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Prompt

Prioritize inbound leads by lookalike fit

Tools: Claude ▪ Lusha

Most inbound queues get worked in the order leads arrive, or by a generic lead score that treats every form fill the same. Neither approach knows which inbound lead actually resembles the contacts who became your best customers. Connect Lusha to Claude and this prompt checks a batch of inbound leads against your proven contact pattern, so the response queue is ordered by fit, not just by arrival time.

The prompt

 

Here are my highest-value customer contacts: [paste names or emails].

Here are today's inbound leads: [paste names or emails from your form
fills].

Using the Lusha connector, score each inbound lead against the pattern
from my best customer contacts - role, seniority, and company
profile. Return a table with lead name, match score, and matched
attributes.

Rank the full inbound batch from highest to lowest fit, and flag the
top five for an immediate response.

What you’ll get back

 

The situation: a demand gen manager pastes 15 best-customer contacts and 18 inbound leads from the morning’s form fills, and asks for a ranked response order.

The output:

Example output

Scored 18 inbound leads against your customer pattern

S.T. — 88% match. Same seniority and function as four of your best customers.

D.M. — 81% match. Matches on role and company size, differs on industry.

Three more in the top five, same structure.

Top 5 for immediate response: S.T. first, highest match. Four more ranked behind, remaining 13 leads ranked below them for standard follow-up.

Example outputs in this play are illustrative — they reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the prompt with your own contact lists to see live results.

Why it works

 

Every other lookalike play in this library is outbound: go find contacts who match a proven pattern. This one applies the same model to a pool of leads that already arrived, which changes what “prioritize” means — instead of ranking by a generic score or first-come order, the queue is ordered by how closely each lead actually resembles who became a customer before. The lead who filled out the form third gets worked first, if they’re the strongest match. Contact and company data is verified across multiple sources and handled under GDPR, CCPA, SOC 2 Type II, and ISO 27701 compliance.

FAQ

  • How is this different from a standard lead score?

    A generic lead score usually weighs firmographic fit and engagement generically. This scores specifically against the pattern from contacts who actually became customers, which can surface a strong-fit lead a generic score would rank lower.

  • How is this different from the outbound lookalike contacts play?

    Same underlying match logic, opposite direction. That play searches the market for new contacts to pursue. This one scores contacts you already have, from inbound, against the same pattern.

  • How many best-customer contacts do I need to build the pattern?

    Same minimum as standard lookalikes: five reference contacts, though a larger, more consistent set produces a sharper pattern to score against.

  • Can I run this automatically on every new inbound lead?

    Yes, via the API — this prompt shows the mechanic conversationally, but the same scoring logic can run automatically as leads come in, feeding a ranked queue instead of a manual batch.

  • Is scoring inbound leads like this compliant?

    The prompt matches business-profile attributes — role, seniority, company data — against contacts who submitted a form themselves. Lusha’s data is professionally sourced and opt-outs are honored automatically.

Related Prompts

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