The dossier covering how a leads marketplace works as a whole briefly mentions the role scoring plays in sorting requests before they're distributed. That mechanism deserves its own treatment, because it's what actually determines whether a company receives genuinely workable requests or a stream of loosely-checked contacts that waste its time. A lead quality score isn't a single tick-box check: it's a composite indicator, recalculated at every stage, that combines a request's technical validity with the track record of the source that produced it.
This dossier sets out how scoring actually works on leads-qualifie.ch, independent of category: what a score is and why a marketplace can't do without one, the technical criteria checked on every request, the behavioural criteria tied to a source's history, how that score then shapes distribution to receiving companies, and finally how a company can check, before committing, whether a platform's advertised scoring is genuinely rigorous or just a marketing line with no operational substance behind it.
What a lead score is, and why it exists
A lead score is a rating or classification assigned to every request before it's distributed, summarising the likelihood that it corresponds to a real, reachable customer who genuinely wants to be contacted. It's built from several verifiable signals — validity of the contact details, coherence of the described need, proof of consent — combined with a track record specific to the source that produced the request. It is never a subjective judgement call; it's a reproducible calculation, applied the same way to every request in a given category.
This mechanism exists for two reasons that overlap without being identical. The first concerns the receiving company: without upfront filtering, it would waste considerable time calling back wrong numbers, poorly described needs, or contacts who never consented to being approached. The second concerns the platform itself: a degraded average experience, even one caused by a single underperforming source, doesn't just damage that source — it erodes every receiving company's trust in the marketplace as a whole, including in perfectly valid requests submitted by other sources. Scoring is therefore as much a tool for individual protection as a mechanism for preserving the system's collective credibility.
The technical criteria behind scoring
The first layer of scoring covers elements that are strictly verifiable at the moment of capture, before any human ever gets involved. The validity of the Swiss phone number is checked against format rules and, in some cases, additional verification that rules out numbers that are obviously wrong or disconnected. The coherence of the e-mail address is examined the same way: a valid domain, correct syntax, and the absence of auto-generated addresses that give away a form filled in carelessly, or by a bot rather than a real customer.
The second layer covers how precisely the need is described: the type of service sought, the stated urgency, and the location must be specific enough for a company to judge, within seconds, whether the request falls within its coverage area. A request that's vague or incomplete on any of these three points gets a degraded score, regardless of how valid its contact details otherwise are. The most decisive criterion, however, remains proof of explicit consent to be contacted: that consent must be timestamped and traceable back to its origin — the form, the checkbox, the exact moment of capture — not simply asserted by the source with no verifiable record. A source that can't produce this proof on request sees its submissions penalised systematically.
Behavioural criteria and the source's track record
Beyond the technical criteria applied to each request taken in isolation, the score factors in a second layer of assessment covering the source itself and its behaviour over time. A source that regularly submits requests leading to an actual, successful contact, with no complaints from receiving companies, has that history work in its favour on the score of each new submission. Conversely, a source whose reachability rate declines over time, or that accumulates complaints about contacts already approached elsewhere, has that history count against it — even when the individual request under review looks technically sound, with valid details and consent that appears to be in order.
This behavioural dimension is what sets genuinely dynamic scoring apart from a one-off compliance check. A valid phone number and a timestamped consent aren't enough to guarantee a request will be well received if the source behind it routinely resells the same contacts in parallel through undisclosed channels, or repeatedly submits poorly described needs. History therefore acts as a multiplier, or a corrective, applied on top of the raw technical score: it can lift the rating of an otherwise average request if the source is consistently reliable, or drag it down despite individually sound technical criteria.
How the score shapes distribution
Once calculated, the score determines what happens to a request in one of three ways. A highly-rated request is passed on as-is, without further delay, into the distribution queue for its category and zone. A request with a middling score may be held back for a check or additional information — an automatic follow-up to the final customer to confirm a missing detail, for instance — before being passed on once complete. A request with an insufficient score is filtered out before it ever reaches a receiving company: it's never counted in the volume delivered, which stops a company from paying for, or counting as received, a request that was never going to lead to a workable contact anyway.
The score also affects the order in which eligible companies see a given category and zone: at equal intake profile, a well-rated request can be offered first to companies whose own responsiveness track record is strongest, which maximises the chance it turns into an appointment. Finally, the score interacts with the choice between an exclusive and a shared lead, without replacing it: an exclusive request puts the full weight of that score on a single company, while a shared request spreads that same quality level across several recipients — the arbitration mechanism specific to that distinction is covered in the dossier dedicated to exclusivity.
How to check a marketplace's scoring is actually reliable before committing
Before committing to a marketplace, a company can gauge how rigorous its scoring genuinely is from a handful of concrete indicators that a serious platform should be willing to share without hedging. The average conversion rate by category — from contact through to booked appointment — is a first benchmark: a platform that refuses to share it, even as a range, or simply has no data of this kind, probably isn't measuring its own results. How quickly complaints get handled is a second: dynamic scoring assumes feedback from the field gets folded in quickly, which is only possible if complaints are dealt with within a stated, communicated timeframe rather than left unanswered.
How sources are audited is the third indicator, and often the most telling: a platform that can describe how it checks its sources — audit frequency, the criteria that trigger a downgrade, a threshold below which a source is suspended — is showing evidence of a genuinely operational system rather than a sales pitch. Several signals, conversely, should raise concern: a platform unable to explain how a score is calculated beyond vague phrasing, no visible differentiation between requests of varying quality within the same category, or a lead volume growing sharply with no mention of any filtering mechanism at all — all signs that no real sorting happens before distribution.