
Although NLP algorithms are used, nowadays, linguistic resources are still essential to increase the accuracy of the model. Machine learning algorithms can reach an accuracy threshold of 60-70%.
So it is necessary to use linguistic resources to solve the difficulties of natural language. For example, if the name of a given programming language that the candidate mentions in his curriculum is not included in the resources, it won’t be detected in the analysis. On the other hand, if you’d like to identify what organization’s departments are mentioned, text classification model must include particular categories representing each department.
Likewise, it is essential to apply rules to solve ambiguity problems, polysemy and irony.