Selective, thoughtful rating of your recommended candidates prompts RecruitBot to give you increasingly accurate candidate suggestions.
Overview
In this article we will explore the many aspects of RecruitBot’s star rating system.
Part I: How to Rate Your Search Results
This section will:
Cover where and when to rate candidates
Demonstrate the collaborative aspects of this feature
Part II: The Impact of High and Low Ratings
This section will:
Explore the impact of various (high, low, neutral, or no/”Unsure”) ratings
Teach you how to apply meaningful, accurate ratings
Rating potential candidates with care will help RecruitBot give you increasingly accurate candidate suggestions.
Here's how to do that.
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You have conducted a candidate search and now have a number of prospects to choose from.
Your next step is to rate a couple of viable (and not so viable) profiles to give RecruitBot’s machine learning (ML) a sense of which candidates are (and which are not) suitable matches for the role in question.
Part I: How to Rate Your Search Results
Select a candidate from your search results. With a candidate selected, you will see this ratings feature in the upper-right:
Here you will rate candidates based on how likely you are to reach out to this candidate based on their profile.
After you rate ten (4-5 stars) qualified and ten (1-2 stars) unqualified candidates, RecruitBot will have enough information about your preferences to be able to rate each candidate in your search based on those preferences.
Part II: The Impact of High and Low Ratings
1-2 stars, 4-5 stars, 3 stars, and “Unsure”
The most important criteria to consider when rating candidates are:
Job Titles
Descriptions in job titles ("I did XXX using YYY")
Current and past companies
University and degree type
Skills
1-2 stars
Giving a candidate a low rating means they are not qualified for the role.
This will also prevent candidates with similar profiles from being recommended to you by RecruitBot.
4-5 stars
Higher ratings should be given to candidates who are qualified for the role.
This will ensure that similar candidates will be given priority as you continue your search.
3 stars
This rating feature has no real effect on the machine learning (ML) feature, but still bookmarks the candidate for potential outreach.
Though seemingly neutral, a rating of 3 stars is not equivalent to an “Unsure”.
Unsure
This selection is not considered a rating because it has no effect on ML whatsoever.
Sometimes you’re just unsure (and that’s fine). But don’t be handing out unsures willy-nilly, give careful consideration to sure-ness and if you are truly torn, go for it.
Note that marking a candidate as unsure is a neutral action that does not contribute to RecruitBot’s understanding of your preferences. Be selective. Plan on returning later to your Unsure(s) to make a final decision one way or the other.
