6 Pipeline questions that revops teams can answer in minutes in Hex

Hi! I’m Omar, and I own sales forecasting at Hex on our RevOps team. That means I need to understand — and help our sales and marketing leaders understand — how our pipeline is progressing and where execution gaps might be hiding

In the past, gaining that clarity meant hours of spreadsheet gymnastics. Now, I can assess pipeline performance in real time and spot issues right in the meetings where decisions happen.

Here are the six most common pipeline questions I need to answer and how I use our Stage Conversion Analysis Data App to get those answers instantly.

1. "What's our actual conversion rate by stage?"

The question behind the question: It’s not enough to know our overall win rate; we need to understand where deals slow down or stall so we can improve forecast accuracy and pipeline health.

How I answer it: I pull up our conversion data app and look at stage-by-stage progression — S0→S1, S1→S2, S2→S3, and so on. In the past, this view revealed we were strong at initial qualification (S0→S1) but saw a drop in advancing deals from Stage 1 to Stage 2. I then dug deeper, breaking it down by deal source, loss stage and loss reason to pinpoint the root cause.

What we did with it: We revamped our qualification framework to focus more on high-fit deals and reduced time spent on poor-fit opportunities.

Time saved: A full day of analysis → 10 minutes

2. "Which segment is actually driving growth?"

The question behind the question: Which segments influence our forecast the most? Where is future revenue most likely to come from?

How I answer it: I filter our pipeline by segment to see what deals are progressing fastest and closing at the highest rates. Recently, I spotted that one segment's share of our pipeline was steadily increasing, while another had an unusual spike in July.

What we did with it: We adjusted our resource allocation to strengthen coverage for our new, growing segment and accounted for the other segment spike in our near-term forecast.

Time saved: 2 hours → 30 seconds

3. "Do partnerships actually help us close deals?"

The question behind the question: How does partner involvement influence our forecast accuracy? When should we bring partners into deals?

How I answer it: I use a filter to add "partnership involvement" to compare conversion rates for deals with and without partners. The data showed partnerships significantly improved Segment A's conversions but had a muted impact in Segment B.

What we did with it: We updated our engagement guidelines to prioritize partner involvement in Segment A deals while streamlining Segment B motions.

Time saved: Half a day of Excel pivoting → 5 minutes

4. "Who's really performing on the team?"

The question behind the question: Beyond quota attainment, whose activity is most likely to drive the forecast? Who needs coaching?

How I answer it: Our performance view shows multiple dimensions instantly — who’s outbounding the most, booking the most meetings, maintaining the highest ASP, and achieving the highest win rate.

What we did with it: Using this information, we implemented targeted coaching plans based on specific behavioral gaps rather than relying solely on quota results.

Time saved: Manual CRM reports → real-time performance views

5. "How accurate is our forecast?"

The question behind the question: Can we trust the numbers in our forecast? Are they grounded in reality?

How I answer it: I compare our current pipeline distribution against historical conversion rates. If reps project a number that's higher than our historical performance, I can quickly validate or challenge the forecast.

What we did with it: We incorporated historical conversion benchmarks into forecast reviews, improving accuracy and leadership confidence.

Time saved: Forecast roll-up spreadsheets → live validation

6. "What changed since last quarter?"

The question behind the question: Are we improving? Are new initiatives working? What trends could impact our forecast?

How I answer it: I compare any metric across time periods using a date filter — conversion rates by stage, segment, or source — all visible quarter-over-quarter. This revealed that deal velocity was slowing in Segment A but improving in Segment B.

What we did with it: We refined our sales methodology and adjusted forecast assumptions in response to shifts in deal velocity across segments.

Time saved: Quarterly business reviews → ongoing visibility

The bottom line

These added layers and questions are the kinds of things our leadership team is constantly asking, and in the past, they would have required ad hoc analysis every time. Now, my team and I can filter and dynamically explore the answers at a moment's notice without rebuilding a model or spreadsheet.

The real magic isn’t just the time saved (though getting hours back each week is nice). It’s that we can spend more time analyzing the data versus building the infrastructure for it. Instead of being bogged down in spreadsheet gymnastics, we can focus on spotting opportunities and risks before they impact the business.

Here’s what we’ve learned: faster response times means we can ask better questions. And better questions lead to better decisions.

Your pipeline has stories to tell. The question is: can you hear them in time to act?