lead-scoring

Lead Scoring

Scoring splits in two, because the data splits in two.

• Pre-contact — what you can read: profile fit, services fit. That is prospect-scoring.
• Post-contact — what only a conversation establishes: whether they engaged, what the deal is actually worth, who really decides, when they would move.

This skill is the second half, and it starts from the first. A lead score that re-derives fit from scratch throws away the work already done and produces two different fit numbers for the same account.

The structure

lead score = prospect profile (carried over, capped)  +  post-contact points (0 until contact)

A workable default — tune the weights, keep the shape:

Block	Max	Available
Prospect profile (from prospect-scoring, normalised)	30	before contact
Engagement — replied, opened a thread, asked something real, attended	15	after contact
Confirmed deal potential — a number they said, not one you inferred	20	after contact
Decision path — you know who signs and how they buy	15	after contact
Timeline — a date or a window they named	10	after contact
Access — you are talking to the person who decides, or to a champion who will carry it	10	after contact

Each block on levels, so the score reads the same whoever fills it in:

Block	Full	Partial	0
Prospect profile (30)	the prospect-scoring total, carried over	—	—
Engagement (15)	15 · came to a call	10 · asked something real · 5 · "send me info"	no reply
Deal potential (20)	20 · named a budget at or above your minimum deal	10 · named one below it, or only a range	no number named
Decision path (15)	15 · you know who signs and how they buy	8 · you know who signs, not the process	unknown
Timeline (10)	10 · a date within 3 months	5 · "this year" or later	no timeline
Access (10)	10 · talking to the decision-maker	5 · talking to a champion who will carry it	whoever happened to reply

Score → action, as starting bands (recalibrate, see below): 60+ a call · 40–59 one qualifying question aimed at a block that scored 0 · below 40 nurture track · stop (confirmed non-target or opt-out) out of all campaigns whatever the total.

Hard ceiling rule. A lead with zero post-contact points cannot outrank one that has them, however good its profile. Cap uncontacted rows below the top band. Otherwise the queue fills with beautiful strangers and the people who actually replied sit behind them.

Bands come from your pipeline, not from the rubric

A rubric's thresholds are a guess until a real pipeline calibrates them. If the team's live sheet treats 60+ as worth a call and the rubric says 75, the sheet wins — it is the one that has been compared against outcomes. Write both numbers down and reconcile them deliberately; do not let a theoretical threshold quietly override observed behaviour.

Recalibrate after every meaningful cohort, and recalibrate on accounts, not rows — a company with three contacts otherwise counts three times and drags the whole model.

Only calibrate on a clean cohort. Accounts with mostly empty fields will show absurd conversion rates, because they were usually created by hand after they converted. Backfilled records predicting the outcome that created them is the most common way a scoring model gets a flattering, useless number. In one calibration a base looked like it converted at 9.6%; on the clean cohort it was 3.2%.

When the base has no deal values, the model optimises conversations, not revenue — and will systematically favour small accounts, because small firms answer more. Say so out loud rather than presenting the ranking as a revenue ranking. Recalibrate only once deal values exist.

Hard gates beat points

Some facts should never be outweighed by a good total:

• Confirmed non-target → clamp the score below the disqualify line, no matter what else scored.
• Data doubt on a load-bearing field → clamp below the top band. The lead stays in the queue and gets human eyes, rather than being either promoted on bad data or silently dropped.
• Explicit opt-out → out of scoring entirely, out of all campaigns.

A gate that only subtracts points is not a gate. It has to clamp.

Keep the components visible

Store every block as its own column, never just the total. When a lead scores 78, you have to be able to see whether that came from a confirmed budget or from three soft engagement points — those two 78s deserve opposite actions. A single total column makes the model unauditable and, in practice, untrusted by the people meant to work the queue.

Same rule for the inputs behind a block: where a number could come from several sources, keep the sources in separate columns and note which one was used. Merging them loses the distinction between a verified figure and an inferred one exactly when you need it.

Terms and plain-language output

Rules for every document a client or their team will read (review, roast, canvas, offer cards, instruction):

1. Open with «Словник цього документа»: only the terms that actually appear in it, at most 10, each as «термін: що це простими словами». Like the definitions section of a contract.
2. No internal codes or letter scales in the text (A/B/C, D L T E, BLEND numbers without words, Own/Shared/Generic, Z1.4, GATE 2.5, T1–T6). Write the meaning instead: «є цифра з клієнтом», not «A».
3. Use the plain form from the table below. English stays only for tool names and for copy that ships in English (promises, hooks, subject lines). If the document is in another language, translate the plain form, never the internal term.
4. Do not score what cannot exist yet. A block that depends on work not done (signal catalog, case studies, a previous step) gets «пропущено: спершу потрібно <що саме>», not a score.
5. Every section of a review opens with one concrete action («до наступного дзвінка: …»), then at most five lines of why. No paragraph that only restates the framework.

Internal term (this skill)	Write for the reader	What it means
ICP fit, services fit, prospect score, prospect profile	оцінка відповідності профілю	Бали за те, чи компанія з вашої ніші і чи їй бракує саме вашої послуги; рахуємо ще до першого контакту.
band (60+ / 40–59 / below 40)	група за сумою балів	оцінку ліда переводимо в групу (наприклад, гарячий · теплий · холодний), і від групи залежить наступний крок
decision path	як ухвалюють рішення	Хто підписує і через які кроки проходить покупка в цій компанії.
confirmed deal potential, deal size	підтверджений бюджет	Сума, яку назвав сам клієнт; число, яке ми припустили, сюди не йде.
decision maker	ОПР (особа, що приймає рішення)	той, хто підписує: власник, операційний директор, керівник напряму
day-to-day owner, champion	той, хто працюватиме з вами щодня	керівник проєкту, BIM-менеджер, керівник розробки; йому важливе інше, ніж ОПР
nurture	догрів	регулярні корисні дотики до тих, хто ще не готовий купувати.
data confidence H / M / L	впевненість у даних: висока · середня · низька	Наскільки заповнені й перевірені поля компанії; від цього залежить, чи можна довіряти оцінці.
gate, fit gate, floor, hard gate, clamp	стоп-фільтр (мінімальний поріг)	Межа, нижче якої компанії не пишемо, хоч би що ще про неї було відомо.
opt-out, hard no	просить більше не писати	Людину одразу прибираємо з усіх кампаній, без спроб переконати.

Output

One row per lead: Name · Company · Prospect profile · Engagement · Deal · Decision path · Timeline · Access · Total · Band · Action · Data confidence · Gate fired (if any) · What is missing.

Sorted by band, then by what is missing — because the top of a "needs more information" list is usually a better use of an hour than the bottom of the qualified one.

Handoff

Band → action: a call, one qualifying reply, a nurture track, or disqualify. The reply itself is written by reply-objection-handler, which also triages it for intent and speed. Patterns across many scored leads — a whole band that never converts — belong in reply-audit and then back into hypothesis-builder.