Find accounts showing buyer intent in ChatGPT

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

@Lusha Find accounts showing buyer intent around topics
related to my product.

INTENT TOPICS:
Topics to search for:
- [intent topic 1]
- [intent topic 2]
- [intent topic 3]

TARGET MARKET:
Industries: [industries]
Company size: [employee range]
Region: [country or region]
Target personas: [titles or personas]
Relevant departments: [Sales / Marketing / RevOps / IT /
Operations / Customer Success / Finance / HR / other]
Disqualifiers: [companies, industries, regions, sizes,
competitors, existing customers, or poor-fit segments
to exclude]

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

Using Lusha, do the following:

1. FIND INTENT-MATCHED ACCOUNTS
   Search for companies that match the target market and
   show buyer intent around the topics I provided.

   Only include companies where Lusha can verify the
   company profile and relevant intent topic.

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

3. RETURN THE INTENT CONTEXT
   For each account, return:
   - Intent topic matched
   - Intent relevance to my product
   - Whether the intent appears strong, medium, or weak
   - Any available timing or recency context

   Keep the interpretation grounded. Do not assume the
   company is ready to buy unless Lusha returns signals
   that support stronger urgency.

4. CHECK SUPPORTING SIGNALS
   Check whether the same company also has recent 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
   - Promotion or company-change signals for relevant contacts

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

   Base the score on industry, company size, region,
   disqualifiers, intent relevance, and fit with my product.

6. PRIORITIZE THE LIST
   Rank the companies:

   Tier 1:
   Strong ICP fit + relevant buyer intent +
   supporting signal or clear persona relevance

   Tier 2:
   Strong ICP fit + relevant buyer intent,
   but weak or no supporting signal

   Tier 3:
   Medium fit, weak intent match, or unclear urgency

   Exclude:
   Poor fit, disqualified, unmatched, competitor,
   existing customer, or unclear intent relevance

7. FIND STARTING CONTACTS
   For each Tier 1 account, find 1-2 relevant contacts
   matching the target persona or department most likely
   connected to the intent topic.

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

8. CREATE THE OUTREACH ANGLE
   For each Tier 1 account, write:
   - One subject line under 7 words
   - One opening line under 30 words
   - One discovery question tied to the intent topic

   Do not:
   Say "I saw you are researching..." or make the
   message sound like surveillance.
   Overstate what the intent signal proves.
   Invent projects, tools, vendors, priorities, or
   internal initiatives that Lusha did not return.
   Force intent into the message if it feels unnatural.

9. OUTPUT FORMAT
   Return:
   - Account table
   - Intent topic matched
   - Supporting signals, if any
   - ICP fit score
   - Priority tier
   - Recommended contacts
   - Outreach angle
   - Discovery question
   - Excluded accounts and why

Do not invent companies, contacts, emails, phone numbers,
intent topics, or signals. If Lusha cannot verify an intent
match, do not include the account.