Turn hiring signals into account research briefs

Create an Airtable base for the research sprint with tables for target accounts, hiring evidence, signal scoring, account briefs, outreach angles, CRM status, and false positives. Define which departments, job titles, seniority levels, locations, technology keywords, and repeated phrases would suggest a real business priority for your product. Add disqualifiers such as generic sales hiring, low-level backfills, unrelated geographies, agency roles, or roles that do not connect to your problem area. Create scoring fields for ICP fit, signal strength, urgency, buyer accessibility, CRM conflict, and confidence. QA the model by testing it against five accounts you know well and checking whether the signals would have produced a sensible research brief.

Create a hiring-signal model for this sales motion.

Product or service:
{{product_or_service}}

ICP summary:
{{icp_summary}}

Problems we solve:
{{problems_we_solve}}

Target departments:
{{target_departments}}

Output:
1. Job titles that may indicate buying intent
2. Departments and seniority levels to watch
3. Keywords and phrases to capture
4. Strong signal examples
5. Weak signal examples
6. Disqualifiers
7. Suggested scoring fields
8. Confidence rules
9. Examples of non-creepy outreach framing

Use LinkedIn Sales Navigator to create the starting account universe before researching individual contacts. Apply ICP filters such as geography, industry, company headcount, company growth, technologies if available, and target account status. Save the account search with a clear naming convention that includes date, segment, territory, and owner. Do not start with individual leads unless your motion is heavily contact-led; hiring intent usually belongs to the company first. Review the first 30 accounts manually and remove obvious mismatches before exporting or staging the list.