You're hiring for a role. 47 applicants. You spent 30 hours on interviews.
Picked the "perfect" candidate. Great resume. Good references.
Six months later, they quit. "Not the right fit."
That phrase keeps coming up. Wrong fit. Bad fit. Not a fit.
You never defined what "fit" actually meant.
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Fit scoring takes a vague feeling ('this seems right') and turns it into measurable criteria. Instead of trusting your gut on whether something is a match, you define what 'match' actually means. Then you score against those criteria consistently.
The magic is in the definition. When you force yourself to articulate what makes something a good fit, you often discover you've been optimizing for the wrong things. The candidate who 'felt right' had charisma. The one you passed on had the skills you actually needed.
Get it wrong and you waste months on mismatches. Get it right and you can predict success before you commit.
Fit scoring solves a universal problem: how do you compare unlike things against an ideal when the criteria are implicit in your head?
Define your ideal profile explicitly. Extract measurable attributes from candidates. Score each attribute against your ideal. Weight by importance. The result is a number you can compare and explain.
Each candidate has fixed scores. Only your priorities change. Watch how different weights produce completely different "best" candidates.
Try: Max out "Culture Alignment" and minimize everything else. Then flip it.
| Name | Technical | Years | Culture | Availability |
|---|---|---|---|---|
| Alex Chen | 9/10 | 4/10 | 8/10 | 7/10 |
| Jordan Miller | 6/10 | 9/10 | 5/10 | 9/10 |
| Sam Wilson | 7/10 | 7/10 | 9/10 | 6/10 |
| Taylor Brooks | 8/10 | 6/10 | 6/10 | 8/10 |
These scores never change. Only your weights change.
Current winner: Jordan Miller at 73% fit
Make implicit criteria explicit
List every attribute that matters. Assign weights based on importance. A skill that is "nice to have" gets a 2. One that is "must have" gets a 10. Be honest about what you actually need versus what sounds impressive.
Turn unstructured info into data points
Pull structured data from whatever you are evaluating. For a person, it might be skills, experience years, certifications. For a vendor, it might be pricing tier, support hours, integration depth. Make the implicit measurable.
Turn attributes into a single comparable number
For each attribute, score how well the candidate matches your ideal. Multiply by weight. Sum the results. Now you have a number you can rank by, and you can explain exactly why one scored higher than another.
This flow turns "who should work on this?" from a gut decision into a data-driven match. Fit scoring compares project requirements against team member profiles, producing ranked recommendations with clear explanations of why each person is a good or bad fit.
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You scored candidates on years of experience because it was easy to count. Ignored problem-solving ability because it was hard to quantify. Hired based on tenure. Got someone who had repeated year one ten times.
Instead: Include hard-to-measure criteria even if you have to score them subjectively. A 1-5 rating on "problem-solving ability" beats ignoring it entirely.
Your scoring rubric had ten criteria, all weighted the same. Someone scored 10/10 on "professional certifications" and 2/10 on "culture alignment." They got the job. They lasted three months.
Instead: Weight criteria by actual importance. If culture fit matters more than certifications, make that explicit in the weights.
Your best performer has an MBA, so you require MBAs. Your best performer started in sales, so you require sales background. You just described one person, not success criteria.
Instead: Look at multiple successes AND failures. What do successes share that failures lack? That is your profile.
You've learned how to turn vague compatibility into measurable scores. The natural next step is understanding how to use those scores to prioritize what gets attention first.