What are some amazing search hacks on naukri ?

Even though Naukri is one of the main go to job portals in an Indian context and almost all recruiters use this day-in, day-out. But still there is a huge gap on how it is used, I would say less than 2-3% recruiters know how to use Naukri 360*. Below is my reasoning

• Recruiters dont go beyond 2-3 pages on Naukri, even when there is good amount of data coz recruiters believe that, resumes beyond 2-3 pages is Junky, which is partially true. But does that mean, there are no suitable profiles after 2-3 page?
• 70% of recruiters use keywords and 20-25% use basic Boolean. This is one more major constrain on why Naukri’s DB is not effectively used. For example: if you are in france and you can hardly speak french, then your communication might be broken or ambiguous. Similarly, the language of Naukri or any database search engine is Boolean, if you arent mastering it then you do not know how to converse with it.

Tips:

• Whenever you are working on a skill which has 2k+ resumes, then prefer using “Title & KeySkill” search, this will give you more qualitative profiles.
• Use Designation search under Employment details (whenever you are working for roles which are designation driven)
• Use NOT operator to build multiple search models & each search model has to give you new data set, this will make sure you dont end up with same repetitive profiles.
• Bring in BigData concepts in sourcing “Slice & Dice data”. Whenever you have search results beyond 1k+, break it down into small slices. Lets say you are working for 2-4 years exp, break it to 2 – 3 in one search and then 3 – 4 in another ( you can break it down even to 2 – 2.6 in one search if you have large data set). Simple advice is, make sure you dont have more than 400 – 500 search results at any given point of time. This will ensure, you have the best of the resumes for that given exp range on your first page itself. If you dont follow this technique of slicing/dicing, then your good resumes might be beyond 2nd 3rd or more pages, which you will miss out on.

Important: Move from being a JD centric sourcer to a candidate centric sourcer.

Search Samples:

Refer to the below attachment, I have added a few search models which will give you an idea of how to build probabilistic search models and how to use NOT.

Search Models – Naukri