Example outputs below were pulled from a live session on October 13, 2026. People are shown as initials, companies are described rather than named, and no emails or phone numbers appear.
Daily account prioritization in Claude: the Lusha connector returns the accounts ranked against your Target Profile, the signal behind each one, and the decision makers inside the top three. You see the credit cost before anything is revealed. Run it before your first call.
Using the Lusha connector, pull today's recommended companies for my account against my active Target Profile. Return the top 10 ranked by lead score, and for each one: company, employee count, ICP fit score, signal score, and the signal types that fired.
For the top 3, pull the buying group filtered to decision makers only, 3 per company. Then run a contact search at each of those 3 companies for people with sales leadership titles (VP Sales, CRO, Head of Sales, Head of Revenue Operations), preview only, no reveals.
Do not reveal any email or phone. Tell me the credit cost of revealing one email and one phone per account before you do anything else.
End with one table: account, decision maker, sales leader to call first, the signal to open with, and a one line opening message.A rep on a Pro plan with one active Target Profile (B2B software, 51 to 1,000 employees, US and UK, sales and RevOps personas) runs the prompt at 08:20. Lusha returns 1,000 ranked companies in the full list and shows the top 10.
| Rank | Account | Fit | Signal | Signals that fired |
|---|---|---|---|---|
| 1 | US fraud prevention software, 712 employees | 1.00 | 0.75 | Hiring surge, IT spend increase, headcount growth 1 to 6 months |
| 2 | US data center operator, 690 employees | 0.93 | 1.00 | Hiring surge, traffic increase, IT spend increase, headcount growth |
| 3 | US ecommerce risk platform, 722 employees | 0.82 | 1.00 | Hiring surge, traffic increase, IT spend increase, headcount growth |
| 4 | US cloud analytics vendor, 823 employees | 0.78 | 0.75 | Hiring surge, traffic increase, IT spend increase |
| 5 | US cloud cost management, 770 employees | 0.77 | 0.88 | Hiring surge, traffic increase, IT spend increase |
For the top three, the decision maker filter returned the executive team: at account 1, M.R., CEO and Co Founder (score 0.85); at account 3, E.G., CEO (0.78) and A.D., CFO (0.78). The sales leader search then found 12 people across the three companies, 1 credit for the preview. The final table:
| Account | Decision maker | Sales leader to call first | Signal to open with | Opening line |
|---|---|---|---|---|
| US fraud prevention software, 712 employees | M.R., CEO and Co Founder | O.O., Chief Revenue Officer, New York | Hiring surge + IT spend increase | “You’re adding headcount and tech budget in the same quarter. That’s usually when the sales team outgrows how it finds and prioritizes accounts. Worth 15 minutes?” |
| US data center operator, 690 employees | Not pulled (second batch) | M.K., Chief Revenue Officer, Los Angeles | Hiring surge + traffic increase | “Your site traffic and your hiring are both up this quarter. More inbound, more reps: how are you deciding who gets worked first?” |
| US ecommerce risk platform, 722 employees | E.G., CEO · A.D., CFO | L.T., Head of Sales, Sydney | Hiring surge + headcount growth | “Saw the sales hiring push. New reps ramp faster when the list is already ranked for them on day one. Happy to show how that looks for a team your size.” |
Two things to read in that table. The decision maker column for account 2 is empty because the test ran that step on accounts 1 and 3 only; the prompt as written fills it. And the Head of Sales at account 3 sits in Sydney while the company is US based, so a rep would skip to the New York or Philadelphia strategic sales lead the same search returned. The play is about reading the output, not trusting it blind. Each reveal is quoted before it happens: 1 credit per email, 5 per phone.
Contact confirmed live via Lusha connector, October 13, 2026. Recommendations cost 1 credit per company returned, Buying Group 1 credit per contact returned. This run used 26 credits before any reveal: 10 for recommendations, 15 for buying groups, 1 for the sales leader preview.
Lusha in Claude does three jobs in one pass that take three screens in a browser: it reads your Target Profile and returns the ranked list with the fit score and signals on every row, it maps the buying group at the accounts you pick, and it holds the reveal until you confirm the credit cost. The persona step is explicit on purpose. Buying Group returns the committee by role; your Target Profile personas narrow the daily list, not the committee, so the follow up search by sales title is what puts the right person at the top. Everything runs on Lusha data that is GDPR and CCPA compliant, with ISO 27001 and 27701 and SOC 2 Type II in place.
No. Without one, Lusha builds a “Lusha generated” list from your account history. A Target Profile adds fit scores and makes the list yours. Create one in ICP Hub or ask Claude to create it through the connector; the list re ranks within minutes.
Buying Group classifies people by role in the committee: decision maker, potential champion, end user. It does not read the personas on your Target Profile. That is why the prompt follows with a contact search by sales title.
Recommendations: 1 credit per company returned, so size 10 is 10 credits. Buying Group: 1 credit per contact returned. Contact search preview: 1 credit per 25 records. Reveals are quoted before they run: 1 credit per email, 5 per phone, as of October 2026.
Yes. Ask Claude to list the available signal types first (free), then rerun the prompt filtered to the one you want.
Yes, through the Lusha connector for ChatGPT with the same tools.
One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.
