customer-expansion-scoring

name	customer-expansion-scoring
title	Customer expansion scoring
description	Use this skill when scoring a paying customer (closed-won account) for expansion. It evaluates product usage depth, persona quality, and expansion headroom to produce an expansion tier with short actionable reasoning. Designed as a sub-skill of your lead-scoring methodology — the parent runs the ACV assessment first and this returns a tier to it.
category	Deals

Template placeholders

Replace every {{...}} before enabling. See the setup checklist reference for the full setup list.

• {{PRODUCT}} — Your product name
• {{PARENT_SCORING_SKILL}} — The parent lead-scoring skill this returns to (runs ACV assessment before this)
• {{CRM}} — Your CRM (e.g. HubSpot)
• {{DEPTH_SIGNALS}} — Your product's REAL usage-depth indicators (see Signal Stack #2 — must be replaced)
• {{AUTO_CREATED_ARTIFACTS}} — Objects your onboarding auto-creates for every account (excluded from depth)
• {{FOUNDER_CONTENT}} — Whose LinkedIn content signals post-conversion advocacy (usually a founder)
• {{TIER_SCALE}} — Tiers, low→high (default: Bronze / Silver / Gold / Diamond)
• {{INACTIVITY_CAP}} — Inactivity window that caps the tier (default: 30 days → cap at Silver)

Use when the account is a paying customer (Closed Won stage).

Goal: How deeply are they using {{PRODUCT}}, and how much bigger could this account get?

The parent skill ({{PARENT_SCORING_SKILL}}) has already run the ACV assessment and applied the ACV tag. Reference those results here — do not re-research ACV dimensions covered there.

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Signal Stack (highest to lowest weight)

Gather all available signals from account memory, product event history, and {{CRM}}.

1. Meeting — a completed call signals active relationship depth. Extract: topics discussed (expansion, new use cases, additional team members mentioned), questions and objections, action items, attendee roles. A meeting that surfaces new departments, budget conversations, or advanced use cases = strong expansion signal.

2. Product depth — the real indicators of depth for {{PRODUCT}} are {{DEPTH_SIGNALS}}.

   Reference implementation (an AI GTM platform) used:

   • Integrations/tools connected beyond defaults — each = deliberate setup effort
   • Automations/triggers created beyond the default one — variety and count signal how much the user is actually building on the product
   • Usage credits consumed (volume = active usage)
   • Multiple team members active

   What does NOT count as product depth: {{AUTO_CREATED_ARTIFACTS}} — anything your onboarding auto-creates is present on virtually every account. Do NOT treat these as engagement or power-user signals. Judge depth only by artifacts the customer deliberately built.

3. Chatbot / support interactions — technical or advanced use-case questions signal active exploration

4. Website visits (post-signup) — return visits to docs, pricing (plan upgrade signal?), or feature pages

5. LinkedIn engagement — post-conversion engagement with {{FOUNDER_CONTENT}} signals advocacy or a deepening relationship

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Persona Quality

Strong: CRO, VP Sales, Head of Sales, RevOps, GTM Engineer, Growth Lead, CEO (small co), Agency Owner (tune to your buying committee) Weak: Product, Engineering, Finance — requires stronger product signals to compensate Multiple strong personas actively using {{PRODUCT}} = significant positive multiplier for the top tier

Persona quality reflects expansion potential. A VP Sales actively building on {{PRODUCT}} is a more valuable expansion signal than a junior IC doing the same. Always note the persona role in your reasoning.

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Expansion Headroom

Assess how much room this account has to grow:

• Current seat count vs. total relevant org size (from the parent ACV assessment)
• Teams or divisions not yet using {{PRODUCT}}
• Growth signals: recent funding, active hiring, new leadership joins
• Are they approaching credit, usage, or seat limits?

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Hard Caps

No product usage signals in the last {{INACTIVITY_CAP}} → cap at the second-lowest tier (churn risk takes priority over expansion scoring). Note explicitly: "[window] inactivity — capped, churn risk flagged."

Senior Stakeholder Exception (overrides seat-count limitations): If the registered user is a very senior stakeholder (VP, SVP, C-suite, or equivalent Director-level executive) AND the company is high-quality ($10M+ funded OR 100+ employees OR well-known brand with a clear motion in your market), score the second-highest tier regardless of team size or seat count. Seniority + company quality is itself a strong expansion signal — this person can unlock budget and teammates.

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Tier Definitions

(Defaults shown for a Bronze/Silver/Gold/Diamond scale — remap to {{TIER_SCALE}}.)

Diamond: Deep product usage (multiple integrations connected, multiple deliberate automations active, multi-user) + high ACV potential (per parent assessment) + clear expansion headroom. This account could 3–5x in ARR.

Gold: Meaningful usage (1–2 integrations connected, active workflows running) + medium-to-high ACV potential + some expansion headroom. Nurture toward deeper adoption.

Silver: Active customer but shallow usage (minimal integrations, few automations, limited engagement) OR low ACV potential with limited expansion ceiling.

Bronze: Barely active — minimal or no usage in recent weeks, no integrations, low ACV potential. Prioritize re-engagement or churn prevention before expansion.

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Output

Produce: tier + 2–3 sentence reasoning (usage depth, persona quality, expansion headroom, ACV ceiling from parent). Then return to {{PARENT_SCORING_SKILL}} and continue its remaining steps.

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What good looks like

A great output is a tier plus two-to-three sentences a CSM can act on immediately, citing all four dimensions: usage depth (deliberately built artifacts only), persona quality, expansion headroom, and the ACV ceiling from the parent assessment. Inactive accounts get capped with the churn risk flagged explicitly, and senior-stakeholder exceptions are called out as such.

Mediocre looks like: a tier derived from seat count alone, depth credited for onboarding auto-created objects, ACV re-researched when the parent already assessed it, or essay-length reasoning nobody reads.