That's exactly the right question — and it points to a layer that most operations haven't instrumented yet.
What you're describing has a name in the tooling market: proposal intelligence. The gap between sending a proposal and getting a response is no longer unobservable — it's just that most teams are still sending static PDFs and getting nothing back.
Tools like Qwilr track buyer behavior at the block level inside web-based proposals — which sections they read, how long they spent on pricing versus scope, whether the proposal was forwarded internally, and real-time notifications when engagement stalls. DocBeacon takes a similar approach focused on analytics depth, including heatmaps and an AI-generated engagement score that ranks deals by real buyer interest so you can prioritize follow-ups without rep input.
HYM
Gluo CRM
The move from static PDF to a trackable web-based proposal link is what closes the blind spot you're describing — without requiring the rep to log anything.
In our case, we're not there yet. But the architecture I'm building — structured CRM, AI transcription, SDR automation — creates the conditions where adding a proposal intelligence layer actually makes sense. Without clean upstream data, even the best post-proposal signal is hard to act on.
To answer your original question: I think it's solvable, it just requires treating the proposal as an instrumented touchpoint rather than a document you send and wait on. The tooling exists. The process change is harder.
What signals have you found most predictive — time on pricing section, repeat opens, or something else entirely?