Find which customer segment is expanding fastest
Images on this page are for illustrative purposes only. Example outputs are based on Lusha data, with personal details masked or abbreviated for privacy.
This Claude prompt cross-references your expansion data with verified Lusha firmographics to find the firmographic attributes that appear most in customers who expanded — and flags the customers in your base who match that profile but haven’t yet. The output is a segment expansion analysis with an ICP refinement recommendation and a shortlist of priority expansion targets.
Tools: Claude, Lusha, CRM data (pasted)
The prompt
This prompt may contain placeholders — look for [BRACKETS] and fill them in.
<context>
I want to understand which customer segment is expanding fastest in our base — not by revenue alone, but by the firmographic attributes that define who we sell to. I want to know if our fastest-growing customers share specific traits: industry, size band, function makeup, geography, or signals like funding stage. That tells us where to focus new business.
My customer base:
- Customer list: [PASTE COMPANY NAME, CURRENT ACV, EXPANSION ACV OR "NONE", SEGMENT — one per line]
- Our segments: [HOW YOU DEFINE THEM — e.g. SMB / Mid-market / Enterprise, or by industry]
- What counts as expansion: [UPSELL / NEW SEATS / NEW USE CASE / ALL]
- Timeframe: [LAST 6 MONTHS / LAST YEAR / LAST QUARTER]
</context>
<task>
1. For each customer that expanded in the timeframe, use Lusha to pull current firmographic data:
- Headcount at time of expansion
- Industry and sub-industry
- Geography
- Headcount in the function we sell into
- Any signals active around the time of expansion: funding, exec hire, headcount growth
2. Identify firmographic patterns across expanding customers:
- Which industry or sub-industry has the highest expansion rate?
- Which size band expands most frequently?
- Which function headcount range correlates with expansion?
- Any geographic concentration?
- Any signals that appear disproportionately in accounts that expanded vs those that didn't?
3. Compare expanding customers against non-expanding customers in the same segment:
- What firmographic attributes do expanding customers have that non-expanders don't?
- Is the pattern consistent across segments or specific to one?
4. Return a segment expansion analysis:
- Top 2-3 firmographic attributes most correlated with expansion
- The segment or sub-segment expanding fastest (by rate, not just volume)
- The signal most commonly present in accounts that expanded
- One ICP refinement recommendation based on the pattern
5. Flag any customer that hasn't expanded but matches the firmographic profile of your fastest-expanding segment.
</task>
<constraints>
- Base the analysis on Lusha-verified firmographic data, not CRM tags or rep notes.
- Correlation is not causation — flag patterns as signals worth testing, not proven rules.
- If fewer than 5 customers expanded in the timeframe, say so — the sample is too small to conclude.
- The ICP refinement recommendation must be specific and actionable, not generic.
</constraints>What you'll get back
The situation: A CS leader at a B2B SaaS company runs the analysis across 18 customers, looking at expansion over the last 6 months. 7 expanded. She wants to know if there’s a pattern she can use to sharpen both new business targeting and expansion prioritization.
Output: Two firmographic attributes dominate expansion. One sub-segment is expanding at 3× the rate of others. Four non-expanders match the profile and are flagged as priority targets.
Segment expansion analysis
18 customers analyzed · 7 expanded · 11 did not expand
Timeframe: Last 6 months. Firmographic data via Lusha.
Top attributes correlated with expansion
1. Sales team headcount between 30–60 reps 6 of 7 expanding customers had a sales team of 30–60 reps at the time of expansion. Only 2 of 11 non-expanders fell in that range. This is the strongest single firmographic predictor in the dataset.
2. Series B or later funding stage 5 of 7 expanding customers had raised a Series B or later. 3 of 11 non-expanders had reached that stage. Funded companies at the 30–60 rep scale appear to be the core expansion profile.
3. New CRO or VP of Sales hired in the 90 days before expansion 4 of 7 expanding customers had an exec hire in the buying function within 90 days of the expansion conversation opening. This signal appears to be a catalyst — the expansion didn’t precede the hire, it followed it.
Fastest-expanding sub-segment
Mid-market SaaS, 150–350 employees, Series B+, sales team 30–60 reps
Expansion rate: 5 out of 6 customers in this sub-segment expanded in the last 6 months — 83%. The next closest segment (enterprise, 500+ employees) expanded at 2 out of 5 — 40%.
The pattern is consistent: this isn’t the highest-ACV segment, but it’s the most reliable expander. A new CRO or VP of Sales joining a company in this band within the last 90 days is the single clearest trigger for an expansion conversation.
ICP refinement recommendation
Add “Series B+, sales team 30–60 reps” as a secondary ICP filter for expansion-focused prospecting. Companies that fit the primary ICP (industry, size, geography) but also match this sub-segment profile should be prioritized in new business targeting — they’re more likely to expand within 12 months of initial close.
Non-expanders matching the expansion profile — flag for CS outreach
| Company | ACV | Profile match | Why flagged |
|---|---|---|---|
| Finova Group | $180K | Series B, 42 sales reps | Matches core expansion profile exactly — no expansion conversation opened yet |
| Dune Analytics | $44K | Series B, 31 sales reps | Matches profile — new VP of Sales hired 6 weeks ago (signal not yet acted on) |
| Corelink SaaS | $95K | Series B, 38 sales reps | Matches profile — last expansion conversation was 8 months ago |
| Waverly Digital | $72K | Pre-Series B, 28 reps | Near-match — watch for next funding event |
Dune Analytics is the highest-priority flag: matches the profile and has a live new exec signal that hasn’t been acted on.
Firmographic data validated via Lusha connector, May 19. Customer data from pasted CRM export.
Why use Lusha in Claude
Expansion planning usually starts with which customers haven’t expanded yet rather than which ones are likely to. The difference is the firmographic layer. Lusha in Claude cross-references your expansion history with verified company data — headcount, funding stage, function size, exec signals — and surfaces the attributes that actually predict expansion rather than the ones your team assumes predict it. The non-expander flag at the end is the most actionable output: four customers who match your fastest-expanding profile but haven’t had an expansion conversation opened yet. That’s a CS outreach list, not an insight deck.
Data drawn from 300M+ verified contacts under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe.
FAQ
How many customers do I need for the analysis to be meaningful?
At least 10 total, with at least 5 that expanded. Under 5 expanders the prompt flags the sample as too small. At 5–10 expanders you get directional signals worth testing. At 15+ you get patterns worth acting on with confidence.
What if all my expanding customers look the same?
That’s the clearest possible output — it means your expansion motion is concentrated in one sub-segment. Use it to sharpen new business targeting: if 83% of your expanders are Series B SaaS with 30–60 sales reps, that’s the profile to weight in outbound.
How often should I run this?
Twice a year. Run it at the start of H1 and H2 to reset expansion priorities based on the previous period’s data. Running it more frequently doesn’t add much — expansion patterns shift over quarters, not weeks.
What's the difference between this and the expansion signals scan?
The expansion signals scan is a weekly tactical play — it looks for signals at specific accounts worth calling this week. This play is a strategic analysis — it looks across your entire customer base to find the firmographic pattern that predicts expansion. Use this one quarterly to set strategy, the signals scan weekly to execute it.
Can I run this for churn prediction too?
Yes — flip the analysis. Paste churned customers instead of expanded ones and look for the firmographic pattern that correlates with churn. The prompt structure works the same way; the insight is in which attributes appear disproportionately in churned vs retained accounts.
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