Cut it four ways before you form a theory: by lead source, by segment or deal size, by rep, and by competitor present or not. One of those cuts almost always explains most of the drop, and the answer changes what you do completely. A drop concentrated in one source is a lead quality problem, in one segment is a positioning or pricing problem, in one or two reps is a coaching problem, and across the board with more competitors named is a market problem. Gong's call data adds a useful nuance here: deals where a competitor comes up early actually close at a higher rate, while competitors appearing late correlate with lower win rates and smaller deals, which usually means the rep discounted to survive a fight they did not see coming.
3 hand-picked resources, 3 link-checked. Pick how you want to dig in.
Why we picked it Call transcript data showing that competitors surfacing early raise your win odds while competitors surfacing late lower them. A precise, testable explanation for a win rate drop.
From Gong Labs by Chris Orlob 7 min read
Open gong.io →Why we picked it For each metric it names the underlying behaviours you can actually coach, which is what turns a dashboard into a management action rather than a monthly lament.
From Gong Labs by Jonathan Costet 12 min read
Open gong.io →Why we picked it The clearest single document on what a revenue data model should look like end to end, from lead through renewal and expansion. It is the vocabulary most modern RevOps teams have quietly standardised on.
From Winning by Design by Winning by Design 46 page PDF
Open winningbydesign.com →How forecasting and gtm metrics reads from a different seat.
