TLDR: Five Claude prompts for the motions that drive net revenue expansion in SaaS — finding which accounts in your book have a signal worth acting on this week, understanding which customer segment expands fastest, opening a cross-sell into an adjacent function with a warm intro from the existing champion, mapping new buying centers when a customer grows faster than the original contract scope, and recovering the relationship when your champion leaves before the renewal cycle catches up. All five run on the Lusha connector for Claude.
Why expansion in SaaS is a data problem, not a relationship problem
The land-and-expand motion sounds simple: close the initial contract, deliver value, expand when the customer is ready. The problem is that “when the customer is ready” is an event the customer rarely announces. A new VP of Sales joining a customer account is ready to audit the sales stack within 90 days — but the CSM only finds out if someone happens to mention it on a QBR call. A customer that closed a Series B six weeks ago has unlocked new vendor spend — but the contract sits unchanged because no one connected the funding event to a seat conversation.
Expansion in SaaS stalls not because the relationship is weak, but because the signal never surfaces. The right account gets deprioritized in favor of the noisiest one. The cross-sell conversation doesn’t happen because no one knows who runs the adjacent function. The renewal arrives as a surprise because the champion left three months ago and no one found a replacement.
The five prompts in this article use Lusha’s verified contact data and signals layer to surface these events before they become missed opportunities: a weekly scan for expansion signals across the book, a firmographic analysis to find which segment expands fastest, a cross-sell map with warm intro paths from the existing champion, a buying center mapping play for customers who outgrow the original contract scope, and a champion replacement finder for the 30-60 day window that determines whether a renewal goes smoothly or sideways. All five connect through the Lusha connector for Claude.
Connect Lusha to Claude in two minutes →
Section 1: Finding the expansion opportunity before the customer tells you about it
The most common expansion mistake in SaaS isn’t pitching too early — it’s not pitching at all because no signal surfaced before the QBR. These two prompts fix that: the first runs a weekly tactical scan across the book for accounts with a signal worth acting on this week; the second runs a quarterly strategic analysis to find the firmographic profile of customers who actually expand so you can find more of them.
Prompt 1: Scan your book of business for expansion signals
This prompt scans every account in a CS or AM’s book using Lusha’s signals layer and returns a ranked shortlist — STRONG, POSSIBLE, MONITOR — based on three signal types that tend to precede an expansion conversation in SaaS: headcount growth in the user team or adjacent functions, funding rounds that unlock new vendor spend, and executive arrivals that trigger a stack audit. The output includes a one-line conversation opener per account specific to the signal, not a generic check-in template.
The STRONG / POSSIBLE / MONITOR classification keeps the list honest. If fewer than three accounts have a STRONG signal in a given week, the prompt says so rather than inflating ratings to fill a list.
<context>
I want to find expansion opportunities in my current book of business before I go looking for new logos. I'm looking for accounts that have a signal worth acting on this week — not accounts I've already qualified for expansion.
My book of business:
- Account list: [PASTE COMPANY NAMES, ONE PER LINE]
- What I sell: [PRODUCT / USE CASE]
- How we typically expand: [SEATS / NEW TEAMS / NEW USE CASES / GEOGRAPHY]
- Any accounts already in an active expansion conversation: [LIST OR "NONE" — skip these]
</context>
<task>
For each account, use Lusha to scan for signals that typically precede an expansion conversation:
1. Headcount growth signals
- Growth of 15%+ in the team or function currently using the product
- New hires in a function or geography we don't currently sell into
- A spike in open roles in a relevant function
2. Funding and investment signals
- New funding round in the last 90 days (Series B+ typically unlocks new vendor spend)
- Acquisition of another company as the buyer
3. Executive arrival signals
- New VP or C-level hire in a function we sell into but don't currently have coverage in
- New CRO, VP of Sales, or Head of GTM (typically audits the sales stack within 90 days)
- New CTO or VP of Engineering (typically expands the technical tooling stack)
4. For each signal found, rate the expansion opportunity:
- STRONG — signal directly suggests a near-term expansion conversation
- POSSIBLE — signal is worth a proactive mention on the next call
- MONITOR — signal is early or indirect — flag for next quarter
5. For each STRONG or POSSIBLE account, return:
- The specific signal Lusha surfaced
- The expansion angle it suggests (more seats, new team, new use case)
- A one-line conversation opener specific to the signal
Return:
1. A ranked list: STRONG first, then POSSIBLE, then MONITOR
2. A summary line: how many accounts have a STRONG or POSSIBLE signal out of the total scanned
3. One flag if any account shows both an expansion signal and a churn signal
</task>
<constraints>
- Only surface signals Lusha actually returns. Don't infer expansion readiness from revenue or industry alone.
- The conversation opener must reference the specific signal — not a generic check-in.
- If an account shows a strong expansion signal but also a structural churn risk, flag it — don't include it in the expansion shortlist without the caveat.
- If fewer than 3 accounts have a STRONG signal, say so. Don't inflate ratings to fill a list.
</constraints>Prompt 2: Find which customer segment is expanding fastest
The signal scan tells you which accounts to call this week. This prompt tells you which type of account is most likely to expand at all — based on verified firmographic data, not gut feel or CRM tags.
This prompt cross-references your expansion history with Lusha firmographics across the full customer base: headcount at time of expansion, industry and size band, function makeup, funding stage, and any signals active in the 90 days before the expansion conversation opened. The output identifies the top two or three firmographic attributes most correlated with expansion, names the sub-segment expanding fastest by rate (not just volume), and flags every non-expander in the book that matches the expansion profile — turning a strategic analysis into an actionable CS outreach list.
<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>Section 2: Opening the expansion conversation inside the account
Knowing which accounts have an expansion signal is half the problem. The other half is knowing who to talk to and how to get in front of them. These two prompts handle the two most common structural expansion situations in SaaS: selling a second product to a different function at an existing customer, and finding the new buying centers when a customer’s growth outpaces the original contract scope.
Prompt 3: Identify cross-sell opportunities by adjacent function
The cross-sell motion in SaaS fails for one specific reason: the CSM has the relationship in one function, the second product sells to a different function, and the introduction never happens. The contact list is there — it just never gets used.
This prompt takes an existing customer, the product already deployed and the function it’s deployed in, and a second product being pitched — and returns the verified leadership in the target function, a buyer role classification per contact (economic, operational, technical evaluator), and a warm-introduction path from the existing champion for each one. A cross-sell introduction routed through the existing champion converts at a multiple of a cold outreach from the CSM. The prompt’s most valuable output is the intro path, not the contact list.
<context>
My company sells multiple products. I have an existing customer using Product A. I want to identify the right buyer at the same customer for Product B, which lives in an adjacent function.
The existing engagement:
- Customer name / domain: [CUSTOMER]
- Product A (already deployed): [PRODUCT NAME — e.g., sales productivity platform]
- Function we sold Product A into: [Sales / Engineering / Marketing / etc.]
- My champion at the customer: [NAME, TITLE]
The cross-sell:
- Product B (the cross-sell): [PRODUCT NAME — e.g., marketing intelligence]
- Function that owns Product B: [Marketing / RevOps / Finance / etc.]
- Buyer profile for Product B: [seniority level + role family]
</context>
<task>
1. Use Lusha to find the verified leadership in the cross-sell function at the customer account:
- The C-suite owner of the function (CMO, CTO, CFO — the right one for the cross-sell product)
- 1-2 VP-level leaders running operational areas inside the function that match the product's use case
- 1 Director-level operator who is likely the day-to-day evaluator
2. For each contact, return:
- Full name and current title
- Validated email
- Direct dial or mobile (with DNC where applicable)
- Buyer role for the cross-sell (economic, operational, technical evaluator)
- Warm-intro path — one sentence on how my existing champion can route the introduction
3. Surface the cross-sell rationale per buyer — one sentence on why this specific person, in this specific function, is the right buyer for Product B given the customer's current state and recent signals.
4. If recent buying signals on the customer (funding, hiring surge in the new function, product launches) make the cross-sell timing especially strong, flag those signals in the response.
</task>
<constraints>
- The cross-sell is to an adjacent function, not the same function. If Product B sells to the same buyer as Product A, this is upsell, not cross-sell — use the multi-thread prompt instead.
- The warm-intro path matters. A cross-sell introduction routed through the existing champion converts at a multiple of a cold outreach. Surface a specific intro route (e.g., "the existing champion's RevOps peer is the natural bridge").
- Do not invent contacts or signals. Surface only what Lusha returns.
- Cap at 4 target buyers per cross-sell. The output should fit on a single discovery brief.
</constraints>Prompt 4: Map new buying centers inside an existing customer
Fast-growing SaaS customers routinely expand faster than their contracts. A customer whose original deal covered the US Sales team opens an EMEA office. A platform that sold to Engineering gets acquired by a larger company and the acquired entity runs a separate tech stack. A customer that started as one business unit launches a new product line with its own VP and its own vendor budget.
The original contract covers one buying center. The customer is now running three. This prompt fires when a structural trigger — geographic expansion, new business unit, M&A as acquirer, function split — creates a new buying center inside an existing account. It uses Lusha’s signals layer to detect the trigger if it isn’t specified, maps the verified leadership running each new buying center, and returns an engagement angle specific to each one. Not “deepen the relationship” — but “the EMEA security platform contract doesn’t extend to the new Dubai office; the VP of EMEA is the right first call before the regional vendor decision gets locked.”
<context>
My existing customer just created one or more new buying centers since my original deal closed. The original contract covers the original buying center, but the customer is now running more of them.
My customer and known scope:
- Customer name / domain: [CUSTOMER]
- My function we sold into: [Sales / Engineering / Marketing / etc.]
- Original buying center we serve: [region, BU, or function — e.g., "US Sales", "North America Engineering"]
- Trigger event (if known): [new region, new business unit, M&A as acquirer, function split — leave blank to auto-detect]
</context>
<task>
1. If the user named a specific trigger event, focus the search there. If not, use Lusha's signals layer to surface recent triggers that would create new buying centers:
- Geographic expansion (new VP of EMEA, Head of APAC, new country office)
- Business unit launch (new VP or GM of a product line we don't currently serve)
- M&A as acquirer (newly acquired entity now part of the customer's footprint)
- Function split (RevOps separating from Sales Ops, AI Engineering becoming its own org)
2. For each new buying center surfaced, find the verified leadership running it:
- The owner of the buying center (VP, Head of, Senior Director)
- One adjacent function leader at the same regional or BU level (channel, marketing, partnerships)
- One technical evaluator if the buying center is product-led (Solutions Engineering, Customer Success leader for that region)
3. For each contact, return:
- Full name and current title
- Validated email
- Direct dial or mobile (with DNC where applicable)
- Buying center role (owner / adjacent / technical evaluator)
- Structural trigger — one sentence on what event created this buying center
- Engagement angle — one sentence on how the original engagement extends
4. Order output by buying center, then by role inside each.
5. Flag any buying center where Lusha returns zero matched leadership — that may mean the buying center is being run by the original team (no expansion opportunity) or it's too new to be indexed.
</task>
<constraints>
- A new buying center is a structural change, not just a hire. A new VP joining the existing US team is a relationship signal, not a new buying center. A new VP of EMEA opening the first European office is a new buying center.
- Do not invent triggers. Surface only what Lusha returns.
- For very large customers (5,000+ employees), cap at 3 buying centers per run to keep the output actionable.
- The engagement angle should be specific: extending a US security platform contract to cover EMEA is concrete; "deepen the relationship" is not.
</constraints>Section 3: Protecting the relationship when the champion leaves
Prompt 5: Find the new champion when yours moves on
A customer champion leaving is the single highest-impact event in a CSM’s book. The 30-60 day window between “your contact moved on” and “the new person owns the renewal” is when the relationship is most exposed. In SaaS, where multi-year renewals can hinge on a single person’s institutional knowledge of why the platform was purchased, finding the replacement quickly isn’t a nice-to-have — it’s risk mitigation.
The challenge is that a straightforward company search isn’t enough. Lusha’s company-side data can lag the contact-side data by weeks, which means contacts who’ve already moved to a different employer may still appear in the previous company’s contact list. This prompt runs a two-pass verification: a company search for same-function candidates at the right seniority, followed by an individual record cross-check to confirm current employment for each one. Any candidate whose individual record shows they’ve also moved gets flagged and excluded. The output is a short list of verified replacement candidates ranked by inheritance likelihood — HIGH, MEDIUM, or LOW — with the reasoning surfaced so the CSM can adjust.
<context>
My champion at an existing customer just left the company. I need to find the verified replacement candidates — people in the same function and at the same or similar seniority — so I can re-engage before the renewal cycle reaches them first.
My departed champion:
- Name: [DEPARTED CHAMPION'S NAME]
- Former title at the customer: [TITLE]
- Customer account: [CUSTOMER NAME / DOMAIN]
- Function they owned: [Sales / RevOps / Engineering / etc.]
</context>
<task>
1. Use Lusha to find current contacts at the customer account who match the function and seniority of my departed champion. Pull 4-6 candidates including same-function peers and one level up (if the champion was VP, also surface the C-suite owner of the function).
2. CRITICAL — cross-validate each candidate. For each contact returned by the company search, look up their individual record to confirm:
- Their current employer is still the customer (catches contacts who also moved)
- Their job start date and title align with someone actively in the role
- Their previous job is consistent with internal progression or external hire
3. For each verified candidate, return:
- Full name and current title
- Validated email
- Direct dial or mobile (with DNC flag where applicable)
- Tenure in current role (from job start date)
- Previous job (signals trajectory)
- Inheritance likelihood — HIGH / MEDIUM / LOW based on scope, seniority, and tenure
4. Flag any candidate whose individual record shows they ALSO moved to a different company. The company search may still list them if Lusha's data hasn't fully refreshed. Surface this honestly and exclude from the replacement candidates.
5. If no clean candidate emerges in the function, surface the one-level-up contact (the C-suite owner of the function) as the right initial point of re-engagement.
</task>
<constraints>
- Cross-validation is the critical step. A company search may return contacts whose individual records show they moved on. Do not list them as candidates without confirming current employment.
- Inheritance likelihood is a judgment based on tenure, scope, and seniority — not a Lusha-returned field. Surface the reasoning so the user can adjust.
- Do not invent contacts or fabricate inheritance signals.
- If the function returns zero genuine candidates (everyone in the function also moved or the role is empty), say so plainly and surface the executive sponsor as the re-engagement starting point.
</constraints>The pattern across all five prompts
Expansion in SaaS fails quietly. No one decides not to expand an account — it just never becomes the conversation because the signal didn’t surface, the intro to the adjacent function didn’t happen, the new buying center went unnoticed, or the champion left and the replacement relationship never got started. Every prompt in this article is designed to catch one of those failure modes before it becomes a missed quarter.
The pattern across all five is the same: verified data driving a specific action, not a list of names to work through. The expansion signal scan returns a ranked shortlist and a conversation opener tied to the actual signal. The segment analysis returns a non-expander flag that is a CS outreach list, not an insight deck. The cross-sell prompt returns an intro path, not just a contact. The buying center prompt returns an engagement angle, not a relationship recommendation. The champion replacement prompt returns a verified candidate list where every name has been cross-checked — because the company-side data and the individual-side data sometimes disagree, and acting on the wrong contact costs more than finding the right one.
The Lusha connector provides the verified contact and firmographic layer that makes all five prompts work — 300M+ verified contacts, compliant under GDPR, CCPA, SOC 2, ISO 27701, ISO 31700, and TRUSTe. No guessed fields, no format assumptions. If Lusha can’t verify it, the prompt flags it rather than inventing it.
Where these prompts live
All five prompts are in the expansion section of Lusha Plays.
