How do I prioritize my inbound leads by fit?

To prioritize inbound leads by fit, score every lead on future value and work the highest-fit accounts first. SignalRx researches each new lead the moment it lands, pulling from the company's website, social, reviews, and public registries to fill in whatever the record is missing, tags the attributes that matter, and scores both the account and the contact, writing a Fit Score and Fit Tier onto the record in Salesforce alongside the signals behind them. Because a net-new lead has no history with you yet, that first score is triangulated from external public sources; the picture deepens with your own internal signals of revenue, support, and usage once the account becomes a customer. Those scores and tags feed Revenue Plays and list views native to Salesforce, so reps spend their day on the accounts most likely to become revenue rather than guessing by instinct.

The allocation problem is expensive. Most teams still work new leads in the order they arrive instead of the order they matter, so reps spend hours on accounts that were never going to buy while a handful of high-fit accounts sit in the same undifferentiated queue. Sorting by fit before anyone makes contact is the cheapest way to get that time back.

What does it mean to score a lead by fit?

Fit is a prediction of future value, not a firmographic filter. SignalRx models each lead against the customers who actually buy, stay, and grow, scoring it against your own internal outcomes, the revenue, retention, and expansion your best accounts produce, then reading how closely the lead matches. The inputs are the signals that predict revenue in healthcare, drawn from many sources and combined and validated against each other, not just industry and size:

• ICP fit modeled on your best customers and the revenue, retention, and expansion they produce.
• Website signals: service lines, locations, and financing or pricing pages read straight from the company site.
• Reviews: Google review volume and rating and the Business Profile, plus RealSelf and Instagram presence for aesthetics and other consumer verticals.
• Social, leadership, and hiring: LinkedIn leadership and headcount, and job postings, where a listing for an RCM lead or a new-location GM is a buying signal.
• Identity and structure anchor: specialty and taxonomy NPI-verified against NPPES, and parent-child ownership from NPPES and CMS, including PE-backed operators, MSOs, DSOs, and rollups, the fixed reference every other source is checked against.
• Number of locations and organizational scale, read from the website and reviews and confirmed against CMS-reported size and setting where a facility is CCN-identified.
• Software and EHR detected on the account.
• Buying signals and revenue potential.

Two leads can look identical on a bought list, same specialty, same size, and be completely different opportunities. One matches the pattern of your best customers on ownership, locations, reviews, and software; the other never will. A spreadsheet cannot see that difference. A model that combines these sources and scores them against your real outcomes can.

How do I work leads by Fit Tier in Salesforce?

Build a list view filtered to GW Fit Tier = High and sorted by GW Fit Score descending. That view is the working list: the accounts most likely to become revenue, in order, with the reasons already on each record. A net-new lead earns a Fit Score and Fit Tier from the outset; a health tier only comes later, once the account is a customer and there is usage to read.

What does this look like in practice?

Illustrative, based on how SignalRx customers work. A healthcare software company takes in a few thousand inbound leads a quarter from demos, content, and events. Scored against their customer model, roughly 15 to 20 percent land in the High tier. Reps work that tier first each morning from a shared list view, and marketing routes the Low tier to nurture instead of passing it to sales. The result is the same number of conversations from far less dialing, because the High tier converts at a multiple of the rest, and the reasons for each account (a PE-backed multi-location group on a matching EHR, say) are visible before the first call.

How do I route and action high-fit leads?

Use assignment rules or a record-triggered Flow on GW Fit Tier to send High-Fit leads to your closers or a round-robin and route Low-Fit leads to a nurture track instead of a rep's queue. Then auto-enroll each lead in the right Revenue Play based on fit, ownership, and buying signals, so the next step, the task, the sequence, the alert, happens on its own rather than depending on a rep to remember it.

How do I rank a big batch of MQLs?

When marketing hands sales a large marketing-qualified list at once, remember that engagement is not fit. Marketing qualification usually means someone engaged, not that they match your ICP, so a big MQL list mixes strong prospects with tire-kickers. Rank it on both the engagement that made each record an MQL and its Fit Score, and work the leads that fit and engaged first.

Illustrative, based on how SignalRx customers work. A company selling a billing platform to behavioral health groups gets 600 MQLs from a webinar. SignalRx scores each by fit. The 70 that are High Fit, including an independent 20-location behavioral health group in California, go to reps first, the Moderate Fit records follow, and the webinar's many No Fit attendees, students and vendors, stay out of the rep queue entirely, so the team works 70 real prospects instead of 600 mixed ones.

How do I do this at scale?

SignalRx scores every new lead automatically and keeps the tiers current as new signals arrive, all inside Salesforce, so there is no new tool to learn and no CSV to export. It weighs external public context against a customer's own internal data as accounts mature, and when two sources disagree it infers which to trust rather than averaging them. Most teams are live in about 30 days.

Key takeaways

• Fit ranks leads by future value, the characteristics of customers who buy, stay, and grow, not static firmographics.
• Work a High-Fit list view top down; route Low-Fit to nurture so reps stop spreading time evenly.
• Engagement is not fit, so rank a big MQL batch on both before routing.
• Healthcare signals from web, social, reviews, and registries, combined and validated against each other, are what separate identical-looking leads.
• Net-new leads score from external public sources; internal signals sharpen the picture once they are customers.
• Revenue Plays make the next step automatic, and the scores update continuously inside Salesforce.

SignalRx turns your inbound queue into a ranked, self-updating worklist inside Salesforce. Close Better Leads with SignalRx.

Related: How do I read the SignalRx Intelligence Card in Salesforce? and How do I turn a score into action and work a prioritized list?