LeadIQ: Scoring PEO Prospects Before the First Call

Most PEO sales teams work a list. The list came from a data vendor, a trade show, a broker's book, or a rep's memory, and every name on it looks the same until someone makes a call. In two decades of selling PEO, the single biggest waste I saw was skilled reps spending their mornings on companies that were never going to fit, because nothing on the list told them which ones would.

The information that predicts fit is mostly available before the first call. It is just scattered across a dozen sources and never assembled. LeadIQ assembles it, scores it, and hands ConnectIQ a ranked list that a territory and quota plan can be built on.

What predicts PEO fit

A lead score is only as good as the signals behind it, so it is worth being explicit about which ones we weight and why. These are the ones that have mattered in our own PEO's experience and in the deal data across HALO IQ tenants, stated qualitatively because the weights vary by PEO and by program.

Headcount band is first. Every PEO has a range where its pricing, its master WC program, and its service model all work. Below the floor, the admin fee does not cover the service cost. Above the ceiling, the client wants things a PEO's shared infrastructure is not built to give them. The band differs by PEO, and the score should be configured to the tenant's own band rather than to a generic notion of "SMB."

Industry comes next, and it cuts two ways. For the WC program, industry drives class code mix, which drives whether the master policy's carrier will accept the risk and at what rate. For the service model, industry drives how much HR complexity the client brings. A professional services firm with 80 salaried employees and a construction firm with 80 field employees are both in band, and they are completely different prospects.

State matters for the obvious reasons: the PEO's registration and licensing, the WC program's filed states, and the compliance load. A California prospect is a different sale than a Texas prospect, and a PEO that is strong in one is not automatically a fit for the other.

Workers' comp class mix, where it can be inferred, is the most underwriting-relevant signal. A prospect whose payroll is concentrated in a class the master program prices well is a prospect the PEO can win on rate. One concentrated in a class the carrier will not take is not a prospect at all, and the rep should know that before the call.

Growth is a positive signal for PEO fit because growing companies are the ones whose HR load is outrunning their HR staff. Hiring velocity, new locations, and funding events are all visible in public data.

Enforcement history is the signal most lead scoring ignores and the one we think is most useful. A prospect with a recent OSHA citation, a DOL back-wage finding, or a rising mod is a prospect with a problem the PEO solves. That makes it a better lead, not a worse one, provided the problem is within the PEO's appetite. A prospect on the federal exclusions list is a different matter, and the score reflects it.

Current PEO or payroll provider is a strong signal in both directions. A company already on a PEO is a replacement sale, which has its own timing around renewal and its own objections. A company on a bare payroll service with no HR support is a greenfield sale.

Enrichment: filling the signals from Apollo and Clay

A name and a domain are enough to start. LeadIQ uses Apollo for company and contact enrichment, which fills in headcount, industry classification, location, technology stack where relevant, and the decision-maker contacts a rep needs. Clay runs the enrichment workflows that pull the rest: hiring signals, news, and the public records that feed the enforcement score.

The enforcement and licensing signals come from the same public datasets the underwriting side of HALO IQ uses. The OSHA inspection and ITA data, the DOL Wage and Hour compliance actions, the SAM.gov exclusions, and for California contractors the CSLB license file, are matched to the prospect by the same company-matching logic InboxIQ uses to route documents. A lead that later becomes a deal carries that record forward, so the underwriter sees what the rep saw.

Enrichment is not free, in either sense. It costs credits, and it introduces matching errors. LeadIQ enriches in stages so that a company which fails the headcount and state filters never consumes a contact credit, and it keeps the confidence of each match visible so a rep can see when a signal came from a fuzzy match on a common name.

The score, and what it is not

The score is a ranked estimate of fit, not a prediction of close. We are careful about that distinction because a lead score that claims to predict revenue invites reps to ignore it the first time a high score goes nowhere.

Fit means: this company is in band, in an industry and state the PEO serves, with a class mix the WC program can accept, and with enough visible need that a conversation is worth having. A high-fit lead that never buys is still a correctly scored lead. A low-fit lead that buys is still a low-fit lead, and probably a client the PEO will regret.

The score is configurable per tenant. A PEO that specializes in healthcare staffing weights industry and class mix differently from one that serves professional services. An MGA using the same module to find agency partners weights an entirely different set of signals. The scoring rules are set by the tenant's admin, the inputs are shown for every lead, and the score can be overridden with a reason that is kept.

We use the IQ Agent for the qualitative part of the score, which is the reading of news, hiring pages, and public records to decide whether a visible event is a buying signal. Two models read the same evidence and the result is merged, which catches the case where one model over-reads a routine press release as a growth event.

From a score to a territory plan

A scored list is useful to a rep. It is more useful to a sales leader, because a scored list across a whole market is the raw material for a territory and quota plan that is based on opportunity rather than on who was hired first.

In ConnectIQ, leads carry their score into the territory model. A sales leader can see, for each territory, how many in-band prospects exist, how they distribute by industry and headcount, and how many are already in the pipeline. Quotas can be set against the addressable opportunity in each territory rather than against last year's number, and the forecast can distinguish between a territory that is underperforming and one that was never going to produce.

For a PEO that works with brokers, the same view answers a different question: which brokers hold books concentrated in the PEO's best-fit prospects, and are therefore the partners worth cultivating. For a broker using HALO IQ, the score runs the other direction, identifying which clients in the broker's own book are candidates for a PEO conversation.

The hypothetical list

Here is a hypothetical to show the shape of the output. A PEO in the Central Valley configures its band at 25 to 250 employees, its states as California only, and its WC appetite to exclude roofing and a few other high-hazard classes. LeadIQ runs across a list of two thousand companies from a data vendor.

Roughly the bottom half drops out on headcount or state before any enrichment credit is spent. Of the rest, the ones in excluded classes drop out on inferred class mix. What remains is ranked: at the top, companies in band with visible growth, an industry the PEO serves well, and a recent enforcement event or a mod that public data suggests is rising. In the middle, in-band companies with no visible need signal, worth a call but not a first call. At the bottom, in-band companies already on a competing PEO with no renewal signal visible, worth a touch six months before their likely renewal.

The rep's Monday morning now starts with the top of that list, with the evidence for each score one click away, and the SPIN discovery questions for the first call already drafted from the same data.

What LeadIQ does not do

It does not call anyone. It does not send sequences; the integrations with HubSpot and Salesforce exist for teams whose outreach already lives there. It does not promise that a score predicts a close. It assembles the evidence a good rep would gather if they had a day for every lead, scores it on rules the tenant controls, and puts the result where the territory plan and the deal record can use it. That is the part that was never getting done, and it is the part that decides whether a rep's time goes to prospects or to the list.