Signals
The signals you watch
20 numbers worth tracking in GTM Engineering, grouped by system, with precise definitions, formulas, benchmarks, and the way each one gets read wrong. Filter, or pick a system to go deep.
Data quality & coverage
This is the health of the data feeding every play: coverage sets the ceiling on who you can reach, accuracy sets what reps hit on the dial. The mistake is trusting a provider's self-reported accuracy, which tests far below the claim.
View signals →Outbound systems
This is how programmatic outbound performs, and after Apple Mail Privacy I trust reply and meeting rates, never open rate. Reply rate is the tell for whether you are sending signal or spraying: spray gets 1–3%, personalized gets 20–40%.
View signals →Automation & ops
This is the operational health and unit economics of the systems themselves. A silent workflow failure corrupts data and pipeline downstream while every dashboard still looks green, so I watch success rate and cost per outcome first.
View signals →Pipeline impact
These are the revenue outcomes that justify the discipline, and I connect every engineered play back to them. A list of high-intent accounts in a spreadsheet is not sourced pipeline until it is wired to a play that books a meeting.
View signals →Deliverability
Since the Google and Yahoo bulk-sender rules of February 2024 (Microsoft followed in May 2025), inbox placement is a hard technical gate, not a soft best practice. The 0.30% spam cap governs how much you can send at all, so I treat it as the binding volume constraint.
View signals →Nothing matches that filter.