When we discuss analytics it is very easy to get lost in an ocean of data, graphs, and reports. Most ERP software or HRIS (human resource information systems) present a long list of graphs and reports that can be accessed and still we will always have some request to the database admin to extract some or the other data from the backend. And yes this luxury is available to only large firms that use enterprise software and have a database admin resource.

In this article, I am specifically alluding to analytics related to recruitment data. However, the concept could be applied to any sphere of work in an organization.

Approach to Analytics

Analytics basically is arranging or counting data in a specific way to be able to see patterns so as to convert it into information that would help in decision making. So,  if we are to approach analytics with a fresh slate then there are these few steps to be followed-

1. Figure out who in the organization requires to make any decision with reference to or in conjunction with recruitment data.
2. Interview them to understand what data do they need to analyze and how they would want it to be presented. ‘What data to analyze’ is important because you need to ensure that your system is capturing that data. For example, if you want to know the source of an online job application but your system is not capturing this info then you need to fix that.
3. The next important question is what kind of analysis and therefore the kind of inferences that you would like to make from the data. This will dictate the way it is collated and presented. As in the above example, if you wish to capture the source of your application and let us say the applications have come from your careers page on your website, you may actually want to understand how did the applicant come to your career page in the first place. Did she type your company URL in the browser, or did she use google search or did she respond to some job advertisement online? So now this not only tells you the application came from your careers page but also how the applicant found your careers page. You may further like to see this data on a daily, weekly or any other periodicity basis. You could also like to analyze this data with the advertisement spend for that specific job. 

I hope you got the drift about the approach I am suggesting. We will now take up recruitment analytics as our subject and see this approach in action. For ease of listing, we will assume that the above exercise has been conducted and all the data and its analysis have been collated and listed along with the stakeholders to whom it may be useful.

Recruitment Analytics

We shall take up various pieces of the recruitment data and see how it could be analyzed in a way that would be useful to different stakeholders.

• Candidate Database  In this you will like to know-

1. 1. The total number of CVs in the database
   2. How updated the Cvs are. (You will need to capture the last updated date)
   3. Number of CVs Education levels wise
   4. Number of CVs Skils wise.
   5. Number of CVs experience wise

Most good recruitment software would be able to capture the data and display the statistics on a live basis. In my opinion, organizations need to worry about their database size only for their niche skills, for generic skills today there are many job boards and online databases available to hire from and therefore maintaining an updated resume database may be redundant.

1. 1. Jobs Yes, there will be a lot of data related to jobs that need to be analyzed and actioned upon.

• Number of total jobs eneterd in a time period This data will be required location wise, role wise, level wise and need to be integrated with the attrition data to keep a track of manpower expansion vis a vis refills of attrition.

• Turn around time Need to track the time taken from the date a job was entered in the system to the date of new joinee. In fact, time needs to be tracked for every stage of the hiring pipeline. This will enable the leadership to analyze the bottlenecks in the system and the stages which need to be speeded up.  Also over a few years, if the normal attrition levels are tracked along with the time to hire, a proactive approach to hiring may be adapted specifically for critical positions.

• Source of New Hires  This becomes an extremely important piece of information for two reasons. 

1. 1. Expense varies based on source of hiring
   2. Source also defines the quality and speed of hiring

Typically a company will have three sources of hiring

• Direct Applicants  Even here to need to track the source from where the applicants have applied. It could be-

• Social Media ( which one?)

• Company Careers Page

• Job boards

• Print Media Advertisements

• Existing Database ( through email campaign)

This also allows the leadership to decide how much to spend on which medium to get optimal results. 

• Employee Referrals Good referrals from employees not only indicates that your employees are happy working with you, but also that you have designed the referral scheme well. This by far is the best source of hiring.

1. 1. Recruitment Agencies  In this case you need to measure the performance of agencies on the following parameters
      1. Costs of hiring
      2. Speed of hiring
      3. Quality of candidates ( Ratio of presented to selected candidates). This is an extremely important parameter as poor candidates will only end up occupying your internal recruiters time in putting them through the recruitment process.

Tracking such sources and its associated data can enable leaders to set targets regarding the `percentage of hires from various sources.

• Offer Acceptance Rate A low rate here may indicate issues with the hiring process including role definition and compensation negotiations.

• Retention Rate The recruitment team must also keep a close track on the retention rates of new hires. I feel this period must be at least one year for junior and mid-level hires an about two years for senior highers. A poor retention rate indicates (among other things) that there has been a mismatch in the expectation set during the hiring process. A rosy picture may have been painted about the role which is far from reality.

• Gender and Ethnic profile  Important to track this so as to ensure that the organization hiring is well balanced in regards to this aspect. This must not be seen as ignoring merit. However, if all other things being equal a conscious effort must be made to ensure diversity.

• New Hire Performance  This must be mapped for at least the first year after hiring. Most selection processes assess candidates on specific competencies required for the role. Based on performance, some specific competencies will stand out which could predict superior performance. These could be used to refined the competency-based hiring models.

• Candidate online foot print This kind of data gathered from the internet is increasingly being used by recruitment teams to decide on the suitability of an applicant. Your tech platform will need to have the capability to capture this.

• Use of AI to shortlist candidates Much as AI (Artificial Intelligence) and Machine Learning are  trendy concepts nowadays. This will require data from other sources. As in point 7 above competency levels or other information like education levels, previous experience, etc can be fed into the system to automatically shortlist candidates. Based on continuous inputs the logic for shortlisting candidates can improve itself (machine learning).

• Internal Recruiter Performance This needs to be exactly measured the same way as you would asses external recruitment agencies performance as mentioned above.

The above data if captured correctly and in a timely manner can be of immense use to various stakeholders. I have just shared a brief example of recruitment data. Similarly, every piece of data, be it training, performance appraisals, productivity, promotions, transfers, attrition, etc can be tracked and analyzed to enable informed decision making.

Most recruitment agency software may have some part of such analytics but would still require customizations. This is exactly why large organizations have integrated ERPs handly nearly every aspect of the business. However, each organization requires a different set of data at different points in time and therefore need to define its own data and analytics requirements.