AI for Prospect Research

The research problem in sales

Good sales conversations require context: who you're talking to, what their company is doing, what pressures they're under, and what they care about. Gathering that context manually takes 30-45 minutes per prospect and often still misses things. AI compresses that to 5 minutes and often surfaces angles you would not have found.

The pre-call research prompt

Before any significant sales call, run this: "I'm meeting [name], [title] at [company] in [X] days. Here's what I know about them: [paste LinkedIn bio or brief description]. My product does [X]. Give me: (1) what they likely care about most in their role, (2) 3 intelligent questions I can ask that show I understand their business, (3) the business pressures their company is probably under right now, (4) any likely objections to [your product category]."

Use Perplexity for the company context first — recent news, earnings calls if public, any press releases. Paste that into ChatGPT along with the LinkedIn profile. The combination of recent news plus role context produces prep that used to take an hour.

Researching the account, not just the contact

One person rarely buys. Ask ChatGPT: "For a [company size] company in [industry] buying [your product category], who are typically the stakeholders involved in the decision? What does each one care about most?" Then research each stakeholder separately. Walking into a deal knowing what the CFO, the IT lead, and the end user each want — before they're in the room — is a significant edge.

Monitoring accounts for trigger events

Use Perplexity to set up ongoing searches for key accounts. Leadership changes, funding rounds, new product launches, regulatory changes in their industry — these are trigger events that create buying opportunities. A company that just raised a Series B is actively spending. A company that just appointed a new CTO is reassessing its tech stack. Know before you call.