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Propensity to hire predicts the next mandate.

Modelled before the signal even fires.

A propensity-to-hire score does not wait for an event. It blends dozens of weak indicators into one number that says how likely a company is to start hiring soon, before any single signal proves it.

recruiter-lexikon / propensity-to-hire
P
Propensity to Hire
Propensity-to-Hire Score
Defined
Definition

A predictive score estimating how likely a company is to open new roles in the near future, modelled from weak indicators such as funding, headcount trend, leadership churn and past hiring seasonality rather than a single observed event.

At a glance
Term Propensity-to-Hire Score
Used for Predicting hiring intent before it is observable
In boilr Modelled continuously in the Company Brain
b
boilr turns this term into a task
Defined here · operationalised by your AI employee

Propensity to Hire, explained for the desk.

What it is, why it matters, and how your AI employee runs it.

What it is

A propensity-to-hire score is a predictive estimate of how likely a company is to open new roles in the near future. It is built differently from a buying signal, which is a single observed event, a funding round, a new executive, a headcount spike. Propensity is modelled from a wider pattern of weak indicators observed together: funding history, headcount trend, leadership churn and the seasonality a company has shown in its own past hiring. No single input proves anything on its own. Combined, they resemble what other companies looked like shortly before they started hiring.

The distinction is direction. A signal describes something that already happened. A propensity score describes something likely to happen next, often before there is any discrete event to point to. It is recalculated continuously as new data arrives rather than produced once and left to go stale.

A buying signal tells you a company is hiring now. Propensity to hire tells you which company is hiring next.

Why it matters

Waiting for a signal means reacting only after something becomes publicly visible, at which point several agencies usually notice it at the same time. A propensity score moves the moment of outreach earlier, into the window where a company is not yet aware it is about to hire but is already showing the pattern that precedes it. That earlier window is where the quietest, least competitive conversations happen.

The honest caveat is that propensity is a probability, not a certainty, and it only becomes reliable once there is enough history behind both the company and the agency's own past outcomes. A high score means an account resembles others that hired before, not that this particular one definitely will. Used without verification it just points a consultant at more accounts to check; it does not replace judgement, it only moves that judgement earlier.

How boilr handles it

boilr builds a propensity-to-hire score for every company it tracks from the same history it already holds in the Company Brain: funding pattern, headcount trajectory, leadership churn and each company's own past hiring seasonality. The score is not a one-off snapshot. It updates as new data and new outcomes arrive, and it sharpens as your agency's own placements confirm which patterns actually convert into mandates rather than staying theoretical.

Propensity does not replace buying signals, it extends them. An account with an active signal still moves fastest through your task inbox. An account with no live signal but a high propensity stays visible instead of disappearing, feeding into lead scoring so the quiet, not-yet-triggered accounts worth watching do not fall out of view until the signal that confirms them finally fires.

Questions, answered.

Everything a working consultant asks about propensity to hire, and how boilr puts it to work.

What is the difference between propensity to hire and a buying signal?

A buying signal is a single observed event, a funding round, a new executive, a headcount spike, that has already happened. A propensity-to-hire score is a probability built from many weaker indicators observed together, funding pattern, headcount trend, leadership churn, past hiring seasonality, that estimates a company is likely to hire soon, often before any one of those indicators would count as a signal on its own.

How is a propensity score different from lead scoring?

Lead scoring ranks accounts you already know about, combining ICP fit, signal recency, touch history and response likelihood into one priority order. Propensity to hire is one input that can feed into that score: it estimates the underlying likelihood a company will hire at all, which lead scoring then weighs alongside fit and timing to decide what reaches your task inbox first.

Can a propensity score be wrong?

Yes. It is a probability, not a certainty, and it needs enough history behind a company, and behind your own placements, to be reliable. A high score means the pattern looks like accounts that hired before, not that this particular company definitely will. Treat it as a reason to watch an account more closely, not as a guarantee worth skipping verification for.

What data feeds a propensity-to-hire model?

The useful inputs are the ones that move together before a hire: funding activity, headcount trajectory, leadership churn or new senior joiners, and a company's own past hiring seasonality. None of these proves intent alone. Combined and tracked over time, they describe a pattern that resembles what other companies looked like shortly before they opened roles.

How does boilr use propensity to hire in practice?

boilr builds a propensity score for every company in the Company Brain from its funding, headcount, leadership and seasonality history, and keeps it current as new data and outcomes arrive. Accounts with an active buying signal still move fastest, but high-propensity accounts with no signal yet stay visible in your lead scoring instead of dropping out of sight, and the model sharpens as your own placements confirm which patterns actually convert.

Helen Wright
Boilr gave us the BD structure and follow-up support to sign our first client and secure a job brief in under a month.
Helen Wright
Managing Director, 923 Jobs

Spot who hires next, before anyone else does.

boilr models propensity to hire continuously from your Company Brain's history and hands you finished tasks for the accounts most likely to convert. One AI sales employee per consultant, watching for the pattern before the signal fires.