phase 2 · listen

Lead scoring and merging

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A qualified lead is one real person who fits the ICP and shows fresh intent. This page covers how signals about the same person merge into one lead, the lead as an object, how a lead is scored and tiered, a library of the lead types worth knowing, and what to record so the score can be learned instead of guessed.

detector · the rule that watches → signal · one person caught → lead · signals merged by person, scored → playbook · what to do next

1 — Fit times intent

Fit says they could buy. Intent says they might buy now. A lead needs both; either one alone is a list entry, not a lead.

2 — One person, one lead

Three signals from the same founder on X, Reddit and your site are one lead with three signals, not three leads.

3 — Freeze the score

Store the features as they were when the score was computed. That snapshot is what the next scorer trains on.

what it is, in industry terms

A qualified lead is fit plus intent, attached to a person.

Sales teams call fit "ICP fit" or "firmographic fit" and intent "intent data" or "buying signals". Marketing-qualified and sales-qualified leads are older names for the same split: someone who matches, and someone who matches and is ready. Here the readiness comes from triggers, so a lead is only as good as its freshest signal.

Person — One human, after identity merging. Handles, domains and emails that belong to them hang off the person, not the lead.

Fit — Which ICP fit lines they match, and that no "not if" line or lead rule (customer, never, competitor) applies.

Intent — The signals that caught them: trigger, detector, strength and age in hours.

Score — One number for ordering today's list, from a versioned scorer, with the features it used frozen beside it.

Tier — Hot, Warm or Watch. The tier decides who gets a move today; the score orders people within a tier.

State — Where they are: new, reviewed, in a campaign, replied, joined, paid, or never contact. Plus the contact policy's clock.

LEAD · EXAMPLE — VIBEDRAFT

Fit: 3 of 3 public-pain lines hold: live SaaS, sells to people, says the pain in public
Intent: "launched 2 weeks ago, 3 signups, no idea where buyers are" on r/SaaS · 9h old · strong; replied to your post on X · 2 days old
Score: 82 · scorer v1.0
Tier: Hot

next move

listen-and-reply on the Reddit thread today; the X reply makes it warm, so a DM is allowed after she answers

identity resolution

How signals about one person become one lead.

Merging is entity resolution. Deterministic rules join records that share a hard key; probabilistic rules join records that only look alike, and carry a confidence. A wrong merge is worse than a missed one: it sends one person's context to another. So soft matches below the bar wait for the founder, and every split the founder makes is kept as a label.

Rule	Type	Joins when	Confidence	Watch out for
Same handle, same channel	deterministic	the platform's user id matches (not just the display name)	certain	renamed handles: key on the platform's id, not the text
Same email	deterministic	normalized email matches across waitlist, Stripe, newsletter	certain	plus-addressing and dots in Gmail; normalize first
Same domain, founder role	deterministic	a handle's bio links a domain that matches a sign-up's email domain or a launch listing	high	shared domains: gmail.com, agencies, studios
Bio cross-link	deterministic	an X or Threads bio links the Reddit, GitHub or site profile, or the reverse	high	links to someone else's product they work on
Launch listing maker	deterministic	the maker named on a launch listing links the same handle or domain	high	listings posted by agencies for a client
Same product, two handles	probabilistic	two handles both claim the same product URL as their own	medium	cofounders: two people, one product; keep two persons, one company
Name plus product words	probabilistic	similar display names and the same distinctive product words, within 30 days	low to medium	common names; never auto-merge, ask the founder
Writing match	probabilistic	the same unusual phrases and the same pain, on two platforms, close in time	low	coincidence; use only to suggest, never to merge
Founder merge	manual	the founder says two records are one person	certain	keep it as a label for the resolver
Founder split	manual	the founder says a merge was wrong	certain	the most valuable label here: it teaches the resolver what not to do

the lead object

Every lead is one record with the same fields.

This is the shape a lead takes in the store and in every event about it. The person holds identities; the lead holds the reason to talk to them now. One person can have a new lead each time a fresh trigger fires after an old lead closed.

Field	Type	What it holds
`lead_id`	uuid	Stable id; every move, touch and outcome carries it.
`person_id`	uuid	The merged person. Several leads over time can share one person.
`identities`	list	Each handle, domain and email: the channel, how it was linked (rule id), confidence, linked at.
`icp_version`	string	The ICP that judged fit.
`fit_lines`	map, frozen	Each fit and not-if line: matched, not matched or unknown, with the evidence.
`signals`	list	signal_id, trigger, detector_id and version, strength, posted at, captured at.
`score`	0 to 100	The ordering number for today's list.
`scorer_version`	string	Which formula or model produced the score.
`features`	JSON, frozen	Every input the scorer used, exactly as it was then.
`tier`	hot \| warm \| watch	Decides who gets a move today.
`tier_history`	list	Each change with time and cause: new signal, age, review, rule change.
`enrichment`	map	Bio, product, socials, brief of their product; each field with source and date.
`status`	enum	new, reviewed, in campaign, replied, joined, paid, closed.
`contact_policy`	state	Last touch, touches this week, cooling-off until, channel allowed.
`never_contact`	bool + reason	Set by the founder, by a stop request or by a lead rule. Never cleared automatically.

one lead, as data

{
 "lead_id": "ld_7f3c2a",
 "person_id": "pn_19b0e4",
 "identities": [
 {
 "channel": "x",
 "handle": "@example_founder",
 "rule": "bio-cross-link",
 "confidence": 0.97
 },
 {
 "channel": "reddit",
 "rule": "bio-cross-link",
 "confidence": 0.97
 },
 {
 "channel": "domain",
 "value": "acmenotes.example",
 "rule": "same-domain-founder",
 "confidence": 0.9
 }
 ],
 "icp_version": "icp.vd.2026-10-09",
 "fit_lines": {
 "live_saas": "match",
 "sells_to_people": "match",
 "pain_in_public": "match"
 },
 "signals": [
 {
 "signal_id": "sg_a12",
 "trigger": "asking-for-a-tool",
 "detector_id": "det.asking.reddit",
 "detector_v": 3,
 "strength": "strong",
 "posted_at": "2026-10-16T21:04Z",
 "captured_at": "2026-10-16T21:40Z"
 },
 {
 "signal_id": "sg_b07",
 "trigger": "engaged-with-you",
 "detector_id": "det.engaged.x-replies",
 "detector_v": 1,
 "strength": "medium",
 "posted_at": "2026-10-15T14:10Z",
 "captured_at": "2026-10-15T14:12Z"
 }
 ],
 "score": 82,
 "scorer_version": "v1.0",
 "features": {
 "strength": 1,
 "fresh": 0.76,
 "fit": 1,
 "stack": 1,
 "engaged": 1,
 "age_h": 9,
 "half_life_h": 24
 },
 "tier": "hot",
 "tier_history": [
 {
 "at": "2026-10-16T21:41Z",
 "to": "hot",
 "cause": "new strong signal"
 }
 ],
 "enrichment": {
 "product": {
 "value": "Acme Notes",
 "source": "site",
 "at": "2026-10-16"
 }
 },
 "status": "new",
 "contact_policy": {
 "last_touch": null,
 "touches_7d": 0,
 "cooling_until": null
 },
 "never_contact": false
}

scoring and tiers

A transparent v1 score now, a learned one later.

Start with a formula a founder can read and argue with. Every input it uses is frozen on the lead, so once replies come back the same inputs train v2: a reply-propensity model that predicts who answers within 7 days.

score = 100 × fit_gate × (0.35·strength + 0.25·fresh + 0.20·fit + 0.10·stack + 0.10·engaged)

fit_gate — 0 if any not-if line or lead rule applies, else 1. Fit gates; it doesn't add.

strength — Strongest signal: strong 1.0, medium 0.6, weak 0.3.

fresh — 0.5 ^ (age in hours ÷ the trigger's half-life). With a 24-hour half-life, yesterday's ask counts half.

fit — Share of ICP fit lines that hold, unknowns counted as half.

stack — 1 if two or more signals from different detectors or triggers, else 0.

engaged — 1 if they already replied to, followed or visited you; warm beats cold.

The weights are a starting point to tune, not a finding. Change them only by bumping scorer_version, and judge the change offline on reply labels before it ranks anyone.

Hot — Move today · All three public-pain fit lines hold and the score is high: a strong, fresh signal, or stacked ones.

Warm — Move this week · All three fit lines hold, but the signal is older, weaker or single.

Watch — No move yet · Any fit line fails or is unknown. Keep listening; a second signal can promote them.

A lead is Hot or Warm only when all three fit lines hold; otherwise it is Watch.

the library

Twelve lead types worth knowing by name.

Each type is a pattern of fit and intent that shows up again and again, with the play that fits it. Reply rates are left as "to measure": record them per type and the library fills itself in.

lead_type.stacked — hot

Stacked-signal founder

who: Two or more fresh signals from different detectors: asked for a tool on Reddit and posted a quiet launch on X the same week.
why: Two independent signals are the strongest evidence you can get without first-party data.
side: cold
score: 80 to 95 (typical, v1)
play: listen-and-reply, then founder-led-outbound
reply: to measure

lead_type.asking — hot

Asking for a tool right now

who: "What do you use for {job}?" posted in the last day, by someone who fits.
why: The window closes fast: the thread gets answered within a day or two.
side: cold
score: 70 to 90 (typical, v1)
play: listen-and-reply
reply: to measure

lead_type.quiet-launch — hot

Launch-week founder, quiet launch

who: Launched in the last 7 days with under 10 upvotes or no comments.
why: They know launch didn't work and they're looking for what does.
side: cold
score: 70 to 85 (typical, v1)
play: launch-or-launch-again
reply: to measure

lead_type.unhappy — hot

Paying a competitor, unhappy

who: Complains in public, or in a review, about a tool in your category that they pay for.
why: Budget already exists; you're replacing a vendor, not creating a need.
side: cold
score: 65 to 85 (typical, v1)
play: comparison-interception
reply: to measure

lead_type.tried-failed — warm

Tried it, it failed

who: Posted what they tried (cold DMs, ads, a launch) and that it didn't work.
why: High pain, but often venting; answer the post before offering anything.
side: cold
score: 55 to 75 (typical, v1)
play: listen-and-reply
reply: to measure

lead_type.lurker — warm

Engaged twice

who: Liked or replied to your posts twice in two weeks, fits the ICP, never signed up.
why: They already know you; a direct, low-pressure offer fits.
side: warm
score: 50 to 70 (typical, v1)
play: offer-post-reply-loop
reply: to measure

lead_type.commenter — hot

Commented the keyword

who: Replied to a comment-to-get post asking for the thing.
why: They raised their hand; the reply is the opt-in. Deliver what was promised first.
side: warm
score: 75 to 90 (typical, v1)
play: comment-a-keyword
reply: to measure

lead_type.waitlist — warm

Waitlist joiner

who: Joined the waitlist; may also have a public signal.
why: Highest-intent person you have. Learn their pain before you open.
side: warm
score: 60 to 80 (typical, v1)
play: waitlist-countdown
reply: to measure

lead_type.former — warm

Former paying customer

who: Paid before, cancelled or lapsed; may be posting about the same pain again.
why: They know the product. What changed since they left is the whole message.
side: warm
score: 55 to 80 (typical, v1)
play: win-back
reply: to measure

lead_type.stalled — warm

Signed up, stalled

who: Created an account but never did the first action that predicts paying.
why: Your problem with them is activation, not reach.
side: warm
score: 45 to 70 (typical, v1)
play: stalled-trial-rescue
reply: to measure

lead_type.building — watch

Building in public, pre-launch

who: Shares progress on a product that isn't live yet.
why: Fits later. A good beta tester now, a buyer once they've launched.
side: cold
score: 30 to 50 (typical, v1)
play: beta-recruit
reply: to measure

lead_type.adjacent — watch

Right person, no signal

who: Fits every line but hasn't shown intent in the window.
why: Fit without intent. Messaging them now is cold outreach, not a response.
side: cold
score: 10 to 30 (typical, v1)
play: none yet: keep listening
reply: to measure

how it connects

What comes in, what goes out.

ICP and Trigger Detection — FROM STAGE 3 · the fit lines and not-if lines that gate every lead

Detector catalog — FROM STAGES 5 AND 6 · detectors emit the signals a lead is made of

Buying Moments Map — FROM STAGE 4 · each trigger's strength and how fast it goes stale

Playbook Library — TO STAGE 8 · the tier and type pick the play

GTM tracking plan — TO STAGE 11 · lead_scored, lead_reviewed, identity_linked and identity_unlinked events

data to capture

Record it so the next scorer can be learned.

record

identity links with method, rule id and confidence, and every split the founder makes
the score with scorer version and the exact features used, frozen as JSON
tier changes (Hot, Warm, Watch) with the time and the cause
enrichment fields with their source and date
the founder's review of each lead: accepted, skipped, snoozed, never contact, and the reason
what was on the daily list and in what order, including leads nobody acted on

label — accepted, skipped or never (founder); replied within 7 days (lead); joined the waitlist or paid (outcome); merge right or wrong (split events)

trains — entity resolution (merge or not, from links and splits); reply propensity, lead score v2; calibrated tiers, so "Hot" means a measured reply rate

test — scorer weights and tier cutoffs: offline against reply labels first, then a randomized slice of the daily top 10, with the assignment probability logged