HR Analytics

HR Analytics helps call centers to resolve staffing requirements by developing an ensemble model of time-series forecasting (Arima, Arimax, LSTM) along with classification techniques like Boosted Trees. Consideration features, such as call volume, number of operators, length of calls, and other relevant historical call data should allow dynamic models to forecast likely call volumes and estimate staff requirements across particular time periods.

HR Analytics and Employee Attrition Analytics helps companies to unravel deeper factors associated with employee attrition. An integrated Connected Data approach helps bring together disparate data sets, such as employee demographics, current job details, HR engagement, employment history, and recruitment data. Ensemble model of survival analysis techniques and supervised and unsupervised ML engines enable companies to build data-driven predictive & prescriptive analytics to reduce the current attrition rates and reduce the high cost of employee replacement. It helps them to look beyond employee satisfaction and exit interview data and develop intelligence to anticipate probable attrition and suitable mitigation strategies.

HR Analytics uses Natural Language Processing (NLP) techniques to process resumes and job listings and sort resumes by cosine similarity in descending order, to create a robust Application tracking, filtering, and prioritization engine. An automated way of “Resume Classification and Matching” eases the tedious process of fair screening and shortlisting and expedites the candidate selection and decision-making process. This system works with a large number of resumes for first classifying the right categories using different classifiers. Once the classification has been done then as per the job description, top candidates are ranked using Content-based Recommendation, using cosine similarity to identify the resumes that are nearest to the provided job description.

Organizations need to move beyond the current cookie-cutter approach of tracking employee satisfaction through satisfaction surveys and exit interviews to monitor reasons for attrition. There needs to be a conscious effort to introduce newer survey tools and connected data systems to have a more granular & accurate view of employee engagement & inspiration. HR Analytics that use Advanced Data Science models have the ability to reveal deeper facets of employee inspiration and inherent differences with regard to employee profile (Line of service, Experience, Age, Gender, Education & Martial Status). These models should allow Organizations to understand the subtle differences between drivers of satisfaction and engagement vis-a-vis drivers of inspiration derived from diverse employee profiles.