Proposal analytics guide: key metrics every sales team should track

Proposal analytics is the practice of tracking how recipients interact with a proposal that’s sent out, including whether it was opened, how much time the recipient spent on each section, and whether it was signed. It covers view status, section-level engagement, completion data, and cycle time. Sales teams use it to time their follow-ups more strategically and to find where deals are stalling before the prospect moves on. 

Why proposal analytics matter more than open rates alone

Receiving a “viewed” notification will hardly tell you anything about the intent of the viewer. Knowing a prospect spent eight minutes on the pricing page and twelve seconds on the case studies will tell you a lot more. This gap is happening with most sales teams. They follow up on gut instinct timing rather than when the data tells them to. 

The moment between when a proposal is sent and when it’s signed is where most deals go cold. A prospect might open a document, get distracted, and then never return to it. Meanwhile, the rep has no idea or signal until it’s too late. Proposal analytics will surface the signals that reps can act on before that happens. 

There are five key metrics that proposal analytics tackle: Whether the proposal was opened, how much time was spent on each section, whether it was completed, how long the entire process took, and how fast the rep responded to engagement signals. We cover these in more detail below.

Check out our proposal analytics software comparison to see how different tools stack up. 

The 5 proposal metrics every sales team should track

We broke down each of the metrics below to include what it measures, what a healthy rate or pattern looks like, and what you can do if the number isn’t working in your favor. 

1. Proposal view rate

Definition: This is the percentage of proposals that are sent and opened at least once by the recipient. 

What good looks like: Industry context matters when considering a benchmark, as it depends heavily on your audience and how proposals are delivered. But a consistent view rate below 70% typically signals a deliverability or subject line problem, not a proposal problem. You can use this as general guidance rather than a hard target. 

What to do if it's low: Check the sending email address, personalize the notification message, confirm that the recipient’s email is correct, and review the timing of when you send. 

PandaDoc sends a real-time notification as soon as a recipient opens a document. View rate can be calculated directly from the dashboard across all of your active proposals. 

2. Time spent per section

Definition: How long a recipient spent on each page or section of the proposal.

What good looks like: If you have uneven time distribution, that’s normal and useful. Heavy time on pricing is actually a buying signal. Very low total time across a multi-page proposal, say under 60 seconds, is a red flag. 

What to do if it's low: If recipients consistently skip a section, it can indicate that the content is too long, too dense, or placed incorrectly in the document flow. That’s information you can act on.

PandaDoc tracks total time spent and time per page or section natively. You can access this data within the document’s analytics view. 

3. Completion rate

Definition: The percentage of opened proposals that are fully signed or completed by the recipient.

What good looks like: Having a high view rate with a low completion rate is the most common analytics pattern. It means people are reading, but not signing. Understanding this gap is where having a follow-up strategy matters most. It’s worth distinguishing this from broader deal outcomes. PandaDoc’s guide to win rate vs. close rate breaks down that distinction for fully. 

What to do if it's low: Completion rate drop-offs will often signal a friction point in the signing flow, an unclear CTA, or a pricing objection that wasn’ addressed. This is where you’d want to cross-reference with time-per-section data to find out where readers stall. 

PandaDoc tracks document status through its full lifecycle, from Draft to Sent, Viewed, and Completed. It also shows per-recipient signing progress, so you know that status at any point. 

4. Time to completion (proposal cycle time)

Definition: The average number of days from proposal sent to proposal signed across a set of deals.

What good looks like: Having a shorter cycle time often correlates with higher close rates. You can benchmark your own average cycle time to spot outlier deals and intervene earlier.

If you want broader context on what “typical” looks like across your document workflows, PandaDoc’s own document workflow benchmarks draw from data on more than 17,000 customers across 10 different document metrics. 

What to do if it's low: If you have long cycle times, you likely have a follow-up timing problem instead of a problem with the proposal quality. Fixing this means fixing the process, like following up within 24 hours of a view event, instead of rewriting the proposal. 

PandaDoc’s audit trail captures timestamps for every status change, which makes it possible to calculate cycle time per document. You can also export this data for pipeline analysis. 

5. Follow-up timing relative to view events

Definition: How quickly a sales rep follows up after receiving a "document viewed" notification.

What good looks like: Following up within an hour of a view event while the proposal is still fresh in the prospect’s mind consistently outperforms delayed follow-up. Of all the metrics listed here, this is the most actionable one. 

What to do if it's low: You can turn on real-time view notifications in PandaDoc and establish a team norm. This could be a view notification triggers an immediate follow-up, instead of getting to it “later.” 

PandaDoc sends real-time email and mobile notifications when a recipient opens a document. The AI Assistant can also be asked directly something like, “Has [Recipient Name] opened [Document Name]?" for a status check, without ever leaving the platform. 

What good proposal analytics looks like in practice

Say a sales rep sends a proposal in the morning. By the early afternoon, they get a view notification. They open the document’s analytics view  for the time-per-section data and see that the prospect spent 11 minutes on pricing and 30 seconds on the intro. 

This information gives the rep specific, usable data to work with. Instead of sending a generic “just checking in” email, the rep can call and say, “I wanted to follow up specifically on the pricing structure. If it’s useful, I’d be happy to walk you through how the tiers break down.” This targets where the prospect’s attention already was instead of assuming. 

Analytics replaces a guess about what the prospect cares about with an actual signal, making the follow-ups more strategic and targeted to help close the deal.

How PandaDoc surfaces these metrics natively

Every PandaDoc document includes a real-time dashboard that shows document status, view events, and time-per-page/section engagement through the document tracking feature. 

Real-time notifications for both email and mobile fire the moment a recipient opens a document, and report and track features give you information on send rates, close rates, and template usage. 

The AI Assistant answers natural-language queries about document status directly when you ask questions like, "What's the status of my proposal to [Company]?" or "Has [Name] signed yet?" 

The audit trail captures every status change with timestamps, which is useful for calculating cycle time and reviewing deal history. 

Plus, if your team uses Salesforce or HubSpot, Pandadoc pushes document status updates back into your CRM records automatically, so engagement data will surface in the deal record without you ever having to leave the CRM. 

See how PandaDoc tracks proposal engagement in real time. Start a free trial.

How to use proposal analytics to improve follow-up timing

The key is building a pattern of trigger-based follow-ups. This could look like a view notification firing, followed by a rep calling within the hour regarding what the prospect likely saw. For example, the rep might say, "I wanted to check in on the pricing section in particular," if the data points to the prospect spending a lengthy time on that section of the document. 

Analytics should feed a follow-up playbook instead of a simple dashboard review. 

It’s also worth looking at engagement tracking data across templates, not just individual deals. If one template consistently underperforms on the time-per-section for the pricing page, that indicates a problem with the template and not the rep. 

PandaDoc’s data backs this up: from analytics, we know that proposals that include at least one image are 32% more likely to close, and adding reminders increases close rates by roughly 30%. Our blog covers how to increase your proposal close rate in more detail. You can also learn how to close more sales by improving your proposal management process.

Ready to get started with your proposal analytics? Explore PandaDoc's proposal tracking features.