Prioritize a target account list in ChatGPT

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 account list and ICP details to see live results.

Most account lists look useful until someone has to work them. Some companies are too small. Some are outside your ICP. Some are good-fit accounts with no urgent reason to act. Others have strong buying signals hiding in plain sight.

The problem is not having a list. The problem is knowing where to start.

This prompt uses Lusha in ChatGPT to match and enrich each company, check for recent buying signals, score ICP fit, and recommend the next action. Instead of handing sales a flat list of accounts, you get a prioritized view of which accounts to contact now, which ones to research further, and which ones to hold.

How to start

1

Open Lusha in ChatGPT

Go to Lusha in ChatGPT and click “Start chat.” Every conversation started this way is automatically Lusha-enabled.

2

Or invoke Lusha in any existing conversation

Type @Lusha in the prompt bar and select Lusha from the dropdown. Unlike Claude, Lusha does not activate automatically in every ChatGPT conversation. You must invoke it every time.

3

Paste your account list and ICP

Copy the prompt below, paste your company names or domains, add your ICP criteria, and send. Lusha enriches the accounts, checks for recent signals, and ranks the list by priority.

The prompt

Start from Lusha in ChatGPT or type @Lusha before sending.

@Lusha Prioritize this target account list.

ACCOUNT LIST:
Paste company names or domains:
1. [company name or domain]
2. [company name or domain]
3. [company name or domain]

ICP:
Best-fit industries: [industries]
Best-fit company size: [employee range]
Best-fit regions: [regions]
Target personas: [personas]
Disqualifiers: [companies, industries, regions, or
segments to exclude]

MY PRODUCT:
[One sentence describing what you sell and the
problem it solves]

Using Lusha, do the following:

1. MATCH AND CLEAN THE ACCOUNT LIST
   Match each company to the correct Lusha company profile.
   Return the company name, domain, and match status.

   If a company cannot be matched, flag it clearly
   instead of guessing.

2. ENRICH EACH ACCOUNT
   For each matched company, return:
   - Company name
   - Domain
   - Industry
   - Employee count
   - HQ location
   - Revenue range if available
   - Company LinkedIn if available

3. CHECK RECENT BUYING SIGNALS
   Check for recent company signals from the last 6 months.
   Prioritize:
   - Hiring surges
   - Hiring surges by relevant department
   - Hiring surges by location
   - Headcount increases or decreases
   - IT spend changes
   - Website traffic changes
   - Commercial activity news
   - Corporate strategy news
   - Financial events news
   - People news
   - Product activity news
   - Risk news

4. SCORE ICP FIT
   Score each account:
   - High fit
   - Medium fit
   - Low fit
   - Exclude

   Base the score on industry, company size, region,
   disqualifiers, and relevance to my product.

5. PRIORITIZE THE LIST
   Assign one priority tier:

   Tier 1:
   Strong ICP fit + strong recent signal +
   clear reason to act now

   Tier 2:
   Strong ICP fit, but weak or no recent signal

   Tier 3:
   Partial ICP fit or unclear urgency

   Exclude:
   Poor fit, disqualified, unmatched, or not relevant

6. RECOMMEND NEXT ACTION
   For each Tier 1 account, recommend one next action:
   - Contact now
   - Research buying group
   - Add to campaign
   - Find decision-maker
   - Hold for later

   Explain the reason in one sentence.

7. FIND STARTING CONTACTS
   For each Tier 1 account, find one relevant contact
   matching the target persona.

   Return:
   - Name
   - Title
   - Department
   - Seniority
   - LinkedIn profile if available
   - Verified business email availability
   - Direct or mobile phone availability
   - DNC status if available

8. OUTPUT FORMAT
   Return:
   - Account prioritization table
   - Strongest signal per account
   - ICP fit score
   - Priority tier
   - Recommended next action
   - Starting contact for Tier 1 accounts
   - One-sentence reason for each recommendation

Do not invent missing company data, contacts, or signals.
If Lusha cannot match an account, mark it as unmatched.
Built by: Lusha
Time to build: 1 min
Difficulty: Easy
Tools: ChatGPT, Lusha
Type: Template

What you’ll get back

 

A cleaned, enriched, and prioritized account list with buying signals and next actions. Here’s what the output looks like:

Target account prioritization — Lusha

FieldValue
Accounts reviewed25 submitted · 23 matched · 2 unmatched
Tier 1 accounts6 accounts · strong ICP fit + recent buying signal
Strongest signalHiring surge in Sales · 68% above historical average
Recommended actionContact now · signal is recent and tied to the target persona
Starting contactR.M. · VP Sales · verified email available · mobile available
OutputPrioritized table · ICP fit · signal · next action · starting contact

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 account list and ICP details to see live results.

 

Why use Lusha in ChatGPT to prioritize target accounts

 

A target account list is only useful if your team knows where to start. Without enrichment and signals, every account looks the same. Reps either work alphabetically, follow gut feeling, or chase logos that may not be active, relevant, or ready.

Lusha adds the context that turns a flat list into a ranked one. The prompt checks whether each company matches your ICP, enriches the account with verified company data, and looks for recent signals that suggest timing. A high-fit account with a hiring surge in your target department should not be treated the same as a high-fit account with no recent movement.

The contact step makes the list actionable. Prioritization alone is not enough if the rep still needs to figure out who to contact. Lusha helps identify the right starting contact for Tier 1 accounts, so the output is not just a scored list. It is a next-action plan.

Lusha data is sourced and used in accordance with Lusha’s Privacy Policy and Terms of Use. Lusha is GDPR compliant and covers contacts across North America, EMEA, and APAC.

FAQ

  • How many accounts should I paste into the prompt?

    Start with a small or medium-sized list, especially if you want ChatGPT to return detailed enrichment, signals, and starting contacts for each account. For larger lists, break the account list into smaller batches so the output stays easy to review.

  • Can I use company names instead of domains?

    Yes. You can paste company names or domains. Domains usually make matching more precise, but the prompt asks Lusha to match each company to the correct profile and flag any account that cannot be verified.

  • What if an account has no recent buying signals?

    A strong-fit account can still be worth working without a recent signal, but it should usually be treated differently from an account with clear urgency. The prompt separates strong-fit accounts with signals from strong-fit accounts that may be better for nurture or later outreach.

  • Can this replace manual account scoring?

    It can help with the first layer of prioritization, especially when your team needs a fast view of fit, signals, and next action. For formal scoring models, RevOps should still align the criteria with your CRM, routing rules, and sales process.

  • Can I use this for ABM campaign planning?

    Yes. This prompt is useful before building an ABM campaign because it helps identify which accounts match your ICP, which ones show recent activity, and which contacts are most relevant for the first touch.

Related Plays

Ready to run this?

One data connection. Works in Claude, ChatGPT, your CRM, or any agent you build.

Get free access Read the Docs