A lead source from public filings

The problem

Every competitor bought leads from the same data vendors, so everyone saw the same signals. I wanted a prospecting angle nobody could buy. For a workspace company, a lease coming due is one of the best reasons to talk.

What I built

A Python pipeline that reads public companies’ XBRL financial filings from SEC EDGAR and scores lease urgency.

The model has four signals:

• Current lease liabilities above 25% of the total: +3
• Year-one payments above 20% of the total: +2
• Years one and two together above 45% of the total: +1
• Right-of-use asset declining year over year: +1

Four or more points is Hot. Two to three is Warm. Zero to one is Monitor.

The pipeline also compares periods. Each company is tagged as persistent Hot, escalated to Hot, new Hot, cooled or dropped. Every result is cross-referenced with the CRM so reps know whether an account is already owned.

Design decisions

• Public filings are free and reliable. No enrichment cost.
• Simple point rules instead of a model. A rep can see exactly why a company is Hot.
• Trajectory matters as much as level. A company escalating to Hot is a better call than one that has sat there for a year.
• CRM cross-reference before handoff, so nobody gets a list of accounts already in play.

Result

368 companies scored. 46 Hot and new-Hot targets, all matched against the CRM. Reps got a specific, numeric reason to reach out about the lease, from an angle competitors couldn’t buy from a data vendor.