Find the signals behind your best-fit accounts

The business problem

Industry, company size and funding filters can produce a large list of similar-looking accounts without explaining why your own deals win or lose. Apparent patterns can also reflect data captured after a deal ended rather than information you could have used beforehand.

The intended outcome

Targeting hypotheses grounded in your deal history, with evidence and limits your team can inspect. A held-out review helps distinguish a useful signal from an attractive explanation of the past.

Evaluate decision quality on unseen accounts. Historical correlation alone is not evidence of causation or a forecast.

A concrete example

Illustrative example, using fictional data. A fictional security vendor notices more hiring activity among some closed-won accounts. The analysis keeps that as a hypothesis, checks when the evidence was available and compares it with accounts outside the discovery sample. The team changes its targeting rule only if the pattern survives that review.

The workflow

Supporting evidence

Attention’s customer story describes joining deal history, conversations and enrichment to investigate buyer–AE fit, then making successful findings part of routing. It illustrates the path from a hypothesis to a tested business decision.

Find the guide and implementation

Explore this outcome by team