Recruitment Analytics: How Data Helps Achieve Better Results

Posted on: February 21 2019, By : NEHA KENI
Recruitment Analytics: How Data Helps Achieve Better Results
Large associations regularly get countless job applications every month and manual screening of such vast candidate volumes is bulky, error-prone, and costly. Indeed, one recent study suggested that human recruiters spend only 6 seconds reviewing an individual resume and 80% of this time is spent on only six data points: name, current title/company, current position start and end date, previous title/company, previous position start and end date, and education. In using a sequential selection procedure, manual screening may select the first few candidates that match the job requirements, thereby missing potentially better candidates.  Many organizations are opting for the services of best hr consultancy in India in order to drive their employees optimistically towards achieving the objectives of the organizations.
It is becoming evident that recruiting candidates that are well-matched to the job, that are of high quality, that are likely to accept offers, and that are likely to stay for the long-term is critical not only for cost savings but also for the continuous well being of an organization. With the increasingly large numbers of applicants causing human screeners to suffer information overload, the use of automatic decision support through data mining and business analytics is becoming necessary. Recruitment analytics system can learn statistical rules that consider tens of data points in a candidate's profile rather than just the six that human screeners typically consider. Further, such a system can simultaneously compare thousands of candidates, rather than simply going with the first few that pass a threshold.  One might think such a system can only work with structured categorical data but in fact they are able to use text mining algorithms to make use of unstructured text from resumes as well.
Working Of Recruitment Analytics
To provide greater insight into recruitment analytics, let us consider a typical simplified system architecture. Inputs include candidate resumes and structured data, as well as associated job descriptions.  The analytics layer implements the various data mining algorithms including text-based technical match for the several candidate facets, and rank aggregation to merge rankings along the several facets.  The aggregated ranking is presented to the HR practitioner through an interactive user interface portal, which supports role-based access protocols so that HR management course in Mumbai practitioners and interviewers can only view and act on applications for which they are authorized.
The keystone of the recruitment analytics system is the analytics layer comprising data mining algorithms that operate on a detailed view of candidate profiles. It make use of many typical features from resumes and structured candidate data, such as experience, education, and skills. When trying to rank candidates on facets of quality, onboard, and attrition, we learn features of candidates that are predictive using historical training sets that provide a binary label for whether a given candidate passed or failed a particular stage in the human resource management lifecycle.  The nature of the training data leads us to use bipartite ranking algorithms.  The goal in bipartite ranking is to minimize the number of disagreements (positive/negative misorderings) among pairs of ranked samples, but to reduce computational complexity
Recruitment Analytics has made possible to developed and deployed a system to aid human resource practitioners in achieving more informed and better decision making in the recruitment process.  The system extracts key features from candidate resumes and structured data; prioritizes applications on a joint criterion that considers several facets such as technical match to job requirement, quality, likelihood of joining if offered, and likelihood of early attrition if joined. The automation of things like Core HR payroll, salary records and benefits has taken away much of the work, and freed up HR pros to focus on more strategic initiatives and analytics. In terms of hiring, there is also a recruiting funnel in which candidates follow similar stages from awareness to hiring. Effective recruitment analytics require a proper data collection, an application funnel with a logical flow and optimized stages, as well as a focus on the right numbers. Not all metrics are equally valuable, some of them might be even misleading. Organizations need to focus on data that will let them make better decisions and improve ROI. The right tools can help managing data. A programmatic recruitment platform helps to analyze data to implement better buying decisions. These insights are leveraged to optimize job ad placements for the best performing recruitment channel mix for the particular organization.
The following metrics can help improving results:
Volume of applicants: Number of applicants entering the top-of-funnel stages. This will inform about the effectiveness of the application website, branding and sourcing initiatives.
Volume of applicants per funnel stage: Observing the number of candidates per stage will help to identify possible friction points and opportunities for optimization across the process.
Volume of hires: Amount of hires actually starting to work. The ultimate goal of the recruitment process is to achieve an optimal volume of hires with a high retention rate.
Cost per Lead (CPL): Cost of a job seeker landing in the application funnel
Cost per Applicant (CPA): Cost of a job seeker performing some sort of action within the application funnel (e.g. contact info submit; background check submit; etc.)
Cost per Hire (CPH): Defined as the sum of all recruitment costs-internal and external, divided by the total number of hires during a certain period. Internal costs are all the costs of the recruitment process inside the company (HR staff, organization, capital), whereas external costs are all the expenses related to external vendors involved in recruitment.
Lifetime Value (LTV): This metric is very specific to each business as every organization has its own definition of quality of hire. However, some measurements are persistently used such as performance levels, time to productivity of new hire and retention rates, among others.
By carefully tracking and optimizing these metrics, the recruitment process will constantly improve, helping organizations meeting their business goals. HR (Hr training in Mumbai) professionals need to have a thorough understanding of their organization’s business goals. This will let them define their application funnel and optimize the recruitment process as a whole. Choosing a recruitment platform, with true programmatic recruitment capabilities, can give organizations a deeper understanding of data across the entire application funnel. The resulting insights can help to achieve recruitment goals while improving the bottom line. To become pace with HR Disciplines you can take help of many Top Education Consultant in India who guides you for career.
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