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Methodology

Where our numbers come from, including the ones we are not sure about.

Most tools in this space ask you to trust a score. This page is the opposite. It sets out what Accolgo measures, the published research each choice rests on, the places where we made a judgement call instead, and the claims we deliberately do not make.

What we measure

Four things, chosen by other people, not by us.

A 2025 meta-analysis in the Journal of Management pulled together decades of research on how new starters settle, and identified four things that consistently predict whether someone stays: whether they feel accepted socially, whether the role is clear, whether they are mastering the work, and whether they feel they fit. Every question Accolgo asks maps onto one of those four.

That matters because we did not invent the framework. If you disagree with what we measure, you are disagreeing with a body of published research rather than with our opinion, and you can go and read it.

Do they feel they belong

Whether they feel part of things and can speak up. One of the two strongest signals we track.

Do they feel they add value

Whether the job feels like a fit and their work matters. The other of the two strongest.

Is the role what they expected

Whether the job matches what they were told at interview.

Are they set up to do it

Whether they have the tools, access and support. The weakest of the four as a predictor of leaving, and we say so below.

Not all four are equal

We weight them by how strongly each one actually predicts someone leaving.

The same meta-analysis reports how strongly each of the four relates to somebody intending to leave. They are not the same, and treating them as if they were would waste a manager’s attention. So belonging and feeling valued raise a flag sooner than the other two do.

Feeling set up is the weakest predictor of leaving, roughly half the strength of the strongest. The logical move would be to quieten it. We have not, on purpose: a set-up problem is the cheapest thing on this list to actually fix, and the cost of missing someone is far higher than the cost of an unnecessary conversation. That is a judgement, not a finding, and we would rather say so than dress it up.

Two things most systems miss

The senior hire, and the slow fade.

Senior people understate how they are doing. That is a well-evidenced bias in workplace self-reporting, and it matters most where you can least afford it. So a senior hire’s answers are read on a tighter setting: a smaller dip is enough to raise it, because the same words mean something different coming from them.

How much tighter is our own judgement call. The research supports the direction confidently and gives no number for the size, so we chose one and label it as a choice.

The slow fade is the one that costs you. Someone who drops sharply usually has a reason, and usually tells somebody. Someone who eases down a little every week for two months tells nobody, never scores badly, and never has a bad week that anything would notice. Accolgo compares each person against how they were when they started, not just against last week, which is the only way that person shows up at all.

What we do not claim

We do not read a retail business differently from an insurer.

We looked hard at whether we should. Sectors genuinely do differ: we checked it in three independent public datasets, from the United States, Hungary and across Europe, and they agreed closely on the size of the gap between hospitality and finance.

But it is a small effect next to what actually moves these numbers. Industry explains under two per cent of the variation in how satisfied people are at work. Which employer someone works for matters about three times more than which industry they are in. And comparing a person against their own earlier answers, which is what we do, cancels out a group difference anyway.

So we record your sector and use it to show you how your numbers compare with published figures for organisations like yours. We do not pretend it changes how we read an individual, because it does not, and a claim like that would fall apart the first time somebody checked it.

The limitation you should ask us about

Nobody has yet proved our flags predict who leaves.

Every threshold in Accolgo is a reasoned starting point, calibrated against published research on what makes new starters stay or go. None of them has yet been tested against what actually happened to the people in this system, because there is not yet enough of that data to test it against.

We think that is the single most valuable thing this product can do next, and it is why the thresholds are written to be corrected rather than defended. If a competitor tells you their model is proven, that is worth asking them the same question about.

Sources

Everything above, traceable.

  • The four things we measure, and their weighting. Bauer, Erdogan, Ellis, Truxillo, Brady and Bodner (2025), “New Horizons for Newcomer Organizational Socialization”, Journal of Management 51(1), 344 to 382. A meta-analysis of 183 studies.
  • Senior people understating difficulty. Keiser and Payne (2019), Safety Science, on impression management in workplace self-report, alongside the wider literature on under-reporting of unwelcome outcomes.
  • Sector differences in job satisfaction. US General Social Survey (44,043 respondents), COPSOQ Hungary (Stauder and colleagues, 2017, 13,104 respondents), and the European Social Survey round 10 (19,565 respondents).
  • Employer mattering more than occupation. Berthelsen and colleagues (2020), COPSOQ III Sweden, across 51 organisations.
  • Cost of losing a new starter. Our own build-up of eight components, deliberately landing below the 100 to 150 per cent of salary usually quoted, with turnover rates by kind of work taken from published CIPD and ONS figures. The full working is on the turnover calculator, where you can change every assumption.
Methodology, and where our numbers come from | Accolgo · Accolgo