TLDR: Three Claude prompts that handle the whole account targeting workflow for SaaS teams — ICP scoring before the team works a list, building a verified ABM list from a campaign brief, and catching inbound leads from target accounts before they get lost in the MQL queue. All three run on the Lusha connector for Claude.
Why account targeting in SaaS needs verified data
SaaS sales teams waste more time on list quality than on actual selling. A rep assigned 15 accounts from a conference might spend the first two days discovering that three are too small, one was acquired, and two have no verified contact in the right function. That’s a list quality problem, not a selling problem.
The three prompts in this article use Lusha’s verified firmographic data and signal layer to solve list quality before the team touches the list — scoring accounts against ICP criteria, building ABM lists directly from campaign briefs, and catching inbound signals from target accounts before they disappear into a standard MQL queue. All three connect to Claude through the Lusha connector
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Prompt 1: ICP score a new target list before the team works it
In SaaS, a conference list or marketing-sourced account list lands on a rep’s desk with no tier information, no signal context, and no verified contacts. The rep works it in arbitrary order. This prompt ends that.
It scores every account against your ICP criteria using Lusha’s verified firmographic data — headcount, headcount trend, industry, geography, function size, and active signals — and returns a four-tier list: STRONG FIT, FIT, PARTIAL FIT, and DISQUALIFIED. Every workable account gets a verified first contact attached. Every disqualified account gets a one-line reason. And a pattern flag at the bottom tells whoever sourced the list what to fix before the next one arrives.
<context>
I have a new list of target accounts I want to score against our ICP before handing it to the team. I want to know which accounts are worth working, in what order, and why — based on verified firmographic data, not assumptions.
My list:
- Account list: [PASTE COMPANY NAMES OR DOMAINS — one per line]
- Our ICP criteria:
- Industry: [LIST TARGET INDUSTRIES]
- Company size: [HEADCOUNT RANGE — e.g. 100–1000 employees]
- Geography: [REGION OR COUNTRY]
- Function we sell into: [SALES / MARKETING / FINANCE / IT / OPS / OTHER]
- Signals that indicate fit: [RECENT FUNDING / HEADCOUNT GROWTH / NEW EXEC / OTHER]
- Disqualifiers: [CRITERIA THAT RULE AN ACCOUNT OUT — e.g. too small, wrong industry, acquired]
</context>
<task>
1. For each account on the list, use Lusha to pull verified firmographic data:
- Current headcount and headcount trend (growing, stable, contracting)
- Industry and sub-industry
- Geography and HQ location
- Headcount in the function we sell into
- Any active signals: recent funding, exec hire in the relevant function, M&A activity
2. Score each account against the ICP criteria:
- STRONG FIT: meets all core ICP criteria and has at least one active signal
- FIT: meets core ICP criteria, no active signal
- PARTIAL FIT: meets some criteria, missing one or two — flag which ones
- DISQUALIFIED: fails a disqualifier criterion — flag why
3. For each STRONG FIT and FIT account, identify the right first contact:
- The person most likely to own the problem we solve, given the function we sell into
- Verified title, work email, and direct phone where available
- Flag if no verified contact found in the relevant function
4. Return a scored list:
- STRONG FIT accounts first, ranked by signal recency within the tier
- FIT accounts second
- PARTIAL FIT accounts third — include what's missing and whether it's worth a manual check
- DISQUALIFIED accounts at the bottom — one-line reason each
5. Return a summary:
- Total accounts scored
- STRONG FIT / FIT / PARTIAL FIT / DISQUALIFIED counts
- Top 5 accounts to work first and why
- Any pattern in the disqualified accounts worth feeding back to whoever built the list
</task>
<constraints>
- Score only against the ICP criteria provided. Don't add criteria the rep didn't specify.
- DISQUALIFIED is a clean output — don't soften it to PARTIAL FIT to avoid wasting the list. A disqualified account worked by a rep is wasted time.
- If Lusha can't find firmographic data for an account, flag it as UNVERIFIED — don't score it.
- The top 5 recommendation must explain why each account ranks where it does — not just repeat the score.
</constraints>Prompt 2: Build a verified ABM target list from a campaign brief
Most ABM lists in SaaS are built manually: someone pulls a filter in LinkedIn, exports a CSV, cleans it in a spreadsheet, and hands it to marketing with stale data and no signal context. By the time the campaign launches, half the signals have moved.
This prompt takes a campaign brief — ICP, persona, region, industry, exclusion list — runs it through Lusha to find matching companies, layers buying signals per account (funding, exec hires, headcount growth), validates the right contact at each one, and returns a three-tier list ready for paid, outbound, or personalized outreach. TIER 1 requires both a signal and a verified decision-maker. It also posts the campaign summary to Slack before the next stand-up.
<context>
I have a campaign brief and I need a verified ABM target list — the right accounts and the right contacts at each one, enriched with firmographic data and layered with buying signals. The list needs to be campaign-ready: accounts tiered by signal strength, contacts validated, ready for paid, outbound, or personalized outreach.
My campaign brief:
- Campaign name or goal: [e.g. "Q3 pipeline push — mid-market SaaS RevOps"]
- Target industry: [e.g. "B2B SaaS" / "Fintech" / "Multiple — list them"]
- Target company size: [e.g. "200–1,000 employees"]
- Target geography: [e.g. "US and Canada" / "EMEA" / "Global"]
- Target persona: [e.g. "VP of Revenue Operations, Head of Sales Operations, Director of RevOps"]
- Seniority floor: [e.g. "Director and above" / "VP and above"]
- Number of accounts to build: [e.g. "50" / "100" / "as many as possible"]
- Named accounts to include: [PASTE LIST OR "none"]
- Accounts to exclude: [e.g. "existing customers, current pipeline"]
- Slack channel for output: [CHANNEL NAME OR "return as list only"]
</context>
<task>
1. Use Lusha to find companies matching the campaign brief criteria:
- Industry, headcount range, and geography as specified
- Exclude any accounts listed in the exclusion field
2. For each company found, check Lusha's signals layer for buying activity in the last 30 days:
- Funding event (Series A or later)
- New exec hire in the target function
- Headcount growth 15%+ in the target function
- M&A as acquirer
Assign a signal score: HIGH (2+ signals), MEDIUM (1 signal), NONE (no signal)
3. For each account, find the right contact matching the target persona:
- Most senior verified contact in the target function
- Secondary contact if available (evaluator or champion level)
- Verified work email and direct mobile for each
4. Build the ABM target list with three tiers:
- TIER 1: signal score HIGH + primary contact verified — prioritise for personal outreach and paid
- TIER 2: signal score MEDIUM or NONE + primary contact verified — include in campaign sequences
- TIER 3: account matches ICP but contact unverified or below seniority floor — research before activating
5. Return:
- Full tiered account list with verified contacts, firmographics, signals, and recommended channel per tier
- Summary: X Tier 1, X Tier 2, X Tier 3
- Top 10 accounts to activate first — named accounts with named contacts
- Any named accounts from the brief with no verified contact found
6. If Slack channel specified: post campaign summary — total accounts, tier breakdown, top 10, list attached.
</task>
<constraints>
- Only Lusha-verified contacts. No format guesses, no unverified emails.
- TIER 1 requires both a signal AND a verified decision-maker. Don't inflate the tier.
- Exclusion list applied before any output — don't include existing customers or current pipeline.
- Top 10 must be named accounts with named contacts.
</constraints>Prompt 3: Find every inbound lead that came from a target account
In SaaS, a junior analyst filling in a pricing form from a Tier 1 ABM account isn’t a standard MQL. It’s evidence that someone at that company assigned them to research the product. Routing that lead to a standard SDR sequence wastes the signal and potentially surfaces the wrong person before the AE can engage the decision-maker.
This prompt checks every inbound form fill against the ABM target account list via Lusha, identifies the account owner, checks Gmail for any active deal at the account, surfaces the most senior verified contact in the buying function, and posts an ABM inbound alert to Slack — before the lead gets routed anywhere. The recommended action is specific: not “follow up” but exactly what to do, who does it, and what to say.
<context>
Someone just filled in a form. Before I route it as a standard inbound lead, I want to check whether the company is on our ABM target account list. A junior contact from a Tier 1 account is not a standard MQL — it's a buying signal that the account owner needs to know about immediately.
My check:
- Inbound lead: [PASTE NAME, EMAIL, COMPANY, TITLE, FORM TYPE — or "check Gmail for today's inbound"]
- ABM target account list: [PASTE COMPANY NAMES OR DOMAINS — or "use the list in [Drive folder / Slack channel]"]
- What we sell: [PRODUCT / SOLUTION]
- Account owner mapping: [PASTE REP NAME PER ACCOUNT — or "auto-assign by territory"]
- Slack channel for alerts: [CHANNEL NAME OR "skip"]
</context>
<task>
1. For each inbound lead, use Lusha to validate and enrich:
- Verified current title and seniority level
- Still at the company?
- Company: headcount, industry, funding stage
- Is this company on the ABM target account list? Match by domain or company name.
2. If the company IS on the ABM target list:
- Flag immediately as ABM INBOUND — regardless of the lead's seniority
- Identify the assigned account owner for this account
- Check Gmail for any prior contact between the sales team and this company
- Check whether there is an active deal at this account
- Find the most senior verified contact in the buying function via Lusha
3. If the company is NOT on the ABM target list:
- Route as standard inbound MQL — apply normal scoring and routing rules
4. For every ABM INBOUND lead, build an account signal brief:
- Who submitted the form: name, verified title, form type
- Company: ABM tier, account owner, active deal status
- Most senior contact in the buying function (may differ from who submitted)
- Recommended action: engage the form submitter, route to account owner,
or use as a signal to accelerate existing outreach
- One suggested message for the account owner referencing the inbound activity
5. If Slack channel specified: post ABM inbound alert immediately — tag the account owner.
6. Return:
- ABM INBOUND leads with full account signal brief
- Standard MQL leads — routed normally
- Summary: X ABM inbound signals, X standard MQLs
</task>
<constraints>
- ABM INBOUND flagged regardless of the lead's seniority.
- Account owner must be notified before any outreach.
- Active deal at the account: flag to deal owner, don't route as a new lead.
- Recommended action must be specific — not "follow up" but exactly what to do and who does it.
</constraints>The pattern across all three prompts
Every prompt in this article enforces the same principle: accounts get worked in order of verified fit and live signal, not whoever happened to fill in a form or land at the top of a CSV. DISQUALIFIED is a clean output — not a soft PARTIAL FIT that wastes rep time. UNVERIFIED means no scoring. TIER 1 requires both a signal and a verified contact, not just firmographic fit. And recommended actions are always specific: who does what, when, and why.
The firmographic data and signal layer that makes this possible comes from Lusha’s 300M+ verified contacts, connected directly to Claude. No manual list cleaning, no spreadsheet hand-offs, no waiting for RevOps to run a report.
Where these prompts live
All three prompts are available in the account targeting section of Lusha Plays.
