Every signal resolves to one of a handful of data types. These are what you capture; the four modes below are how you find them.

This is the case for a custom engine over a bought tool or brute force. The methods of market research map almost one-to-one onto how intelligence gets sourced, and the breadth is the point. An off-the-shelf tool reads one mode. Your most expensive people, working by hand, cover two or three at enormous cost. A custom engine reads all four, calibrated to your market.

The common error is to assume first-party data only grows existing accounts and third-party data only finds new ones. It does not sort that cleanly. Both feed both.

The integration of your first and third-party signals feeds one intelligence platform that improves both the pursuit of net-new accounts and the expansion of existing ones. Same engine, two sides of the book.

Generic signals are commoditized. Every competitor can track a funding round. Few can read a shift in technology strategy at a named account before the RFP. The library is calibrated to the team it serves, which is the breadth that proves the custom build.

Traditional lead scoring, also called manual or rule-based scoring, is a static point system. Marketing and sales assign fixed point values to predefined actions and characteristics, for example ten points for an email open or twenty for a relevant title. When the accumulated score crosses a threshold, typically sixty to eighty points, the lead becomes an MQL and is handed to sales. The defining trait is that the weights are set by the team's assumptions rather than learned from outcomes, with no real-time adaptation.

What it is not: it scores individual inbound leads for a marketing-to-sales handoff. It does not surface accounts, does not act on real-time buying signals, and does not tell a rep which contact to reach inside an account or why. That is the gap Redline Growth fills.