stg-segmenting-customers

To segment customers, you enumerate three to five candidate segments using observable filters like industry, size, geography, and tools, then score their pain intensity with tiered evidence to identify defensible target groups.

A compression-based elimination workflow systematically narrows down candidate customer segments by documenting a defendable elimination log, retaining only those with the strongest grounded pain signals and observable evidence.

You document a segment hypothesis in a register format that explicitly records underlying assumptions, predefined kill conditions for early termination, and viable alternative segments to pivot toward if testing fails.

No, testing effectively requires explicit observable filters such as industry, size, tech usage, and geography to define candidate segments and ensure the resulting hypothesis is structurally sound and testable.

The best way to score pain intensity is to assign tiered pain scores using public signals and evidence, attaching tier labels to quantify the urgency and validate the segment's pain.

You need a register-format hypothesis during BUILD-phase tasks when you must defend customer segmentation choices with structured evidence, documenting assumptions, kill conditions, and alternatives for rigorous hypothesis testing.