The Applied
sift
: our not-so-secret
sauce to
data-driven
hiring
We share it freely because we want to improve hiring, regardless of whether your organisation uses Applied.
We share it freely because we want to improve hiring, regardless of whether your organisation uses Applied.
The information contained on CVs isnât a very good predictor of whether someone will be good on the job.
Instead, Appliedâs first stage (the sift) uses work sample questions to help you identify the best candidates with data-led outcomes.
Some of these steps can be implemented by yourself, but we do it best đ
STEPÂ 1
Basic personal information such as name, age, race and gender can trigger a reviewerâs unconscious bias. As you donât need this information to assess whether a candidate is great for the job, we remove it up front.
STEPÂ 2
As humans, we often look for information that confirms our existing beliefs, this is known as confirmation bias.
Believing a candidate has made a great point because they've studied at a top university? Confirmation bias.
STEPÂ 3
Order effects happen when we make several judgements one after the other. When hiring, we usually make kinder judgements early on and get progressively harsher as we continue, meaning somebody at the bottom of a CV pile has to be extra special to stand out.
STEPÂ 4
The hiring manager can decide which team members contribute to assessing the candidates. Each reviewer sees one answer at a time and rates it against a marking guide to give it between 1 and 5 stars.
STEPÂ 5
Reviewersâ scores are automatically reconstituted and combined into a leaderboard that shows the best candidates.
Keen to see the sift in action? See this and much more by scheduling a demo with the team or signing up for a free trial.
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