Your Lead Score Should Explain Itself. Here Is How.

Open your pipeline and pick the account sitting at 84. Can you say out loud, in one sentence, what it did to earn that number and when? If the answer needs a hover, a second tab and a guess, you do not have an explainable lead score. You have a number your reps quietly stopped believing. This guide works backwards from the sentence a rep reads in the queue to the data that has to exist behind it, and shows how to pull the outside half of that data by asking for it in plain language in Claude or ChatGPT.

Start at the End: the Sentence a Rep Reads

Design the sentence first and the scoring model second, because the sentence is the only part of the system a rep ever touches. Everything upstream exists to make that one line true.

What the Sentence Has to Contain

• The strongest thing that happened, named the way a human would name it.
• How many days ago, in digits, so freshness is obvious at a glance.
• Whether you saw it yourself or picked it up outside your own site.
• The two fit facts that let the account through the filter, usually headcount and industry.

Where It Has to Live

• In the list view, as a column, not behind a tooltip. Nobody hovers over 200 rows.
• In whatever travels with the account into the first email or call, so the reason survives the handoff.
• In one read-only field a nightly job rewrites, so no rep maintains it by hand.

Three Questions a Score Must Answer Out Loud

A score is explainable when it can answer three questions without anyone opening a spreadsheet: what fired, how old is it, and why is that worth this many points. Miss any one and the number becomes decoration.

The Three Questions

1. What fired? A named thing that happened, not a category and not a rolled-up total.
2. How old is it? The day it happened, not the day your system noticed.
3. Why this many points? A weight traceable to how often that thing showed up before a real win.

How Teams Usually Fail the Test

• The score is one opaque total, so an 84 and a 61 feel interchangeable and reps sort by whichever logo they recognise.
• Nothing expires, so a page view from March still props up a number in September.
• The point values were argued into existence in a meeting and nobody can defend them under pushback.

A phrase that keeps surfacing in the r/gtmengineering thread on this: most teams are weighting on vibes. The fix is not a smarter model, it is writing down where each point came from.

Agree on Ready Before You Award a Point

Write down what each stage means, who owns it and how fast it has to move before you configure anything, because a scoring rule laid over a vague handoff just makes the vagueness run faster. Two people arguing about whether an account is ready have a definitions problem wearing a scoring costume.

The Plain-Words Stage Table

Stage, in plain words	What gets it in	Who owns it	How long it can sit
Worth a proposal	Conversation happened, need and timing captured	The closer	To the close date
Worth a call	A rep looked at it and agrees the fit is real	The rep who took it	1 working day
Worth a look	Over the points line, plus one thing on your own site in the last fortnight	Whoever runs marketing	5 working days
Parked	Turned down with a reason code, not deleted	Same owner as before	Recheck in 90 days

Settle the Record Rules First

• Pick the field two records match on and never change it. Website domain for companies, work email for people, never the company name.
• Decide per field whether new information overwrites, fills blanks only, or lands in a parallel field for review.
• Stamp where a value came from and the day it arrived, so a wrong number gets chased back rather than argued about.
• Only then start enrichment, the step where outside facts get appended to a record you already trust.

What a Signal Must Carry to Earn Points

Anything awarding points needs a date, an origin and a track record, and anything missing one of the three is scored on feel. The fastest audit you can run today: list every input to your score and cross out whatever has no date.

The Seven Things to Record Per Signal

What you record	Why it matters	Example value
The day it happened	Nothing can fade without one	2026-08-30
Where you saw it	Your own property or outside	outside source
Plain-words name	The rep reads this, not a code	Opened a second office
Base points	Worth before any fading	1 to 10
Shelf life	Days until it is worth half	60
Track record	How often it preceded a win	0.00 to 1.00
The sentence	What lands in the list view	Under 120 characters

Inputs That Should Not Score

• Anything undated, or any vendor total you cannot break apart into the things that produced it.
• A company match with no confidence figure. Points on the wrong record beat no points at all only in a demo.
• Bought intent at full strength. Halve it. That spike was sold to everyone you compete with the same week.

Give Every Signal Its Own Shelf Life

Set one clock per kind of signal rather than one clock for the whole score, because a pricing page view and a funding announcement do not age at the same rate. Company records themselves rot at roughly 2.1% a month, so even fit facts need rechecking on a calendar.

The Shelf-Life List

• Never, recheck quarterly: right size, industry and region. Those are rules, not events.
• 180 down to 60 days: a new tool in their stack, then a funding announcement, then hiring on the team you sell to. Money and headcount both take a while to move.
• 30 down to 7 days: a demo request, a guide download, a bought intent spike at half strength, and a pricing page visit last of all.

What Built-In Timers Cannot Do

• Most platforms, HubSpot included, offer fixed monthly steps inside one score property, so anything finer runs in your own job.
• They fade against the day the record updated, which quietly rewards slow data.
• They cannot see clustering, so three things in a fortnight count like three across half a year.

The other line worth stealing from that thread: one signal is noise, three inside two weeks is a buying window.

Let Your Last 25 Wins Set the Weights

Do not invent point values. Pull your last 25 closed deals and let them tell you which signals were present before the money moved. It takes an afternoon and it ends the argument permanently.

The Afternoon Version

1. Export the last 25 wins with the company and the date the deal was created.
2. Per signal, count how many of those winners carried it in the 90 days before that date.
3. Divide by every company carrying the same signal in the same window, wins and losses alike. That fraction, not the raw count, is the track record.
4. Retire anything under 0.10. It adds noise and defends itself with volume.

how_often_before_a_win = wins_with_signal_90d / all_accounts_with_signal
points_today           = base_points * how_often_before_a_win * 0.5 ** (days_old / shelf_life)

# drop the signal entirely if how_often_before_a_win < 0.10

The Trap to Avoid

• Counting how often a signal appears rather than how often it preceded a win. Popular is not predictive.
• Widening the window past 90 days until everything looks good. A signal needing six months to correlate is not timing information.

Where Vibe Prospecting Fits: the Outside Half of the Score

Your own site gives you clicks and form fills. Everything else, whether a company grew, hired, moved, raised or swapped tools, comes from outside, and Vibe Prospecting is how you ask for it in a sentence instead of building a pipeline for it.

Why Chat Is the Right Shape for This Job

• You describe the accounts and the window in ordinary words, and answers come back with the day each thing happened attached, the one field the fading maths cannot run without.
• Company facts and recent activity arrive together, so the fit half and the event half of the score already agree on which company they mean.
• Preview before you commit: see the records and the cost before anything is spent, so a signal earns its coverage before you pay for it.
• Batch asks cover up to 1,000 companies at once, so refreshing the whole book overnight is a scheduled request, not a project. Plan around roughly 40 companies per activity request and about three months of history.
• It is powered by Explorium Enterprise Business Data, so the premium sources behind the answers are the ones larger teams pay engineers to plumb in.

The Ask That Feeds a Score

Take these 40 companies. For each one, tell me what happened in the
last 90 days, the exact day it happened, and the current headcount,
industry and region. Skip anything you cannot date.

Curious what your score has been missing? Start free with 100 credits and check one week of activity against your last 25 wins.

Wire It Into Claude or ChatGPT in an Afternoon

If your scoring job already runs somewhere, add Vibe Prospecting as a connected skill rather than one more login to remember. Install once and the same ask works from the chat window and from a scheduled job.

# Claude Code, from the official plugin store
/plugin install vibe-prospecting@claude-plugins-official

# Anywhere else (Claude chat, Cowork, Codex, terminal, scheduled jobs)
npx skills add explorium-ai/vibeprospecting-plugin --all

The Vibe Prospecting Plugin carries install guides for Claude Code, Claude chat, Claude Cowork and Codex, plus a terminal path for scheduled work. Once it is in, the nightly job that recalculates points requests fresh activity in the same run instead of reading a file exported last week.

Two Things to Get Right on Day One

• Confirm the company match before anything writes points.
• Store the day each thing happened as its own field. Keep only the fetch date and every shelf life above becomes uncomputable.

What This Looks Like for a Three-Person Sales Team

None of this needs a data team. A founder with 400 accounts and two reps can run it on a spreadsheet and a weekly ask.

The Small-Team Version

• Four signals, not forty: recent funding, hiring on the team you sell to, a pricing page visit, and a stack change. Four is enough to rank 400 accounts.
• One weekly ask covering the whole book on a Monday morning, rather than a live feed nobody watches.
• The sentence goes in a column of the sheet your reps already work from. No new tool, no new login.
• Review on the last Friday of the month: which sentences preceded a reply, and which nobody acted on.

A Worked Account

Nordvik Freight, 620 staff, sits at 84. Twenty-six points came from a second office opened nine days ago, twenty-two from a pricing page visit three days ago, fourteen from four operations manager roles already a month old and fading, the rest from fit and a tool you replace. The rep reads one line and knows what to lead with.

Your First Week, Day by Day

Run it in this order. Every step depends on the one above, and starting at the scoring rules is why so many of these get rebuilt six months later.

1. Monday: write the stage table in plain words, signed by marketing and sales.
2. Tuesday: fix the match field, the overwrite rules and the source stamps. Nothing enriches first.
3. Wednesday: list every input to your score and cross out whatever has no date.
4. Thursday: pull the last 25 wins and calculate a track record per surviving signal.
5. Friday: write the sentence into a visible column and watch a rep work the queue with it.
6. Next quarter: check win rate by score band against closed revenue, not meetings booked. Meetings reward whoever picks up; revenue rewards whoever buys.

Wednesday is where most teams discover half their score runs on things nobody dated. That is the gap Vibe Prospecting fills: recent company activity with the day attached, asked for in a sentence, previewed before you spend, refreshed on whatever schedule your queue runs on.

Ready to give every point a reason? Try Vibe Prospecting free and rebuild one week of your queue with dated signals.