How RevOps Leaders Address the Visibility Gap After Proposals Are Sent

·Jun 24, 2026 03:35 PM·

One thing I've been wondering about: We spend a lot of time instrumenting what happens before a proposal is sent — lead scoring, intent data, opportunity stages, CRM activity, forecasting. But once a proposal lands with the buyer, visibility drops off dramatically. When deals stall after a proposal goes out, how often do you actually know why? Do you usually have enough evidence in the CRM to understand what happened, or is the explanation mostly reconstructed later from rep memory and deal reviews? Curious how RevOps leaders think about this gap, and whether it's a tooling problem, a process problem, or simply something that's impossible to observe reliably.

18 comments

· Sorted by Oldest

Janet B.
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I worked with an organization where we addressed this by implementing data capture in Salesforce at the rep level when they closed a deal as well as having automation to change the lifecycle stage after a period of no activity. If the rep closed the deal, there were drop down fields they had to complete with the most common reasons why a deal was closed. If the lifecycle changed due to inactivity, we were able to grab those through reporting and go back to the rep. We were not able to solve for the issue if a deal stalls or a rep stops opening the record it is very difficult to get them to update the fields for reporting purposes. The automation at least helped get some visibility and put accounts and contacts back into a nurture queue. Some of this is a process issue that needs to be enforced by sales leadership.
Pat H.
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Brice, this topic doesn't seem to be getting traction
Brice E.
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Janet, that's exactly the pattern I was curious about the automation helping at the extremes (closed won, gone cold) but the middle state staying blind. The rep behavior problem at the stall point is the one that seems hardest to solve with process alone, because asking someone to update a record for a deal they've mentally moved on from is fighting human nature. Useful to hear that even a well-instrumented Salesforce org hits that wall.
Janet B.
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Maybe this topic isn't getting much traction because most of us don't want to talk about the human challenges we can't solve through AI and/or automation 😁 We can use the tools to surface when these things happen, but we can't "fix" human nature.
🤣1
santiago362
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What helped us most was to stop relying on the rep to explain the stall and read it from the buyer side instead. Proposal-stage signals you can capture without manual entry: did the doc get reopened, did new stakeholders get looped in, did replies slow down or go one-word. That tells you "stalled because it went quiet" vs "stalled because procurement showed up" without asking the rep to log anything. Two things made it stick for us: only instrument the 2-3 stall reasons that actually move the forecast, and put the nudge in the rep's flow (a Slack prompt when a deal crosses a stage-age threshold) instead of a CRM field they fill in after they have checked out. Tooling won't fully close the middle-state gap, but you stop depending on memory for the deals that matter.
Janet B.
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santiago362 That is an interesting approach and seems to help. How did you operationalize that?
Akshayata S.
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Well I solved it by implementing pipe hygiene. as a subset of forecasting. Every deal clearly visible with mandatory notes from rep in sfdc/ clari - depending on where it lived. Automated regular monitoring, macro reporting - eg team A tends to have 5X coverage but doesnt make target etc. Basically lots of monitoring. This isnt a simple one tool or one behavior problem. Its an interconnected set of variables - all of which need to be monitored with discipline.
Brice E.
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Akshayata, the interconnected variables point is important and I think you're right that there's no single tool or behavior fix here. The discipline layer you're describing — mandatory notes, automated monitoring, macro coverage reporting — is the foundation that makes everything else legible. The gap I keep coming back to is that even a well-disciplined system like what you're describing is still instrumented from the seller side. The rep notes, the stage updates, the coverage ratios — they tell you what the rep observed and logged, which is genuinely valuable but still filtered through rep memory and incentive. What stays blind is what's happening on the buyer side during the evaluation, which by definition nobody logged because nobody was there. Curious whether in your monitoring setup you've ever had a deal that looked healthy on all the seller-side metrics — good coverage, notes updated, stage progression — but died anyway in a way that only made sense in hindsight? That's the scenario I'm most interested in understanding better.
santiago362
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Janet, two moving parts, and I kept both deliberately low-lift so reps didn't get one more field to fill. The signals come from activity that already exists: doc reopens, a new name getting cc'd, reply latency stretching or going one-word. Most of that is already in the email and CRM sync, so it's a saved view and a couple of flags, not data entry. The second part is the nudge. When a deal crosses a stage-age threshold the rep gets a Slack ping in their own flow with one question, still live or did it go quiet, and that answer is what gets logged. Akshayata's point on discipline is the real unlock: the monitoring only holds if the rep's easiest path is also the one that captures the signal.
Akshayata S.
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Brice E. yes, partially. we hired an external agency to interview prospects in-flight, anonymously, and not just focused on our suite of products, more around understanding what they were trying to solve. plus interview closed lost deals. combining these gave us a good enough view - although not always in time. but it was better than being completely blind. i cant remember the name of the agency from many years ago, but am sure there are many of these.
Akshayata S.
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(edited)·
Brice E. i also wanted to highlight something. Having set up and run global revops teams, I discovered that if your product suite is good enough and stable enough, then even insights from hindsight matter. Teams kinda overrate the challenges of a particular deal, especially buying behavior across different geographies - it feels very complex and to some extent it is. However, do a cohort analysis of your support tickets or those interviews I mentioned, or POC feedback or closed lost deals rep feedback, they tend to fall in a few major buckets. Once we know the buckets, we can address the need of the cohort - for example i had some key things from our reserahc included in the sales onboarding and it helped. I am not a fan of understanding in advance every single buying decision factor for every single deal. That is too complex. Even in the age of AI. However knowing the top 5 factors that become bottlenecks or lead to closed won - building a repeatable sales architecture around that helps. Much as we like to think, the deal happened only as a very special complex unique case, isnt very useful.
Brice E.
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·
Akshayata, the anonymous in-flight interviews are a method I hadn't considered, and combining them with closed-lost interviews to uncover what CRM data misses makes a lot of sense. The cohort perspective is especially interesting. It seems more durable to learn from recurring patterns than to over-analyze every individual deal. Something you said also connects to a point another advisor raised with me. They argued that the bigger opportunity might not be understanding one proposal deeply, but identifying patterns across many proposals—like which pages consistently slow deals down, which sections correlate with closed-won outcomes, or where the same friction keeps appearing across different reps and buyers. That cross-proposal view doesn't really exist in most tools today. I'm curious whether you've seen those kinds of cohort insights actually change how reps handle deals in the moment, or whether they mostly improve sales processes, coaching, and onboarding. And do you think there's real value in surfacing those cross-proposal patterns alongside individual deal signals, or does the durable insight almost always live at the cohort level?
Jacomini
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(edited)·
In our operation, before the proposal goes out I have solid data — lead source, order volume, marketplaces, current tool, declared pain, objections raised during the demo. That side of the funnel is reasonably instrumented. Once the proposal is sent, visibility drops significantly. When a deal stalls or goes dark, the explanation is almost always reconstructed from the closer's memory, not from actual data. I do track loss reasons with detailed categories, and that part works. But the window between sending the proposal and the final decision is still a real blind spot. My read is that it's a process problem before it's a tooling problem. The tool won't fix it if the closer doesn't have the habit of logging each post-proposal interaction in real time. I'm addressing this now by implementing a more structured CRM and AI-powered meeting transcription — the goal is to have that data flowing without depending on each closer's individual discipline. My hypothesis is that most operations accept this blind spot as a given rather than treating it as a problem worth solving. What's been working for you?
Brice E.
·
·
Jacomini, the process-before-tooling diagnosis is the honest one and I think you're right. If the closer doesn't log, no tool fixes that — which is exactly why the AI transcription approach makes sense, it removes the dependency rather than fighting it. The hypothesis at the end is the one I keep coming back to: most operations have normalized the blind spot. From what I'm hearing across these conversations, that seems accurate — but I'm curious whether it's because the pain isn't sharp enough to prioritize, or because people genuinely don't believe it's observable without rep input. The thing I've been exploring is whether there's a layer of post-proposal signal that doesn't require the closer to log anything at all — observable directly from buyer behavior on the document side. Not replacing the CRM or the transcription layer, but filling the gap in between calls when nothing is being logged anywhere. The question I'm sitting with: if you could see, without any rep input, exactly which part of your proposal buyers consistently disengage from across multiple deals — would that change how you build the proposal, or would it mostly just confirm what your closers already suspected?
Jacomini
·
·
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?

Janet B.
·
·
I worked with an organization where we addressed this by implementing data capture in Salesforce at the rep level when they closed a deal as well as having automation to change the lifecycle stage after a period of no activity. If the rep closed the deal, there were drop down fields they had to complete with the most common reasons why a deal was closed. If the lifecycle changed due to inactivity, we were able to grab those through reporting and go back to the rep. We were not able to solve for the issue if a deal stalls or a rep stops opening the record it is very difficult to get them to update the fields for reporting purposes. The automation at least helped get some visibility and put accounts and contacts back into a nurture queue. Some of this is a process issue that needs to be enforced by sales leadership.
Pat H.
·
·
Brice, this topic doesn't seem to be getting traction
Brice E.
·
·
Janet, that's exactly the pattern I was curious about the automation helping at the extremes (closed won, gone cold) but the middle state staying blind. The rep behavior problem at the stall point is the one that seems hardest to solve with process alone, because asking someone to update a record for a deal they've mentally moved on from is fighting human nature. Useful to hear that even a well-instrumented Salesforce org hits that wall.
Janet B.
·
·
Maybe this topic isn't getting much traction because most of us don't want to talk about the human challenges we can't solve through AI and/or automation 😁 We can use the tools to surface when these things happen, but we can't "fix" human nature.
🤣1
santiago362
·
·
What helped us most was to stop relying on the rep to explain the stall and read it from the buyer side instead. Proposal-stage signals you can capture without manual entry: did the doc get reopened, did new stakeholders get looped in, did replies slow down or go one-word. That tells you "stalled because it went quiet" vs "stalled because procurement showed up" without asking the rep to log anything. Two things made it stick for us: only instrument the 2-3 stall reasons that actually move the forecast, and put the nudge in the rep's flow (a Slack prompt when a deal crosses a stage-age threshold) instead of a CRM field they fill in after they have checked out. Tooling won't fully close the middle-state gap, but you stop depending on memory for the deals that matter.
Janet B.
·
·
santiago362 That is an interesting approach and seems to help. How did you operationalize that?
Akshayata S.
·
·
Well I solved it by implementing pipe hygiene. as a subset of forecasting. Every deal clearly visible with mandatory notes from rep in sfdc/ clari - depending on where it lived. Automated regular monitoring, macro reporting - eg team A tends to have 5X coverage but doesnt make target etc. Basically lots of monitoring. This isnt a simple one tool or one behavior problem. Its an interconnected set of variables - all of which need to be monitored with discipline.
Brice E.
·
·
Akshayata, the interconnected variables point is important and I think you're right that there's no single tool or behavior fix here. The discipline layer you're describing — mandatory notes, automated monitoring, macro coverage reporting — is the foundation that makes everything else legible. The gap I keep coming back to is that even a well-disciplined system like what you're describing is still instrumented from the seller side. The rep notes, the stage updates, the coverage ratios — they tell you what the rep observed and logged, which is genuinely valuable but still filtered through rep memory and incentive. What stays blind is what's happening on the buyer side during the evaluation, which by definition nobody logged because nobody was there. Curious whether in your monitoring setup you've ever had a deal that looked healthy on all the seller-side metrics — good coverage, notes updated, stage progression — but died anyway in a way that only made sense in hindsight? That's the scenario I'm most interested in understanding better.
santiago362
·
·
Janet, two moving parts, and I kept both deliberately low-lift so reps didn't get one more field to fill. The signals come from activity that already exists: doc reopens, a new name getting cc'd, reply latency stretching or going one-word. Most of that is already in the email and CRM sync, so it's a saved view and a couple of flags, not data entry. The second part is the nudge. When a deal crosses a stage-age threshold the rep gets a Slack ping in their own flow with one question, still live or did it go quiet, and that answer is what gets logged. Akshayata's point on discipline is the real unlock: the monitoring only holds if the rep's easiest path is also the one that captures the signal.
Akshayata S.
·
·
Brice E. yes, partially. we hired an external agency to interview prospects in-flight, anonymously, and not just focused on our suite of products, more around understanding what they were trying to solve. plus interview closed lost deals. combining these gave us a good enough view - although not always in time. but it was better than being completely blind. i cant remember the name of the agency from many years ago, but am sure there are many of these.
Akshayata S.
·
(edited)·
Brice E. i also wanted to highlight something. Having set up and run global revops teams, I discovered that if your product suite is good enough and stable enough, then even insights from hindsight matter. Teams kinda overrate the challenges of a particular deal, especially buying behavior across different geographies - it feels very complex and to some extent it is. However, do a cohort analysis of your support tickets or those interviews I mentioned, or POC feedback or closed lost deals rep feedback, they tend to fall in a few major buckets. Once we know the buckets, we can address the need of the cohort - for example i had some key things from our reserahc included in the sales onboarding and it helped. I am not a fan of understanding in advance every single buying decision factor for every single deal. That is too complex. Even in the age of AI. However knowing the top 5 factors that become bottlenecks or lead to closed won - building a repeatable sales architecture around that helps. Much as we like to think, the deal happened only as a very special complex unique case, isnt very useful.
Brice E.
·
·
Akshayata, the anonymous in-flight interviews are a method I hadn't considered, and combining them with closed-lost interviews to uncover what CRM data misses makes a lot of sense. The cohort perspective is especially interesting. It seems more durable to learn from recurring patterns than to over-analyze every individual deal. Something you said also connects to a point another advisor raised with me. They argued that the bigger opportunity might not be understanding one proposal deeply, but identifying patterns across many proposals—like which pages consistently slow deals down, which sections correlate with closed-won outcomes, or where the same friction keeps appearing across different reps and buyers. That cross-proposal view doesn't really exist in most tools today. I'm curious whether you've seen those kinds of cohort insights actually change how reps handle deals in the moment, or whether they mostly improve sales processes, coaching, and onboarding. And do you think there's real value in surfacing those cross-proposal patterns alongside individual deal signals, or does the durable insight almost always live at the cohort level?
Jacomini
·
(edited)·
In our operation, before the proposal goes out I have solid data — lead source, order volume, marketplaces, current tool, declared pain, objections raised during the demo. That side of the funnel is reasonably instrumented. Once the proposal is sent, visibility drops significantly. When a deal stalls or goes dark, the explanation is almost always reconstructed from the closer's memory, not from actual data. I do track loss reasons with detailed categories, and that part works. But the window between sending the proposal and the final decision is still a real blind spot. My read is that it's a process problem before it's a tooling problem. The tool won't fix it if the closer doesn't have the habit of logging each post-proposal interaction in real time. I'm addressing this now by implementing a more structured CRM and AI-powered meeting transcription — the goal is to have that data flowing without depending on each closer's individual discipline. My hypothesis is that most operations accept this blind spot as a given rather than treating it as a problem worth solving. What's been working for you?
Brice E.
·
·
Jacomini, the process-before-tooling diagnosis is the honest one and I think you're right. If the closer doesn't log, no tool fixes that — which is exactly why the AI transcription approach makes sense, it removes the dependency rather than fighting it. The hypothesis at the end is the one I keep coming back to: most operations have normalized the blind spot. From what I'm hearing across these conversations, that seems accurate — but I'm curious whether it's because the pain isn't sharp enough to prioritize, or because people genuinely don't believe it's observable without rep input. The thing I've been exploring is whether there's a layer of post-proposal signal that doesn't require the closer to log anything at all — observable directly from buyer behavior on the document side. Not replacing the CRM or the transcription layer, but filling the gap in between calls when nothing is being logged anywhere. The question I'm sitting with: if you could see, without any rep input, exactly which part of your proposal buyers consistently disengage from across multiple deals — would that change how you build the proposal, or would it mostly just confirm what your closers already suspected?
Jacomini
·
·
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?

How RevOps Leaders Address the Visibility Gap After Proposals Are Sent | RevGenius