Candidate Discovery – A New Recruitment Trend!

If you are taking below steps every time you start on new job position then candidate discovery method is for you.

  1. Start sourcing candidates from job boards, social media.
  2. Start contacting recruitment companies or recruitment agencies.

It means you are not able to mine your existing recruitment database efficiently.

Why Recruiters Don’t Utilize Existing Recruitment Database?

Recruiters know they have some of the best candidates sitting in their database. But do you ever wonder why they never find motivation to use that database ever?

Let’s investigate some of the possible reasons.

A. Volume of recruitment database

On an average, companies get 250 applications for every job. This means if you have worked on 10 job  positions, you already have 2500 resumes in your database.  And this database keeps growing every day. Higher the volume, lesser will be the motivation to mine resume database.

Some of the companies we are working with have millions of resumes sitting in their databases!

B. Inefficient Recruitment Technology

The kind of recruitment technology your firm uses will decide the efficiency of a recruiter. There are many factors such as how do they mange their recruitment database? Is it cloud based or on premise recruitment software they are using? To what extent they can perform search in their recruitment database?

  1. If your organization is using ATS (Applicant Tracking System) or Recruitment Software, chances are you are using keyword search. Beside the point keyword search is error prone, keyword search will only give you another pile of resumes. e.g. Let’s say you have 3000 resumes (much smaller number) in your database and you are looking for a full stack developer. You search for terms like javascript, angular js, node js, etc in your database. Let’s say your software gives you 300 resumes. Now what? Do you really think it is wise to scan those 300 resumes, again? Also, remember, searching is not resume screening!
  2. There are many recruiters who use recruitment tracking spreadsheet or candidate tracking spreadsheet, Excel sheets, google forms, google documents to manage their recruitment cycles. These tools could be great for tracking your process but these are simply not meant for data mining!

Candidate discovery strategies can address most of these issues.

Candidate Discovery - Find candidates in existing database
Candidate Discovery – Upcoming Recruitment Trend

What is Candidate Discovery ?

Candidate discovery is a search, match and rank capability that will help recruiter in finding most suitable candidates in their own talent database without even looking out for new ones! It uses machine learning therefore it understands the kind of candidate your organization hires and gives you short list of candidates from existing candidate database.

Candidate discovery is a leading recruitment technology trend seen since 2017.

How Candidate Discovery Works ?

Candidate discovery is a layer of intelligence that is added on top of your recruitment database. It works as given in below steps.

  1. Once you create a job requisition, it scans candidate databases and shortlists candidates based on the candidates you engaged in the recent past for the similar positions.
  2. It ranks these candidates based on your company’s hiring pattern.
  3. You can re-engage these candidates using automated personalized communication as part of your recruitment marketing.

Advantages of Candidate Discovery

Here are some of the key advantages of using candidate discovery approach. It saves time, money and efforts significantly.

A. Find Qualified Candidates Immediately

Imagine finding best qualified candidates in matter of few seconds right after creating a job in the system! These are not only best suitable candidates, but you also may find additional information about these candidates post previous engagements; like if they were willing to move to a different location, their salary expectations, etc.

B. Reduce Duplicate Efforts

It is very common for recruiters to start sourcing resumes immediately after new job position is opened. In the process they end up sourcing same resumes multiple times.  It happens –

  1. Because multiple recruiters working on similar job profiles. e.g. two different recruiters sourcing for two marketing communication openings. These two positions could be in the same organizations but different teams or different departments. There is a high probability that most resumes they would source would be of the same candidates.
  2. Because of the skills overlap. e.g. You are looking for Java developer and then few days later you are working on Java + Angular requirement. We know keyword searches are error prone and don’t be surprised if you end up downloading same resumes again!

Candidate discovery allows you to avoid sourcing same resumes again. Also, if you can filter resumes based on its freshness,  you can focus on resumes that are most recent in the database.

C. Reduce Your Recruitment Cost

Clearly sourcing new candidates every time is an expensive and exhaustive task. Candidate discovery not only saves you from repetitive efforts but also saves large amount of money spent on sourcing efforts. It also helps you in reaching best candidates faster.

Final Thoughts

For years companies, recruiters were looking for recruitment technology that would help them mine existing recruitment database. Today, even existing recruiting softwares like applicant tracking system are adapting to technologies like AI, NLP, machine learning to achieve smart automation. No surprise, candidate discovery is becoming a recruitment trend in recent time and many companies are asking for it.  

Amit Gawande

Amit Gawande

Amit Gawande is a Co-Founder of CVViZ, an AI recruiting software. He has more than 15 years of experience in software development and leading large teams. He has built products using NLP and machine learning. He has recruited engineers, programmers, marketing and sales people for his organizations. He believes in using technology for solving real-life problems.

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