6 Ways to Optimise Recruitment through Data Analytics_Yellow Spark Blog
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6 Ways to Optimise Recruitment through Data Analytics

What would you call a person who completed his doctorate studies in understanding human behaviour but never used it in practical life? While studying this person made many projects and presentations as a part of the curriculum. Conducted intensive research and various experiments, however, never shared any of the information he learnt during the process. All you’d see was an on-point presentation about the given topic. The additional data gathered through the surveys or interviews were catalogued and stored but never used. Would you consider this as a loss of opportunity?

What if I told you that HR professionals could find themselves in a similar situation; especially if they are not reading and analysing the reams of data that is parked with them?

Gathering data and documenting employee-related information is a practice that goes back many years. Smaller companies maintain this data in simple excel formats, while larger companies (those above 100 employees) rely on popular HRIS software. Before data analytics became popular, this data was probably looked at only once a month to process payroll. At max some analysis was done once a year (usually during the year-end) for tax formalities. In recent times, data has become a buzz word, HR analytics appears in the job descriptions of several HR related roles and we have reached a point where data can no longer be ignored.

Data can give invaluable insights if time and resources are spent on analysis. And if (a big if) the management seeks insight to make informed decisions on people related matters.

Has HR missed the bus?

No, not at all. HR departments haven’t ignored the trend. For the purpose of this article, let’s focus on recruitment – they utilise data for knowing things like application completion rate, the time taken to hire, retention rate, sourcing channel effectiveness, employee turnover rate, employee referrals, time to hire, cost per hire etc. While all these are completely important and useful parameters in the entire recruitment lifecycle, there is also more to it.

Here are some of the uncommon ways in which data analytics can help improve your recruitment outcome (provided your employee data is well organised through an HRIS system or maintained in MS Excel):

1. Recruitment funnel effectiveness

This is a very critical aspect that needs to be analysed thoroughly. This one perspective alone can help you optimise your recruitment process drastically. Your company’s ‘Recruitment funnel’ means the various stages that a candidate moves through before he/she reports to work on day 1. For example, it could comprise of simple stages like application awareness, candidates applying for the role, screening, 1st round of interview, final interview, offer roll-out and joining formalities.

When you analyse your recruitment funnel you will know how many candidates move from the application awareness phase to the joining formality phase. This can be done by setting data collection points at each stage. For instance, analysing the number of website visitors turning into applicants can give you the effectiveness of your application awareness stage. Similarly, the percentage of applicants that you choose to call for an interview from those who have applied shows the quality of candidates you are attracting, so on and so forth.

You are doing a great job if the ratio between application awareness and joining formalities is small (say 8:1), but if the ratio is on the higher side (say 30:1) chances are you tend to reject most of the applicants, now that’s a red flag you may want to address and work on. Of course you will have to deep dive to understand where the leak is; basically, where are the maximum drop-outs, what could be the reasons and how do we seal the leak.

2. Application drop off rate

That brings us to this specific parameter, analysing the application drop off rate. A job post for a vacancy can receive anywhere between 15- 500 applications based on the channel you put it on and other T&C associated with it. While most of us consider the application conversion rate, not much attention is given to the application drop off rate.

Application drop off rate is often considered as the opposite of the application conversion rate but there is more to it. Careful analysis of data reveals that the application drop off rate can give you more insights than the application conversation rates. Knowing why a candidate says no or drops off after considering the offer is an important yardstick of knowing your brand reputation.

For instance, you could put a process in place to ask every candidate to give a reason for rejecting the offer, something like how we are asked to give a reason to unsubscribe from a mailing list. You may not always know the real reason, but in this way, you will most certainly know the most popular reason for rejection. Reasons emerging from this kind of analysis can help you improve your strategy, reach and overall impact too.

3. First-year attrition

Another important yardstick to measure recruitment outcomes is to keep a tab on first-year attrition. Attrition in the first year, especially first 6 months is attributed to a bad hiring strategy. Recording data about the number of people joining, the number of people leaving and their reasons for exit can be analytically very valuable.

You can make strategies on changing your approach, hiring techniques, interview questions or even work culture if that is what the employees are complaining about. There could also be a monetary aspect involved where you might be offering lesser than the industry standards or simply have a bad name among the candidate pool. Whatever be the reason, it needs to be identified and most importantly fixed. Having comparative data will help you reduce this attrition and improve the overall efficiency of not only your recruitment process but also your company.

4. Cost of open roles (vacancy)

No one likes vacancies that are not filled promptly in a company. However, the reason could be more financial than professional. Think of every vacancy as a loss of opportunity and money that you have to spend from your pocket. Each new day that a salesperson is not hired, you lose a potential deal that you could have closed. Every day an accounts executive role remains vacant, the workload on the existing resources increases and adds to pending tasks that could have associated costs & repercussions especially, if they are compliance related.

Have a mechanism to record this loss both in monetary as well as on the resource front. Link it to other parameters to identify strategies to reduce the time taken to fill a vacancy. For example, compare the cost of open roles with candidate sourcing channel to know which roles are heavy on your pocket and if there is scope to optimise the sourcing channel. Another example could be to compare the cost of open roles with employee referral rates to correlate which roles are better filled with employee referral and therefore are easy on the pocket.

5. Impact of hire

On one hand, there is a loss of opportunity due to open roles (vacancies), while on the other hand there is a gain with every good (read right) hire. When a right hire is made, the new employee is able to make quick progress and therefore, the company gains from additional skills, resources, contacts and experience that the new employee deploys.

Impact of hire can be calculated by taking some simple parameters into consideration such as number of projects initiated, number of stalled projects completed, number of head counts added, percentage of profits from a new project initiated, etc. Having data recorded for all these factors can help you determine how impactful a particular hire has been.

It may seem like an over the top kind of analysis, but is very valuable to people in leadership roles. With these figures handy it could serves as a great motivational tool for your team. Also keeping this as a benchmark, you can even plot trends and increase the overall productivity of the entire activity as well as the employees involved.

6. Skill Impact

Last but not least, another unique parameter that you can focus on while using data analytics is the skill impact of the candidates you are hiring. If you are hiring for an IT role where the candidate is academically qualified into hardware and networking but lacks the basic MS office skills required for making presentations on network structures. This wasn’t one of the core skills needed but definitely a support skill that could affect the employee morale, productivity and even his/her growth in the company.

A simple skills matrix keeping in mind the company goal will enable you to optimise several recruitment related aspects. Namely, optimise your job descriptions, recruitment process, recruitment promotions, interview process, selection tools, etc. Having a list of comprehensive hard as well as soft skills based on the requirement of the role along with feedback of current employees will help you approach appropriate candidates and improve overall recruiting outcome.

Recruiting outcome is completely a thing you can steer if you leverage the power of data analytics available at your disposal. Don’t shy away from the sheer amount of information you would have to deal with. Instead, consider it as a wealth of knowledge that would help you get quality employees and take your business to new heights. HR analytics is a vast subject, this week we have focused on using data to improve the recruitment outcome, soon we will share our thoughts and ideas about various other HR aspects as well.

 

Yellow Spark can assist you in customising tools and also training you in better use of data analytics through a structured process. You can know more by writing to us at contact@yellowspark.in

Author Profile: Aparna Joshi Khandwala is a passionate HR professional. She co-founded Yellow Spark to work with like-minded people who believe in the power of leadership, which is the only business differentiator in today’s time.

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6 Ways to Optimise Recruitment through Data Analytics
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Published on:  December 5, 2018

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