Overview

Teach Claude to infer prospect-centric pain-point segments from HG data patterns — category sprawl, capability gaps, declining platforms, spend anomalies — and map each segment to the buyer persona most likely to feel the pain.

Use cases

Segments your AEs can actually use in a first call
Instead of 'ITSM Displacement' (seller talk), the skill produces 'The multi-vendor service desk with rising ticket complexity' (prospect talk). Each segment names the prospect's situation, not your product motion — so the AE can open with empathy, not a pitch.
Persona targeting that follows from the data
Each segment suggests who feels the pain most: observability sprawl targets the VP of IT Ops, security spend gaps target the CISO, legacy ERP decline targets the CIO. The contact-search step inherits these persona recommendations automatically.

View full skill

HG Pain-Point Segmentation

When to use

After building a target account list, to group accounts by shared pain points
When the user provides pain points, to classify accounts against them
When the user doesn't provide pain points, to infer them from data patterns

Naming convention

Segments are always prospect-centric — named by the prospect's situation, never the seller's motion.

GOOD (prospect-centric)	BAD (seller-centric)
"The multi-vendor service desk with rising ticket complexity"	"ITSM Displacement"
"The 5+ monitoring stack with no correlated alerting"	"Cross-sell opportunity"
"The mainframe estate running batch without CI/CD"	"Competitive win-back"
"HR platform transition window"	"Workday churn target"

Detection patterns from data

1. Category sprawl

Signal: Too many vendors in one category.
Query: WHERE category_leaf_name = '...' GROUP BY url_id HAVING count(DISTINCT vendor_id) >= N
Severity tiers:

Severe: 5+ vendors in category
Moderate: 3–4 vendors
Mild: 2 vendors (dual-stack)
Persona: VP of IT Ops, SRE Lead, Director of Engineering

2. Capability gap (has X but not Y)

Signal: Company has one capability but lacks the natural complement.
Query: CTE for "has X" companies + NOT IN for "has Y"
Examples:

Has ITSM but no ITOM → "Ops visibility gap"
Has cloud infra but no FinOps → "Cloud spend without governance"
Has CRM but no marketing automation → "Pipeline without top-of-funnel"
Persona: Depends on gap — IT Ops for ITOM, Finance/VP Infra for FinOps, CMO for marketing automation

3. Declining momentum

Signal: Negative intensity_momentum for a product.
Query: JOIN install_intensity_momentum_global with intensity_momentum < -20
Examples:

Declining Workday usage → "HR platform transition window"
Declining on-prem storage → "Storage modernization underway"
Persona: CIO, VP Digital Transformation, VP Infrastructure

4. Over-investment

Signal: Disproportionate spend in a category relative to peers or total IT.
Query: Spend data from company_spend compared to benchmarks
Examples:

Security > 15% of IT spend → "Security-heavy, looking for consolidation"
Persona: CISO, CFO, VP Security

5. Under-investment

Signal: Spend in a category below benchmark for company size/industry.
Examples:

Security < 5% of IT for critical infra → "Security spend gap"
Cloud < 10% of IT for a digital-native industry → "Cloud adoption laggard"
Persona: CISO (security), CIO (cloud), Board/Audit Committee (compliance)

Inference methodology

When the user does not provide pain points:

Sample the list — run company_technographic on the top 10–15 accounts by intensity
Find repeating patterns — look for common technology combinations, gaps, or anomalies across the sample
Name each pattern as a prospect-centric segment
Classify severity — severe / moderate / mild based on vendor count, momentum magnitude, or spend gap size
Suggest target persona per segment based on who owns the pain
Present for validation — show the user the inferred segments and ask them to approve, modify, or replace

When the user does provide pain points, classify each account against the provided segments using the same data signals.

ICP prioritization scoring

Before segmenting, score and rank each account so the user knows where to focus. Use these weights:

Factor	Weight	Signal
Install intensity	4	Strongest signal of technology engagement
IT spend	3	Direct proxy for tech budget
Technology fit	3	Presence of complementary/competing tech matching the criteria
Revenue tier	2	Higher revenue = larger deal potential
Industry match	2	Alignment with user's target industries

Score 0–100, then classify:

A-tier (80–100): Highest priority — strong signals across multiple factors
B-tier (50–79): Good fit — worth pursuing with the right angle
C-tier (below 50): Lower priority — include for completeness but deprioritize

Present the tiered list to the user before segmenting. This gives them a chance to prune C-tier accounts or promote B-tier ones they know are strategic.

Segment-to-persona mapping

Detected pattern	Suggested persona
Category sprawl in IT ops	VP IT Operations, SRE Lead
Security spend gap	CISO, VP Security
Legacy ERP / declining platform	CIO, VP Digital Transformation
Multi-cloud without orchestration	VP Infrastructure, Cloud Architect
HR tech transition	CHRO, VP People Ops
Data/analytics fragmentation	CDO, VP Data Engineering
Observability sprawl	VP Engineering, SRE Lead

Skill: HG Pain-Point Segmentation