Pain-Qualified Segments from Calls

The workflow

Play 21

Pain-Qualified Segments from Calls

1Gather calls and customers

Sales callsBuyer side

DiscoveryDemoClosing

What was true for the buyer when they chose to talk.

From

Fathom

Closed won and lostBacktest set

Close dateReasonAccount

The customers every segment is checked against.

From

HubSpot

2Mine the buying situations

One schema per call

SituationWhy nowAlready triedVerbatim painOutside-in fingerprint

Every situation is counted across calls. The buyers' exact phrases go into a language bank.

Read

Claude

3Situation plus tension

Segment anatomyTwo facts pulling apart

Doing

The thing they are doing

Hurts

The thing that makes it hurt

Now

A deadline, target or season

Number

The figure that costs the quarter

4Membership test, field by field

Condition isUse it as

Public: the field states it→Filter and copy

Proxy: the field suggests it→Score and ask, never assert

Not public→A question for the call

5Measure before building

Gate1

Count it

Free counts, with the query and the date recorded.

× Too small or too broad

Gate2

Eyeball five rows

Open the evidence. Two of five wrong means the test is wrong.

× Fix the test

Gate3

Backtest for lift

Customers in the 90 days before they bought, against a TAM sample.

× Low lift

6Build the list and the opener

Refreshable list

Discover on one filter, score on the rest. Unknown never passes. Emails re-verified on every refresh.

Find

Explorium

Evidence

Exa

Build

Clay

Verify

BounceBan

Opener in their words

Names the situation, says where you saw it, asks if the read is right.

Email

Smartlead

LinkedIn

HeyReach

Segments with measured lift, in the buyers' own words

The stackFathomHubSpotClaudeExploriumExaClayBounceBanSmartleadHeyReach

Oriel · GTM plays library

Open full size

The diagram as text

1. 1

   Gather calls and customers

   Sales calls (buyer side): Discovery, Demo and Closing. What was true for the buyer when they chose to talk. From: Fathom. Tool: Fathom.

   Closed won and lost (backtest set): Close date, Reason and Account. The customers every segment is checked against. From: HubSpot. Tool: HubSpot.

2. 2

   Mine the buying situations

   One schema per call: Situation, Why now, Already tried, Verbatim pain and Outside-in fingerprint. Every situation is counted across calls. The buyers' exact phrases go into a language bank. Read: Claude. Tool: Claude.

3. 3

   Situation plus tension

   Segment anatomy (two facts pulling apart): Doing: The thing they are doing. Hurts: The thing that makes it hurt. Now: A deadline, target or season. Number: The figure that costs the quarter.

4. 4

   Membership test, field by field

   Condition is → Use it as

   • Public: the field states it → Filter and copy
   • Proxy: the field suggests it → Score and ask, never assert
   • Not public → A question for the call
5. 5

   Measure before building

   Gate 1: Count it. Free counts, with the query and the date recorded. Rejected: Too small or too broad. Tools: Explorium.

   Gate 2: Eyeball five rows. Open the evidence. Two of five wrong means the test is wrong. Rejected: Fix the test.

   Gate 3: Backtest for lift. Customers in the 90 days before they bought, against a TAM sample. Rejected: Low lift. Tools: HubSpot.

6. 6

   Build the list and the opener

   Refreshable list: Discover on one filter, score on the rest. Unknown never passes. Emails re-verified on every refresh. Find: Explorium. Evidence: Exa. Build: Clay. Verify: BounceBan.

   Opener in their words: Names the situation, says where you saw it, asks if the read is right. Email: Smartlead. Linkedin: HeyReach.

   Outcome: Segments with measured lift, in the buyers' own words. Tools: Clay.

The stack: Fathom, HubSpot, Claude, Explorium, Exa, Clay, BounceBan, Smartlead and HeyReach.

Open the diagram full size (1180 px wide)

The play in six steps

1. 01

   Step 1: Gather the calls and the customers

   The best segments come from what was true for real buyers at the moment they decided to talk or to buy.

   • Pull sales call transcripts from Fathom. Calls with buyers outrank internal kickoffs, which only capture what the team believes.
   • Export closed-won and closed-lost deals from HubSpot with close dates and reasons. This is the backtest set.
   • Tag every piece of context with its source. Anything without one is an assumption.
2. 02

   Step 2: Mine the buying situations

   Read each call for the situation the buyer was in, not their industry, and keep the words they used.

   • Claude extracts, per call: the situation, why now, what they already tried, and verbatim pain lines with the speaker.
   • Record the outside-in fingerprint: what a stranger could have seen beforehand, such as a job post, a filing, a tool or a hire.
   • Run large call sets in parallel batches against one shared schema.
   • Count every situation. 'Came up in 12 of 100 calls' is a finding. 'Came up a lot' is not.
   • Keep a language bank of the exact phrases buyers used for the pain.
3. 03

   Step 3: Write each segment as a situation and a tension

   One signal is noise every vendor fires on. Two facts pulling against each other is where the pain lives.

   • Situation: one sentence about something that happened to them, not something they are.
   • Tension: what they are doing, against what makes it hurt. For example, hiring sales reps with nobody owning the system those reps will work in.
   • Amplifier: the deadline, target or season that makes it hurt now.
   • The number: the figure that, past a threshold, costs someone the quarter. No such number means the segment is incremental, and the spec says so.
   • Kill segments that are vague, unsourced, rest on a single signal, or describe a pain the offer does not fix.
4. 04

   Step 4: Write the membership test field by field

   A segment is only real if the next person can rebuild it from the spec alone.

   • Every condition gets a source, a field, an operator and a threshold.
   • Public conditions (the field states it) filter the list and can appear in copy.
   • Proxy conditions (the field suggests it) score and prompt a question. Never state them as fact.
   • Not-public conditions leave the test and become discovery questions.
   • A segment whose core tension rests on a not-public condition is a discovery question, not a segment.
5. 05

   Step 5: Measure before building

   Three checks turn a hypothesis into a measured segment, and they are cheap next to a wasted campaign.

   • Count it with free or near-free counts, such as an Explorium preview, and record the query and the date.
   • Open five rows the test lets in and confirm each one is really in the situation. Two failures out of five means fix the test first.
   • Backtest: what share of recent closed-won accounts were in the situation in the 90 days before they bought, against a random TAM sample. That ratio is the lift. Low lift means a firmographic in disguise.
   • Label each segment measured, partly measured or hypothesis.
6. 06

   Step 6: Build the list and the opener

   A measured segment becomes a list that refreshes and a message written in the buyers' own words.

   • Discover rows on one filter, then score on the rest. Stacking discovery filters collapses the list.
   • Build in Clay with Explorium for companies and people and Exa for evidence that needs the open web. Unknown is an allowed answer and never counts as a pass.
   • Set the refresh cadence from how long a row stays true, and re-verify emails with BounceBan on each refresh.
   • Open on the situation with language-bank vocabulary, say what you saw and where, and ask if the read is right.
   • Send by email through Smartlead and on LinkedIn through HeyReach. Never quote one buyer to another.

Tools used

The nine tools this play names. Each links to the other plays that use it.

FathomHubSpotClaudeExploriumExaClayBounceBanSmartleadHeyReach

Related plays

Play 20

Call-Transcript Signal Extraction

1Collect the transcripts

Recorded calls

DiscoveryDemoClient calls

Every call names people nobody ever follows up with. Any recorded call works.

From

Fathom

2Extract the people named

Third-party extraction

PersonCompanyTitleVerbatim quote

Finds everyone mentioned who is not on the call. Built from what was said, never from speaker labels, and tolerant of names the transcript mangled.

Read

Claude

3Deduplicate and merge

Mention foundWhat happens

Same name, same company→Exact match, merged

Misspelled name, same company→Fuzzy match, merged

Three mentions, three quotes→One record, every quote kept

Context quotes are merged, never dropped.

4Apply the resolvability gate

Gate1

Full name

First and last name both heard. A first name alone is held, not guessed.

× Held: first name only

Gate2

Named company

A real company is attached to the person.

× Held: no real company

Gate3

Inferable title

The role can be worked out from the call.

× Held: no title

5Resolve, enrich, check it is current

Match person to company

Only rows that passed the gate get here, so only they cost money.

Match

ClayApollo

Verified email, current role

People are often mentioned by an old title. Confirm the role before anyone writes.

Find

Findymail

Role

LinkedIn

Verify

BounceBan

6Push to Clay and tell the rep

Webhook into Clay

One call per resolvable lead, with the quote attached. Clay owns the sequence from here.

Push

Clay

Tell the rep

The rep who ran the call sees who came out of it.

Notify

Slack

Named leads, with the quote attached

The stackFathomClaudeClayApolloFindymailLinkedInBounceBanSlack

Oriel · GTM plays library

Play 20Outbound

Call-Transcript Signal Extraction

6 steps · Email, CRM

+2 more

Play 23

HubSpot Account Tiering & Scoring

1Learn from the deals you already closed

Closed won and closed lost, last 12 to 24 months

Closed wonWhat good looks like

ACVSales cycleExpansion

Start with the highest revenue customers. Look for patterns in industry, size and geography.

Closed lostWhat to rule out

Poor-fit industrySize mismatch

The disqualification reasons that keep coming back.

2Define segments and scoring logic

SegmentsOne per account

Company sizeIndustryBusiness model

Scoring inputWhat it checks

Firmographics→Size, industry, location

Technographics→Tech stack

Signals→Recent activity at the account

3Build the Clay model and tier

Core fields

CompanyDomainLinkedIn URL

Enrichment fields

EmployeesRevenueIndustryTech stack

Logic fields

SegmentTierLast enriched

Clay formulas assign the tier

Tier 1Strong fit

Tier 2Good fit

Tier 3Low fit

4Sync tiers into HubSpot

New HubSpot properties

Account tierSegmentKey firmographics

Company and contact data mapped field by field, so the sync never creates duplicates.

Data

Clay

Emails

Findymail

Verify

BounceBan

Write

HubSpot

5Keep tiers live with workflows

Tier workflowsProperty trigger

Company workflows with re-enrollment on, so the tier changes when the data does.

FallbackMissing tier

Accounts without a tier are flagged, not left to slip through.

FreshnessQuarterly

Re-enrich any account last enriched over 90 days ago, and re-check its contacts for job changes.

Job

LinkedIn

Monthly data review, quarterly strategy update

Every account tiered in HubSpot

The stackHubSpotClayFindymailBounceBanLinkedIn

Oriel · GTM plays library

Play 23ABM · Outbound

HubSpot Account Tiering & Scoring

7 steps · CRM

Play 01

Signal Activation

1Collect signals from every party

First partyYour own data

Site visitsProduct usageEvents

Web

WarmlyWebflow

Product

AmplitudeMixpanel

Events

Luma

Second partyShared with you

LinkedIn activityReview sitesPartners

Social

TrigifySales Navigator

Third partyPublic web

Tech stackNewsHiring

Tech

TheirStack

News

ExploriumPerplexity

Arrives by webhook, API or native sync, each with a date and a source

2One table, one row per signal

Signal tableClean first

SignalCompanyContactDate

Domains, LinkedIn URLs and titles are normalized before anything else, then duplicates are removed.

Fill the gaps, then check they are still true

FirmographicsContact detailsTech stack

Enrich

ClayBetterContactFindymail

Job change

LinkedIn

Verify

BounceBan

3Look up the CRM, then qualify

Who owns it todayCRM lookup

Account matched by domain, contact by email, current owner and open deals noted.

Lookup

HubSpotSalesforce

Gate1

Account and persona fit

Claude checks the company and the person behind each signal against the ICP.

× Not ICP

Gate2

Score into tiers

Fit, tech stack and signal strength set Tier 1, 2 or 3.

× Weak signal

Segmented by size, industry, region and business model

4Route to a rep and write it back

Account statusWho gets the signal

Owned in the CRM→The existing owner

No owner yet→Next rep on the live round-robin

Open deal→Deal owner, as context only

Each signal written as its own CRM event, many per accountHubSpot or Salesforce

5Activate on every channel

Tier 1Priority accounts

Same-day human playNow

A rep works the account today with a short custom pitch.

Alert

SlackHubSpot

Call

Nooks

Pitch

Gamma

Tier 2Good fit

SequencesAuto

Email plus a LinkedIn connection and DM, opening on the signal topic.

Email

Smartlead

LinkedIn

HeyReach

All tiersStay visible

RetargetingAuto

The same accounts see you in their feed and in search while the sequence runs.

Ads

LinkedIn AdsGoogle Ads

Every signal reaches the right rep

The stackWarmlyWebflowAmplitudeMixpanelLumaTrigifySales NavigatorTheirStackExploriumPerplexityClayBetterContactFindymailLinkedInBounceBanClaudeHubSpotSalesforcen8nSlackNooksGammaSmartleadHeyReachLinkedIn AdsGoogle Ads

Oriel · GTM plays library

Play 01Outbound · ABM · Inbound

Signal Activation

11 steps · Website, LinkedIn, Email, CRM, Call, Ads

+20 more

All GTM plays