Sales · Analyze & Recommend
Stop guessing which lead to call first.
Doe scores every lead in your CRM on fit and real buying signals, attaches a reason you can read, and ranks the list. Your reps open the day knowing who to work: a ranked queue with the "why" on every row.
Works acrossDoeHubSpotWeb ResearchSlack
What you get.
Doe scores the leads already sitting in your CRM by how well they fit your ICP and what buying signals they are showing, and attaches a plain-English reason to each score. It enriches thin records with its native tooling, reads public signals like hiring and funding, and ranks the list so reps work the top of the pile first. Run as a Loop, it re-scores on a schedule as fit and signals change, so the priority order stays current instead of decaying the day you set it.
A score with no reason is a number nobody trusts.
Most lead scoring is a points model someone set up two years ago: +10 for a demo request, +5 for opening an email, and a grand total that reps quietly ignore because nobody can tell them why a 78 is better than a 64. The model never saw that the 64 just raised a round and posted three roles on your exact team, and the 78 has been cold since spring. So reps fall back on gut and recency, and the highest-intent leads sit in the queue while someone works a stale MQL because it bubbled to the top.
Meanwhile half your records are too thin to score at all: a name and an email, no title, no company size, no signal, so they get a default and disappear. The leads most worth a call are often the ones the model can’t see, and the cost is real: response goes to the loudest record instead of the best one.
What changes.
- 01What the score is built onBefore · Static points for clicks and form fillsWith Doe · ICP fit + live buying signals, weighed together
- 02TrustBefore · A number with no explanation; reps ignore itWith Doe · A reason on every score reps can act on
- 03Thin recordsBefore · Unscoreable, default to the bottom, forgottenWith Doe · Enriched first, then scored like the rest
- 04FreshnessBefore · Set once; decays silently as signals changeWith Doe · Re-scored on a schedule; top movers flagged
From an undifferentiated pile to a ranked queue
- 01Pulls the leads and fills in what the records are missingHubSpotDoe read your open leads and used its native enrichment to complete the thin ones (title, company size, seniority) so every record is scoreable, including the ones that came in thin
- 02Checks each lead for real buying signalsWeb ResearchDoe read public sources for the signals a points model misses, like recent funding, relevant hiring, leadership changes, or a switch off a competing tool, and attached the evidence to the row
- 03Scores fit and intent together, with a written reasonDoeThe Judge ranked each lead on how well it matches your ICP and how strong its current signals are, and wrote a one-line rationale ("Series A two weeks ago, hiring 3 on your buyer’s team, VP-level") so the order stays explainable
- 04Writes the score, reason, and rank back to the CRMHubSpotDoe updated each lead with a fit/intent score, the reason, and a priority tier, so reps sort by one field and trust it, with the evidence one click away
- 05Surfaces the top movers to the teamSlackDoe posted the leads that jumped into the top tier this run, with the reason each one moved, so nobody has to refresh a dashboard to catch a hot lead
- 06RecurringEvery weekday at 7:00 AMYou set the scoring rubric once in chat. Doe runs it as a Loop, a scheduled job that re-scores your open leads each morning, re-checking signals and re-ranking the queue, then posts the leads that moved into the top tier overnight. Because intent decays fast, a score from last month is worse than no score; the Loop keeps the order honest so reps spend the day on who is hot now. Scores written to HubSpot + top movers posted to #sales.
Start with the right source material.
- 01Add your library and toolsAdd or select the source files Doe should use, then connect the tools this task needs. No API keys, no engineering.
- 02Describe what you need“Every weekday, score our open leads in HubSpot on ICP fit and buying signals like funding, hiring, and tech changes. Enrich the thin records first, write a score, reason, and priority tier back to each, and post the leads that jumped into the top tier to #sales.”
- 03It runs on scheduleRuns every weekday at 7am. Reps open a ranked queue with the reasoning on every lead.
Before you delegate.
- 01How is this different from native HubSpot or Salesforce lead scoring?Built-in scoring is mostly a static points model over CRM activity: it credits clicks and form fills and can’t see what is happening outside your funnel. Doe scores fit and live external signals together, enriches thin records so they’re scoreable at all, and writes a reason for every score. You get a ranked queue your reps trust instead of a number they route around.
- 02Do I have to define the scoring rules, or does Doe decide?You set the rubric in plain language: what fit means for you and which signals matter. Doe applies it consistently and shows its reasoning. When you disagree with a rank and say so, it records that and weights the next run accordingly. You keep control of the definition; Doe does the work and stays explainable.
- 03Is this a training model on my data?No. There is no model training and your data never trains anyone else’s. Each run is a fresh, traceable evaluation against your rubric and current signals. When you correct a score, that preference is applied to your future runs only. That is compounding precision: your corrections sharpen your own runs and never feed a shared model.
- 04What about leads with almost no information?Those are exactly the ones a points model drops. Doe enriches them first with its native tooling (title, company size, seniority, and any public signals) then scores them on the same footing as complete records, so a high-intent lead doesn’t get buried just because it came in as a name and an email.
- 05How often does it re-score, and can I change that?By default the Loop re-scores every weekday morning, because buying signals decay quickly. You can set any schedule down to a five-minute minimum from the Loops page, or pause and resume it. Each run re-checks signals and re-ranks, and flags the leads that changed tier so nothing hot sits unnoticed.